Tracing and Profiling
Tracing and Profiling in Yocto
Yocto bundles a number of tracing and profiling tools - this ‘HOWTO‘ describes their basic usage and shows by example how to make use of them to examine application and system behavior.
The tools presented are for the most part completely open-ended and have quite good and/or extensive documentation of their own which can be used to solve just about any problem you might come across in Linux. Each section that describes a particular tool has links to that tool‘s documentation and website.
The purpose of this ‘HOWTO‘ is to present a set of common and generally useful tracing and profiling idioms along with their application (as appropriate) to each tool, in the context of a general-purpose ‘drill-down‘ methodology that can be applied to solving a large number (90%?) of problems. For help with more advanced usages and problems, please see the documentation and/or websites listed for each tool.
The final section of this ‘HOWTO‘ is a collection of real-world examples which we‘ll be continually adding to as we solve more problems using the tools - feel free to add your own examples to the list!
General Setup
Most of the tools are available only in ‘sdk‘ images or in images built after adding ‘tools-profile‘ to your local.conf. So, in order to be able to access all of the tools described here, please first build and boot an ‘sdk‘ image e.g.
$ bitbake core-image-sato-sdk
or alternatively by adding ‘tools-profile‘ to the EXTRA_IMAGE_FEATURES line in your local.conf:
EXTRA_IMAGE_FEATURES = "debug-tweaks tools-profile"
If you use the ‘tools-profile‘ method, you don‘t need to build an sdk image - the tracing and profiling tools will be included in non-sdk images as well e.g.:
$ bitbake core-image-sato
NOTES:
By default, the Yocto build system strips symbols from the binaries it packages, which makes it difficult to use some of the tools.
You can prevent that by putting the following in your local.conf when you build the image:
INHIBIT_PACKAGE_STRIP = "1"
The above setting will noticeably increase the size of your image.
If you‘ve already build a stripped image, you can generate debug packages (xxx-dbg) which you can manually install as needed.
To generate debug info for packages, you can add dbg-pkgs to EXTRA_IMAGE_FEATURES in local.conf. For example:
EXTRA_IMAGE_FEATURES = "debug-tweaks tools-profile dbg-pkgs"
Additionally, in order to generate the right type of debuginfo, we also need to add the following to local.conf:
PACKAGE_DEBUG_SPLIT_STYLE = ‘debug-file-directory‘
Overall Architecture of the Linux Tracing and Profiling Tools
It may seem surprising to see a section covering an ‘overall architecture‘ for what seems to be a random collection of tracing tools that together make up the Linux tracing and profiling space. The fact is, however, that in recent years this seemingly disparate set of tools has started to converge on a ‘core‘ set of underlying mechanisms:
- static tracepoints
- dynamic tracepoints
- kprobes
- uprobes
- the perf_events subsystem
- debugfs
Tying It Together: Rather than enumerating here how each tool makes use of these common mechanisms, textboxes
like this will make note of the specific usages in each tool as they come up in the course
of the text.
Basic Usage (with examples) for each of the Yocto Tracing Tools
perf
The ‘perf‘ tool is the profiling and tracing tool that comes bundled with the Linux kernel.
Don‘t let the fact that it‘s part of the kernel fool you into thinking that it‘s only for tracing and profiling the kernel - you can indeed use it to trace and profile just the kernel , but you can also use it to profile specific applications separately (with or without kernel context), and you can also use it to trace and profile the kernel and all applications on the system simultaneously to gain a system-wide view of what‘s going on.
In many ways, it aims to be a superset of all the tracing and profiling tools available in Linux today, including all the other tools covered in this HOWTO. The past couple of years have seen perf subsume a lot of the functionality of those other tools, and at the same time those other tools have removed large portions of their previous functionality and replaced it with calls to the equivalent functionality now implemented by the perf subsystem. Extrapolation suggests that at some point those other tools will simply become completely redundant and go away; until then, we‘ll cover those other tools in these pages and in many cases show how the same things can be accomplished in perf and the other tools when it seems useful to do so.
The coverage below details some of the most common ways you‘ll likely want to apply the tool; full documentation can be found either within the tool itself or in the man pages:
Setup
For this section, we‘ll assume you‘ve already performed the basic setup outlined in the General Setupsection.
In particular, you‘ll get the most mileage out of perf if you profile an image built with INHIBIT_PACKAGE_STRIP = "1" in your local.conf.
perf runs on the target system for the most part. You can archive profile data and copy it to the host for analysis, but for the rest of this document we assume you‘ve ssh‘ed to the host and will be running the perf commands on the target.
Basic Usage
The perf tool is pretty much self-documenting. To remind yourself of the available commands, simply type ‘perf‘, which will show you basic usage along with the available perf subcommands:
[email protected]:~# perf usage: perf [--version] [--help] COMMAND [ARGS] The most commonly used perf commands are: annotate Read perf.data (created by perf record) and display annotated code archive Create archive with object files with build-ids found in perf.data file bench General framework for benchmark suites buildid-cache Manage build-id cache. buildid-list List the buildids in a perf.data file diff Read two perf.data files and display the differential profile evlist List the event names in a perf.data file inject Filter to augment the events stream with additional information kmem Tool to trace/measure kernel memory(slab) properties kvm Tool to trace/measure kvm guest os list List all symbolic event types lock Analyze lock events probe Define new dynamic tracepoints record Run a command and record its profile into perf.data report Read perf.data (created by perf record) and display the profile sched Tool to trace/measure scheduler properties (latencies) script Read perf.data (created by perf record) and display trace output stat Run a command and gather performance counter statistics test Runs sanity tests. timechart Tool to visualize total system behavior during a workload top System profiling tool. See ‘perf help COMMAND‘ for more information on a specific command.
Using perf to do basic profiling
As a simple test case, we‘ll profile the ‘wget‘ of a fairly large file, which is a minimally interesting case because it has both file and network I/O aspects, and at least in the case of standard Yocto images, it‘s implemented as part of busybox, so the methods we use to analyze it can be used in a very similar way to the whole host of supported busybox applets in Yocto.
[email protected]:~# rm linux-2.6.19.2.tar.bz2; wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2
The quickest and easiest way to get some basic overall data about what‘s going on for a particular workload it to profile it using ‘perf stat‘. ‘perf stat‘ basically profiles using a few default counters and displays the summed counts at the end of the run:
[email protected]:~# perf stat wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2 Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |***************************************************| 41727k 0:00:00 ETA Performance counter stats for ‘wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2‘: 4597.223902 task-clock # 0.077 CPUs utilized 23568 context-switches # 0.005 M/sec 68 CPU-migrations # 0.015 K/sec 241 page-faults # 0.052 K/sec 3045817293 cycles # 0.663 GHz <not supported> stalled-cycles-frontend <not supported> stalled-cycles-backend 858909167 instructions # 0.28 insns per cycle 165441165 branches # 35.987 M/sec 19550329 branch-misses # 11.82% of all branches 59.836627620 seconds time elapsed
Many times such a simple-minded test doesn‘t yield much of interest, but sometimes it does (see Real-world Yocto bug (slow loop-mounted write speed)).
Also, note that ‘perf stat‘ isn‘t restricted to a fixed set of counters - basically any event listed in the output of ‘perf list‘ can be tallied by ‘perf stat‘. For example, suppose we wanted to see a summary of all the events related to kernel memory allocation/freeing along with cache hits and misses:
[email protected]:~# perf stat -e kmem:* -e cache-references -e cache-misses wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2 Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |***************************************************| 41727k 0:00:00 ETA Performance counter stats for ‘wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2‘: 5566 kmem:kmalloc 125517 kmem:kmem_cache_alloc 0 kmem:kmalloc_node 0 kmem:kmem_cache_alloc_node 34401 kmem:kfree 69920 kmem:kmem_cache_free 133 kmem:mm_page_free 41 kmem:mm_page_free_batched 11502 kmem:mm_page_alloc 11375 kmem:mm_page_alloc_zone_locked 0 kmem:mm_page_pcpu_drain 0 kmem:mm_page_alloc_extfrag 66848602 cache-references 2917740 cache-misses # 4.365 % of all cache refs 44.831023415 seconds time elapsed
So ‘perf stat‘ gives us a nice easy way to get a quick overview of what might be happening for a set of events, but normally we‘d need a little more detail in order to understand what‘s going on in a way that we can act on in a useful way.
To dive down into a next level of detail, we can use ‘perf record‘/‘perf report‘ which will collect profiling data and present it to use using an interactive text-based UI (or simply as text if we specify --stdio to ‘perf report‘).
As our first attempt at profiling this workload, we‘ll simply run ‘perf record‘, handing it the workload we want to profile (everything after ‘perf record‘ and any perf options we hand it - here none - will be executedin a new shell). perf collects samples until the process exits and records them in a file named ‘perf.data‘ in the current working directory.
[email protected]:~# perf record wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2 Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |************************************************| 41727k 0:00:00 ETA [ perf record: Woken up 1 times to write data ] [ perf record: Captured and wrote 0.176 MB perf.data (~7700 samples) ]
To see the results in a ‘text-based UI‘ (tui), simply run ‘perf report‘, which will read the perf.data file in the current working directory and display the results in an interactive UI:
[email protected]:~# perf report
The above screenshot displays a ‘flat‘ profile, one entry for each ‘bucket‘ corresponding to the functions that were profiled during the profiling run, ordered from the most popular to the least (perf has options to sort in various orders and keys as well as display entries only above a certain threshold and so on - see the perf documentation for details). Note that this includes both userspace functions (entries containing a [.]) and kernel functions accounted to the process (entries containing a [k]). (perf has command-line modifiers that can be used to restrict the profiling to kernel or userspace, among others).
Notice also that the above report shows an entry for ‘busybox‘, which is the executable that implements ‘wget‘ in Yocto, but that instead of a useful function name in that entry, it displays an not-so-friendly hex value instead. The steps below will show how to fix that problem.
Before we do that, however, let‘s try running a different profile, one which shows something a little more interesting. The only difference between the new profile and the previous one is that we‘ll add the -g option, which will record not just the address of a sampled function, but the entire callchain to the sampled function as well:
[email protected]:~# perf record -g wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2 Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |************************************************| 41727k 0:00:00 ETA [ perf record: Woken up 3 times to write data ] [ perf record: Captured and wrote 0.652 MB perf.data (~28476 samples) ]
[email protected]:~# perf report
Using the callgraph view, we can actually see not only which functions took the most time, but we can also see a summary of how those functions were called and learn something about how the program interacts with the kernel in the process.
Notice that each entry in the above screenshot now contains a ‘+‘ on the left-hand side. This means that we can expand the entry and drill down into the callchains that feed into that entry. Pressing ‘enter‘ on any one of them will expand the callchain (you can also press ‘E‘ to expand them all at the same time or ‘C‘ to collapse them all).
In the screenshot above, we‘ve toggled the __copy_to_user_ll() entry and several subnodes all the way down. This lets us see which callchains contributed to the profiled __copy_to_user_ll() function which contributed 1.77% to the total profile.
As a bit of background explanation for these callchains, think about what happens at a high level when you run wget to get a file out on the network. Basically what happens is that the data comes into the kernel via the network connection (socket) and is passed to the userspace program ‘wget‘ (which is actually a part of busybox, but that‘s not important for now), which takes the buffers the kernel passes to it and writes it to a disk file to save it.
The part of this process that we‘re looking at in the above call stacks is the part where the kernel passes the data it‘s read from the socket down to wget i.e. a copy-to-user.
Notice also that here there‘s also a case where the a hex value is displayed in the callstack, here in the expanded sys_clock_gettime() function. Later we‘ll see it resolve to a userspace function call in busybox.
The above screenshot shows the other half of the journey for the data - from the wget program‘s userspace buffers to disk. To get the buffers to disk, the wget program issues a write(2), which does a copy-from-user to the kernel, which then takes care via some circuitous path (probably also present somewhere in the profile data), to get it safely to disk.
Now that we‘ve seen the basic layout of the profile data and the basics of how to extract useful information out of it, let‘s get back to the task at hand and see if we can get some basic idea about where the time is spent in the program we‘re profiling, wget. Remember that wget is actually implemented as an applet in busybox, so while the process name is ‘wget‘, the executable we‘re actually interested in is busybox. So let‘s expand the first entry containing busybox:
Again, before we expanded we saw that the function was labeled with a hex value instead of a symbol as with most of the kernel entries. Expanding the busybox entry doesn‘t make it any better.
The problem is that perf can‘t find the symbol information for the busybox binary, which is actually stripped out by the Yocto build system.
One way around that is to put the following in your local.conf when you build the image:
INHIBIT_PACKAGE_STRIP = "1"
However, we already have an image with the binaries stripped, so what can we do to get perf to resolve the symbols? Basically we need to install the debuginfo for the busybox package.
To generate the debug info for the packages in the image, we can to add dbg-pkgs to EXTRA_IMAGE_FEATURES in local.conf. For example:
EXTRA_IMAGE_FEATURES = "debug-tweaks tools-profile dbg-pkgs"
Additionally, in order to generate the type of debuginfo that perf understands, we also need to add the following to local.conf:
PACKAGE_DEBUG_SPLIT_STYLE = ‘debug-file-directory‘
Once we‘ve done that, we can install the debuginfo for busybox. The debug packages once built can be found in build/tmp/deploy/rpm/* on the host system. Find the busybox-dbg-...rpm file and copy it to the target. For example:
[[email protected] core2]$ scp /home/trz/yocto/crownbay-tracing-dbg/build/tmp/deploy/rpm/core2/busybox-dbg-1.20.2-r2.core2.rpm [email protected]: [email protected]‘s password: busybox-dbg-1.20.2-r2.core2.rpm 100% 1826KB 1.8MB/s 00:01
Now install the debug rpm on the target:
[email protected]:~# rpm -i busybox-dbg-1.20.2-r2.core2.rpm
Now that the debuginfo is installed, we see that the busybox entries now display their functions symbolically:
If we expand one of the entries and press ‘enter‘ on a leaf node, we‘re presented with a menu of actions we can take to get more information related to that entry:
One of these actions allows us to show a view that displays a busybox-centric view of the profiled functions (in this case we‘ve also expanded all the nodes using the ‘E‘ key):
Finally, we can see that now that the busybox debuginfo is installed, the previously unresolved symbol in the sys_clock_gettime() entry mentioned previously is now resolved, and shows that the sys_clock_gettime system call that was the source of 6.75% of the copy-to-user overhead was initiated by the handle_input() busybox function:
At the lowest level of detail, we can dive down to the assembly level and see which instructions caused the most overhead in a function. Pressing ‘enter‘ on the ‘udhcpc_main‘ function, we‘re again presented with a menu:
Selecting ‘Annotate udhcpc_main‘, we get a detailed listing of percentages by instruction for the udhcpc_main function. From the display, we can see that over 50% of the time spent in this function is taken up by a couple tests and the move of a constant (1) to a register:
As a segue into tracing, let‘s try another profile using a different counter, something other than the default ‘cycles‘.
The tracing and profiling infrastructure in Linux has become unified in a way that allows us to use the same tool with a completely different set of counters, not just the standard hardware counters that traditionally tools have had to restrict themselves to (of course the traditional tools can also make use of the expanded possibilities now available to them, and in some cases have, as mentioned previously).
We can get a list of the available events that can be used to profile a workload via ‘perf list‘:
[email protected]:~# perf list List of pre-defined events (to be used in -e): cpu-cycles OR cycles [Hardware event] stalled-cycles-frontend OR idle-cycles-frontend [Hardware event] stalled-cycles-backend OR idle-cycles-backend [Hardware event] instructions [Hardware event] cache-references [Hardware event] cache-misses [Hardware event] branch-instructions OR branches [Hardware event] branch-misses [Hardware event] bus-cycles [Hardware event] ref-cycles [Hardware event] cpu-clock [Software event] task-clock [Software event] page-faults OR faults [Software event] minor-faults [Software event] major-faults [Software event] context-switches OR cs [Software event] cpu-migrations OR migrations [Software event] alignment-faults [Software event] emulation-faults [Software event] L1-dcache-loads [Hardware cache event] L1-dcache-load-misses [Hardware cache event] L1-dcache-prefetch-misses [Hardware cache event] L1-icache-loads [Hardware cache event] L1-icache-load-misses [Hardware cache event] . . . rNNN [Raw hardware event descriptor] cpu/t1=v1[,t2=v2,t3 ...]/modifier [Raw hardware event descriptor] (see ‘perf list --help‘ on how to encode it) mem:<addr>[:access] [Hardware breakpoint] sunrpc:rpc_call_status [Tracepoint event] sunrpc:rpc_bind_status [Tracepoint event] sunrpc:rpc_connect_status [Tracepoint event] sunrpc:rpc_task_begin [Tracepoint event] skb:kfree_skb [Tracepoint event] skb:consume_skb [Tracepoint event] skb:skb_copy_datagram_iovec [Tracepoint event] net:net_dev_xmit [Tracepoint event] net:net_dev_queue [Tracepoint event] net:netif_receive_skb [Tracepoint event] net:netif_rx [Tracepoint event] napi:napi_poll [Tracepoint event] sock:sock_rcvqueue_full [Tracepoint event] sock:sock_exceed_buf_limit [Tracepoint event] udp:udp_fail_queue_rcv_skb [Tracepoint event] hda:hda_send_cmd [Tracepoint event] hda:hda_get_response [Tracepoint event] hda:hda_bus_reset [Tracepoint event] scsi:scsi_dispatch_cmd_start [Tracepoint event] scsi:scsi_dispatch_cmd_error [Tracepoint event] scsi:scsi_eh_wakeup [Tracepoint event] drm:drm_vblank_event [Tracepoint event] drm:drm_vblank_event_queued [Tracepoint event] drm:drm_vblank_event_delivered [Tracepoint event] random:mix_pool_bytes [Tracepoint event] random:mix_pool_bytes_nolock [Tracepoint event] random:credit_entropy_bits [Tracepoint event] gpio:gpio_direction [Tracepoint event] gpio:gpio_value [Tracepoint event] block:block_rq_abort [Tracepoint event] block:block_rq_requeue [Tracepoint event] block:block_rq_issue [Tracepoint event] block:block_bio_bounce [Tracepoint event] block:block_bio_complete [Tracepoint event] block:block_bio_backmerge [Tracepoint event] . . writeback:writeback_wake_thread [Tracepoint event] writeback:writeback_wake_forker_thread [Tracepoint event] writeback:writeback_bdi_register [Tracepoint event] . . writeback:writeback_single_inode_requeue [Tracepoint event] writeback:writeback_single_inode [Tracepoint event] kmem:kmalloc [Tracepoint event] kmem:kmem_cache_alloc [Tracepoint event] kmem:mm_page_alloc [Tracepoint event] kmem:mm_page_alloc_zone_locked [Tracepoint event] kmem:mm_page_pcpu_drain [Tracepoint event] kmem:mm_page_alloc_extfrag [Tracepoint event] vmscan:mm_vmscan_kswapd_sleep [Tracepoint event] vmscan:mm_vmscan_kswapd_wake [Tracepoint event] vmscan:mm_vmscan_wakeup_kswapd [Tracepoint event] vmscan:mm_vmscan_direct_reclaim_begin [Tracepoint event] . . module:module_get [Tracepoint event] module:module_put [Tracepoint event] module:module_request [Tracepoint event] sched:sched_kthread_stop [Tracepoint event] sched:sched_wakeup [Tracepoint event] sched:sched_wakeup_new [Tracepoint event] sched:sched_process_fork [Tracepoint event] sched:sched_process_exec [Tracepoint event] sched:sched_stat_runtime [Tracepoint event] rcu:rcu_utilization [Tracepoint event] workqueue:workqueue_queue_work [Tracepoint event] workqueue:workqueue_execute_end [Tracepoint event] signal:signal_generate [Tracepoint event] signal:signal_deliver [Tracepoint event] timer:timer_init [Tracepoint event] timer:timer_start [Tracepoint event] timer:hrtimer_cancel [Tracepoint event] timer:itimer_state [Tracepoint event] timer:itimer_expire [Tracepoint event] irq:irq_handler_entry [Tracepoint event] irq:irq_handler_exit [Tracepoint event] irq:softirq_entry [Tracepoint event] irq:softirq_exit [Tracepoint event] irq:softirq_raise [Tracepoint event] printk:console [Tracepoint event] task:task_newtask [Tracepoint event] task:task_rename [Tracepoint event] syscalls:sys_enter_socketcall [Tracepoint event] syscalls:sys_exit_socketcall [Tracepoint event] . . . syscalls:sys_enter_unshare [Tracepoint event] syscalls:sys_exit_unshare [Tracepoint event] raw_syscalls:sys_enter [Tracepoint event] raw_syscalls:sys_exit [Tracepoint event]
Tying It Together: These are exactly the same set of events defined by the trace event subsystem and
exposed by ftrace/tracecmd/kernelshark as files in /sys/kernel/debug/tracing/events,
by SystemTap as kernel.trace("tracepoint_name") and (partially) accessed by LTTng.
Only a subset of these would be of interest to us when looking at this workload, so let‘s choose the most likely subsystems (identified by the string before the colon in the Tracepoint events) and do a ‘perf stat‘ run using only those wildcarded subsystems:
[email protected]:~# perf stat -e skb:* -e net:* -e napi:* -e sched:* -e workqueue:* -e irq:* -e syscalls:* wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2 Performance counter stats for ‘wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2‘: 23323 skb:kfree_skb 0 skb:consume_skb 49897 skb:skb_copy_datagram_iovec 6217 net:net_dev_xmit 6217 net:net_dev_queue 7962 net:netif_receive_skb 2 net:netif_rx 8340 napi:napi_poll 0 sched:sched_kthread_stop 0 sched:sched_kthread_stop_ret 3749 sched:sched_wakeup 0 sched:sched_wakeup_new 0 sched:sched_switch 29 sched:sched_migrate_task 0 sched:sched_process_free 1 sched:sched_process_exit 0 sched:sched_wait_task 0 sched:sched_process_wait 0 sched:sched_process_fork 1 sched:sched_process_exec 0 sched:sched_stat_wait 2106519415641 sched:sched_stat_sleep 0 sched:sched_stat_iowait 147453613 sched:sched_stat_blocked 12903026955 sched:sched_stat_runtime 0 sched:sched_pi_setprio 3574 workqueue:workqueue_queue_work 3574 workqueue:workqueue_activate_work 0 workqueue:workqueue_execute_start 0 workqueue:workqueue_execute_end 16631 irq:irq_handler_entry 16631 irq:irq_handler_exit 28521 irq:softirq_entry 28521 irq:softirq_exit 28728 irq:softirq_raise 1 syscalls:sys_enter_sendmmsg 1 syscalls:sys_exit_sendmmsg 0 syscalls:sys_enter_recvmmsg 0 syscalls:sys_exit_recvmmsg 14 syscalls:sys_enter_socketcall 14 syscalls:sys_exit_socketcall . . . 16965 syscalls:sys_enter_read 16965 syscalls:sys_exit_read 12854 syscalls:sys_enter_write 12854 syscalls:sys_exit_write . . . 58.029710972 seconds time elapsed
Let‘s pick one of these tracepoints and tell perf to do a profile using it as the sampling event:
[email protected]:~# perf record -g -e sched:sched_wakeup wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2
The screenshot above shows the results of running a profile using sched:sched_switch tracepoint, which shows the relative costs of various paths to sched_wakeup (note that sched_wakeup is the name of the tracepoint - it‘s actually defined just inside ttwu_do_wakeup(), which accounts for the function name actually displayed in the profile:
/*
* Mark the task runnable and perform wakeup-preemption.
*/
static void
ttwu_do_wakeup(struct rq *rq, struct task_struct *p, int wake_flags)
{
trace_sched_wakeup(p, true);
.
.
.
}
A couple of the more interesting callchains are expanded and displayed above, basically some network receive paths that presumably end up waking up wget (busybox) when network data is ready.
Note that because tracepoints are normally used for tracing, the default sampling period for tracepoints is 1 i.e. for tracepoints perf will sample on every event occurrence (this can be changed using the -c option). This is in contrast to hardware counters such as for example the default ‘cycles‘ hardware counter used for normal profiling, where sampling periods are much higher (in the thousands) because profiling should have as low an overhead as possible and sampling on every cycle would be prohibitively expensive.
Using perf to do basic tracing
Profiling is a great tool for solving many problems or for getting a high-level view of what‘s going on with a workload or across the system. It is however by definition an approximation, as suggested by the most prominent word associated with it, ‘sampling‘. On the one hand, it allows a representative picture of what‘s going on in the system to be cheaply taken, but on the other hand, that cheapness limits its utility when that data suggests a need to ‘dive down‘ more deeply to discover what‘s really going on. In such cases, the only way to see what‘s really going on is to be able to look at (or summarize more intelligently) the individual steps that go into the higher-level behavior exposed by the coarse-grained profiling data.
As a concrete example, we can trace all the events we think might be applicable to our workload:
[email protected]:~# perf record -g -e skb:* -e net:* -e napi:* -e sched:sched_switch -e sched:sched_wakeup -e irq:* -e syscalls:sys_enter_read -e syscalls:sys_exit_read -e syscalls:sys_enter_write -e syscalls:sys_exit_write wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2
We can look at the raw trace output using ‘perf script‘ with no arguments:
[email protected]:~# perf script
perf 1262 [000] 11624.857082: sys_exit_read: 0x0
perf 1262 [000] 11624.857193: sched_wakeup: comm=migration/0 pid=6 prio=0 success=1 target_cpu=000
wget 1262 [001] 11624.858021: softirq_raise: vec=1 [action=TIMER]
wget 1262 [001] 11624.858074: softirq_entry: vec=1 [action=TIMER]
wget 1262 [001] 11624.858081: softirq_exit: vec=1 [action=TIMER]
wget 1262 [001] 11624.858166: sys_enter_read: fd: 0x0003, buf: 0xbf82c940, count: 0x0200
wget 1262 [001] 11624.858177: sys_exit_read: 0x200
wget 1262 [001] 11624.858878: kfree_skb: skbaddr=0xeb248d80 protocol=0 location=0xc15a5308
wget 1262 [001] 11624.858945: kfree_skb: skbaddr=0xeb248000 protocol=0 location=0xc15a5308
wget 1262 [001] 11624.859020: softirq_raise: vec=1 [action=TIMER]
wget 1262 [001] 11624.859076: softirq_entry: vec=1 [action=TIMER]
wget 1262 [001] 11624.859083: softirq_exit: vec=1 [action=TIMER]
wget 1262 [001] 11624.859167: sys_enter_read: fd: 0x0003, buf: 0xb7720000, count: 0x0400
wget 1262 [001] 11624.859192: sys_exit_read: 0x1d7
wget 1262 [001] 11624.859228: sys_enter_read: fd: 0x0003, buf: 0xb7720000, count: 0x0400
wget 1262 [001] 11624.859233: sys_exit_read: 0x0
wget 1262 [001] 11624.859573: sys_enter_read: fd: 0x0003, buf: 0xbf82c580, count: 0x0200
wget 1262 [001] 11624.859584: sys_exit_read: 0x200
wget 1262 [001] 11624.859864: sys_enter_read: fd: 0x0003, buf: 0xb7720000, count: 0x0400
wget 1262 [001] 11624.859888: sys_exit_read: 0x400
wget 1262 [001] 11624.859935: sys_enter_read: fd: 0x0003, buf: 0xb7720000, count: 0x0400
wget 1262 [001] 11624.859944: sys_exit_read: 0x400
This gives us a detailed timestamped sequence of events that occurred within the workload with respect to those events.
In many ways, profiling can be viewed as a subset of tracing - theoretically, if you have a set of trace events that‘s sufficient to capture all the important aspects of a workload, you can derive any of the results or views that a profiling run can.
Another aspect of traditional profiling is that while powerful in many ways, it‘s limited by the granularity of the underlying data. Profiling tools offer various ways of sorting and presenting the sample data, which make it much more useful and amenable to user experimentation, but in the end it can‘t be used in an open-ended way to extract data that just isn‘t present as a consequence of the fact that conceptually, most of it has been thrown away.
Full-blown detailed tracing data does however offer the opportunity to manipulate and present the information collected during a tracing run in an infinite variety of ways.
Another way to look at it is that there are only so many ways that the ‘primitive‘ counters can be used on their own to generate interesting output; to get anything more complicated than simple counts requires some amount of additional logic, which is typically very specific to the problem at hand. For example, if we wanted to make use of a ‘counter‘ that maps to the value of the time difference between when a process was scheduled to run on a processor and the time it actually ran, we wouldn‘t expect such a counter to exist on its own, but we could derive one called say ‘wakeup_latency‘ and use it to extract a useful view of that metric from trace data. Likewise, we really can‘t figure out from standard profiling tools how much data every process on the system reads and writes, along with how many of those reads and writes fail completely. If we have sufficient trace data, however, we could with the right tools easily extract and present that information, but we‘d need something other than pre-canned profiling tools to do that.
Luckily, there is general-purpose way to handle such needs, called ‘programming languages‘. Making programming languages easily available to apply to such problems given the specific format of data is called a ‘programming language binding‘ for that data and language. Perf supports two programming language bindings, one for Python and one for Perl.
Tying It Together: Language bindings for manipulating and aggregating trace data are of course not a new
idea. One of the first projects to do this was IBM‘s DProbes dpcc compiler, an ANSI C
compiler which targeted a low-level assembly language running on an in-kernel interpreter
on the target system. This is exactly analagous to what Sun‘s DTrace did, except that DTrace
invented its own language for the purpose. Systemtap, heavily inspired by DTrace, also
created its own one-off language, but rather than running the product on an in-kernel
interpreter, created an elaborate compiler-based machinery to translate its language into
kernel modules written in C.
Now that we have the trace data in perf.data, we can use ‘perf script -g‘ to generate a skeleton script with handlers for the read/write entry/exit events we recorded:
[email protected]:~# perf script -g python generated Python script: perf-script.py
The skeleton script simply creates a python function for each event type in the perf.data file. The body of each function simply prints the event name along with its parameters. For example:
def net__netif_rx(event_name, context, common_cpu,
common_secs, common_nsecs, common_pid, common_comm,
skbaddr, len, name):
print_header(event_name, common_cpu, common_secs, common_nsecs,
common_pid, common_comm)
print "skbaddr=%u, len=%u, name=%s\n" % (skbaddr, len, name),
We can run that script directly to print all of the events contained in the perf.data file:
[email protected]:~# perf script -s perf-script.py in trace_begin syscalls__sys_exit_read 0 11624.857082795 1262 perf nr=3, ret=0 sched__sched_wakeup 0 11624.857193498 1262 perf comm=migration/0, pid=6, prio=0, success=1, target_cpu=0 irq__softirq_raise 1 11624.858021635 1262 wget vec=TIMER irq__softirq_entry 1 11624.858074075 1262 wget vec=TIMER irq__softirq_exit 1 11624.858081389 1262 wget vec=TIMER syscalls__sys_enter_read 1 11624.858166434 1262 wget nr=3, fd=3, buf=3213019456, count=512 syscalls__sys_exit_read 1 11624.858177924 1262 wget nr=3, ret=512 skb__kfree_skb 1 11624.858878188 1262 wget skbaddr=3945041280, location=3243922184, protocol=0 skb__kfree_skb 1 11624.858945608 1262 wget skbaddr=3945037824, location=3243922184, protocol=0 irq__softirq_raise 1 11624.859020942 1262 wget vec=TIMER irq__softirq_entry 1 11624.859076935 1262 wget vec=TIMER irq__softirq_exit 1 11624.859083469 1262 wget vec=TIMER syscalls__sys_enter_read 1 11624.859167565 1262 wget nr=3, fd=3, buf=3077701632, count=1024 syscalls__sys_exit_read 1 11624.859192533 1262 wget nr=3, ret=471 syscalls__sys_enter_read 1 11624.859228072 1262 wget nr=3, fd=3, buf=3077701632, count=1024 syscalls__sys_exit_read 1 11624.859233707 1262 wget nr=3, ret=0 syscalls__sys_enter_read 1 11624.859573008 1262 wget nr=3, fd=3, buf=3213018496, count=512 syscalls__sys_exit_read 1 11624.859584818 1262 wget nr=3, ret=512 syscalls__sys_enter_read 1 11624.859864562 1262 wget nr=3, fd=3, buf=3077701632, count=1024 syscalls__sys_exit_read 1 11624.859888770 1262 wget nr=3, ret=1024 syscalls__sys_enter_read 1 11624.859935140 1262 wget nr=3, fd=3, buf=3077701632, count=1024 syscalls__sys_exit_read 1 11624.859944032 1262 wget nr=3, ret=1024
That in itself isn‘t very useful; after all, we can accomplish pretty much the same thing by simply running ‘perf script‘ without arguments in the same directory as the perf.data file.
We can however replace the print statements in the generated function bodies with whatever we want, and thereby make it infinitely more useful.
As a simple example, let‘s just replace the print statements in the function bodies with a simple function that does nothing but increment a per-event count. When the program is run against a perf.data file, each time a particular event is encountered, a tally is incremented for that event. For example:
def net__netif_rx(event_name, context, common_cpu,
common_secs, common_nsecs, common_pid, common_comm,
skbaddr, len, name):
inc_counts(event_name)
Each event handler function in the generated code is modified to do this. For convenience, we define a common function called inc_counts() that each handler calls; inc_counts simply tallies a count for each event using the ‘counts‘ hash, which is a specialized has function that does Perl-like autovivification, a capability that‘s extremely useful for kinds of multi-level aggregation commonly used in processing traces (see perf‘s documentation on the Python language binding for details):
counts = autodict()
def inc_counts(event_name):
try:
counts[event_name] += 1
except TypeError:
counts[event_name] = 1
Finally, at the end of the trace processing run, we want to print the result of all the per-event tallies. For that, we use the special ‘trace_end()‘ function:
def trace_end():
for event_name, count in counts.iteritems():
print "%-40s %10s\n" % (event_name, count)
The end result is a summary of all the events recorded in the trace:
skb__skb_copy_datagram_iovec 13148 irq__softirq_entry 4796 irq__irq_handler_exit 3805 irq__softirq_exit 4795 syscalls__sys_enter_write 8990 net__net_dev_xmit 652 skb__kfree_skb 4047 sched__sched_wakeup 1155 irq__irq_handler_entry 3804 irq__softirq_raise 4799 net__net_dev_queue 652 syscalls__sys_enter_read 17599 net__netif_receive_skb 1743 syscalls__sys_exit_read 17598 net__netif_rx 2 napi__napi_poll 1877 syscalls__sys_exit_write 8990
Note that this is pretty much exactly the same information we get from ‘perf stat‘, which goes a little way to support the idea mentioned previously that given the right kind of trace data, higher-level profiling-type summaries can be derived from it.
Documentation on using the ‘perf script‘ python binding
System-wide tracing and profiling
The examples so far have focused on tracing a particular program or workload - in other words, every profiling run has specified the program to profile in the command-line e.g. ‘perf record wget ...‘.
It‘s also possible, and more interesting in many cases, to run a system-wide profile or trace while running the workload in a separate shell.
To do system-wide profiling or tracing, you typically use the -a flag to ‘perf record‘.
To demonstrate this, open up one window and start the profile using the -a flag (press Ctrl-C to stop tracing):
[email protected]:~# perf record -g -a ^C[ perf record: Woken up 6 times to write data ] [ perf record: Captured and wrote 1.400 MB perf.data (~61172 samples) ]
In another window, run the wget test:
[email protected]:~# wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2 Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |*******************************| 41727k 0:00:00 ETA
Here we see entries not only for our wget load, but for other processes running on the system as well:
In the snapshot above, we can see callchains that originate in libc, and a callchain from Xorg that demonstrates that we‘re using a proprietary X driver in userspace (notice the presence of ‘PVR‘ and some other unresolvable symbols in the expanded Xorg callchain).
Note also that we have both kernel and userspace entries in the above snapshot. We can also tell perf to focus on userspace but providing a modifier, in this case ‘u‘, to the ‘cycles‘ hardware counter when we record a profile:
[email protected]:~# perf record -g -a -e cycles:u ^C[ perf record: Woken up 2 times to write data ] [ perf record: Captured and wrote 0.376 MB perf.data (~16443 samples) ]
Notice in the screenshot above, we see only userspace entries ([.])
Finally, we can press ‘enter‘ on a leaf node and select the ‘Zoom into DSO‘ menu item to show only entries associated with a specific DSO. In the screenshot below, we‘ve zoomed into the ‘libc‘ DSO which shows all the entries associated with the libc-xxx.so DSO.
We can also use the system-wide -a switch to do system-wide tracing. Here we‘ll trace a couple of scheduler events:
[email protected]:~# perf record -a -e sched:sched_switch -e sched:sched_wakeup ^C[ perf record: Woken up 38 times to write data ] [ perf record: Captured and wrote 9.780 MB perf.data (~427299 samples) ]
We can look at the raw output using ‘perf script‘ with no arguments:
[email protected]:~# perf script
perf 1383 [001] 6171.460045: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
perf 1383 [001] 6171.460066: sched_switch: prev_comm=perf prev_pid=1383 prev_prio=120 prev_state=R+ ==> next_comm=kworker/1:1 next_pid=21 next_prio=120
kworker/1:1 21 [001] 6171.460093: sched_switch: prev_comm=kworker/1:1 prev_pid=21 prev_prio=120 prev_state=S ==> next_comm=perf next_pid=1383 next_prio=120
swapper 0 [000] 6171.468063: sched_wakeup: comm=kworker/0:3 pid=1209 prio=120 success=1 target_cpu=000
swapper 0 [000] 6171.468107: sched_switch: prev_comm=swapper/0 prev_pid=0 prev_prio=120 prev_state=R ==> next_comm=kworker/0:3 next_pid=1209 next_prio=120
kworker/0:3 1209 [000] 6171.468143: sched_switch: prev_comm=kworker/0:3 prev_pid=1209 prev_prio=120 prev_state=S ==> next_comm=swapper/0 next_pid=0 next_prio=120
perf 1383 [001] 6171.470039: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
perf 1383 [001] 6171.470058: sched_switch: prev_comm=perf prev_pid=1383 prev_prio=120 prev_state=R+ ==> next_comm=kworker/1:1 next_pid=21 next_prio=120
kworker/1:1 21 [001] 6171.470082: sched_switch: prev_comm=kworker/1:1 prev_pid=21 prev_prio=120 prev_state=S ==> next_comm=perf next_pid=1383 next_prio=120
perf 1383 [001] 6171.480035: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
Filtering
Notice that there are a lot of events that don‘t really have anything to do with what we‘re interested in, namely events that schedule ‘perf‘ itself in and out or that wake perf up. We can get rid of those by using the ‘--filter‘ option - for each event we specify using -e, we can add a --filter after that to filter out trace events that contain fields with specific values:
[email protected]:~# perf record -a -e sched:sched_switch --filter ‘next_comm != perf && prev_comm != perf‘ -e sched:sched_wakeup --filter ‘comm != perf‘ ^C[ perf record: Woken up 38 times to write data ] [ perf record: Captured and wrote 9.688 MB perf.data (~423279 samples) ]
[email protected]:~# perf script
swapper 0 [000] 7932.162180: sched_switch: prev_comm=swapper/0 prev_pid=0 prev_prio=120 prev_state=R ==> next_comm=kworker/0:3 next_pid=1209 next_prio=120
kworker/0:3 1209 [000] 7932.162236: sched_switch: prev_comm=kworker/0:3 prev_pid=1209 prev_prio=120 prev_state=S ==> next_comm=swapper/0 next_pid=0 next_prio=120
perf 1407 [001] 7932.170048: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
perf 1407 [001] 7932.180044: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
perf 1407 [001] 7932.190038: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
perf 1407 [001] 7932.200044: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
perf 1407 [001] 7932.210044: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
perf 1407 [001] 7932.220044: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
swapper 0 [001] 7932.230111: sched_wakeup: comm=kworker/1:1 pid=21 prio=120 success=1 target_cpu=001
swapper 0 [001] 7932.230146: sched_switch: prev_comm=swapper/1 prev_pid=0 prev_prio=120 prev_state=R ==> next_comm=kworker/1:1 next_pid=21 next_prio=120
kworker/1:1 21 [001] 7932.230205: sched_switch: prev_comm=kworker/1:1 prev_pid=21 prev_prio=120 prev_state=S ==> next_comm=swapper/1 next_pid=0 next_prio=120
swapper 0 [000] 7932.326109: sched_wakeup: comm=kworker/0:3 pid=1209 prio=120 success=1 target_cpu=000
swapper 0 [000] 7932.326171: sched_switch: prev_comm=swapper/0 prev_pid=0 prev_prio=120 prev_state=R ==> next_comm=kworker/0:3 next_pid=1209 next_prio=120
kworker/0:3 1209 [000] 7932.326214: sched_switch: prev_comm=kworker/0:3 prev_pid=1209 prev_prio=120 prev_state=S ==> next_comm=swapper/0 next_pid=0 next_prio=120
In this case, we‘ve filtered out all events that have ‘perf‘ in their ‘comm‘ or ‘comm_prev‘ or ‘comm_next‘ fields. Notice that there are still events recorded for perf, but notice that those events don‘t have values of ‘perf‘ for the filtered fields. To completely filter out anything from perf will require a bit more work, but for the purpose of demonstrating how to use filters, it‘s close enough.
Tying It Together: These are exactly the same set of event filters defined by the trace event subsystem. See
the ftrace/tracecmd/kernelshark section for more discussion about these event filters.
Tying It Together: These event filters are implemented by a special-purpose pseudo-interpreter in the kernel
and are an integral and indispensable part of the perf design as it relates to tracing. kernel-
based event filters provide a mechanism to precisely throttle the event stream that appears
in user space, where it makes sense to provide bindings to real programming languages for
postprocessing the event stream. This architecture allows for the intelligent and flexible
partitioning of processing between the kernel and user space. Contrast this with other tools
such as SystemTap, which does all of its processing in the kernel and as such requires a special
project-defined language in order to accommodate that design, or LTTng, where everything is sent
to userspace and as such requires a super-efficient kernel-to-userspace transport mechanism in
order to function properly. While perf certainly can benefit from for instance advances in
the design of the transport, it doesn‘t fundamentally depend on them. Basically, if you find
that your perf tracing application is causing buffer I/O overruns, it probably means that you
aren‘t taking enough advantage of the kernel filtering engine.
Using dynamic tracepoints
perf isn‘t restricted to the fixed set of static tracepoints listed by ‘perf list‘. Users can also add their own ‘dynamic‘ tracepoints anywhere in the kernel. For instance, suppose we want to define our own tracepoint on do_fork(). We can do that using the ‘perf probe‘ perf subcommand:
[email protected]:~# perf probe do_fork Added new event: probe:do_fork (on do_fork) You can now use it in all perf tools, such as: perf record -e probe:do_fork -aR sleep 1
Adding a new tracepoint via ‘perf probe‘ results in an event with all the expected files and format in /sys/kernel/debug/tracing/events, just the same as for static tracepoints (as discussed in more detail in the trace events subsystem section:
[email protected]:/sys/kernel/debug/tracing/events/probe/do_fork# ls -al drwxr-xr-x 2 root root 0 Oct 28 11:42 . drwxr-xr-x 3 root root 0 Oct 28 11:42 .. -rw-r--r-- 1 root root 0 Oct 28 11:42 enable -rw-r--r-- 1 root root 0 Oct 28 11:42 filter -r--r--r-- 1 root root 0 Oct 28 11:42 format -r--r--r-- 1 root root 0 Oct 28 11:42 id
[email protected]:/sys/kernel/debug/tracing/events/probe/do_fork# cat format name: do_fork ID: 944 format: field:unsigned short common_type; offset:0; size:2; signed:0; field:unsigned char common_flags; offset:2; size:1; signed:0; field:unsigned char common_preempt_count; offset:3; size:1; signed:0; field:int common_pid; offset:4; size:4; signed:1; field:int common_padding; offset:8; size:4; signed:1; field:unsigned long __probe_ip; offset:12; size:4; signed:0; print fmt: "(%lx)", REC->__probe_ip
We can list all dynamic tracepoints currently in existence:
[email protected]:~# perf probe -l probe:do_fork (on do_fork) probe:schedule (on schedule)
Let‘s record system-wide (‘sleep 30‘ is a trick for recording system-wide but basically do nothing and then wake up after 30 seconds):
root[email protected]:~# perf record -g -a -e probe:do_fork sleep 30 [ perf record: Woken up 1 times to write data ] [ perf record: Captured and wrote 0.087 MB perf.data (~3812 samples) ]
Using ‘perf script‘ we can see each do_fork event that fired:
[email protected]:~# perf script
# ========
# captured on: Sun Oct 28 11:55:18 2012
# hostname : crownbay
# os release : 3.4.11-yocto-standard
# perf version : 3.4.11
# arch : i686
# nrcpus online : 2
# nrcpus avail : 2
# cpudesc : Intel(R) Atom(TM) CPU E660 @ 1.30GHz
# cpuid : GenuineIntel,6,38,1
# total memory : 1017184 kB
# cmdline : /usr/bin/perf record -g -a -e probe:do_fork sleep 30
# event : name = probe:do_fork, type = 2, config = 0x3b0, config1 = 0x0, config2 = 0x0, excl_usr = 0, excl_kern
= 0, id = { 5, 6 }
# HEADER_CPU_TOPOLOGY info available, use -I to display
# ========
#
matchbox-deskto 1197 [001] 34211.378318: do_fork: (c1028460)
matchbox-deskto 1295 [001] 34211.380388: do_fork: (c1028460)
pcmanfm 1296 [000] 34211.632350: do_fork: (c1028460)
pcmanfm 1296 [000] 34211.639917: do_fork: (c1028460)
matchbox-deskto 1197 [001] 34217.541603: do_fork: (c1028460)
matchbox-deskto 1299 [001] 34217.543584: do_fork: (c1028460)
gthumb 1300 [001] 34217.697451: do_fork: (c1028460)
gthumb 1300 [001] 34219.085734: do_fork: (c1028460)
gthumb 1300 [000] 34219.121351: do_fork: (c1028460)
gthumb 1300 [001] 34219.264551: do_fork: (c1028460)
pcmanfm 1296 [000] 34219.590380: do_fork: (c1028460)
matchbox-deskto 1197 [001] 34224.955965: do_fork: (c1028460)
matchbox-deskto 1306 [001] 34224.957972: do_fork: (c1028460)
matchbox-termin 1307 [000] 34225.038214: do_fork: (c1028460)
matchbox-termin 1307 [001] 34225.044218: do_fork: (c1028460)
matchbox-termin 1307 [000] 34225.046442: do_fork: (c1028460)
matchbox-deskto 1197 [001] 34237.112138: do_fork: (c1028460)
matchbox-deskto 1311 [001] 34237.114106: do_fork: (c1028460)
gaku 1312 [000] 34237.202388: do_fork: (c1028460)
And using ‘perf report‘ on the same file, we can see the callgraphs from starting a few programs during those 30 seconds:
Tying It Together: The trace events subsystem accomodate static and dynamic tracepoints in exactly the
same way - there‘s no difference as far as the infrastructure is concerned. See the ftrace
section for more details on the trace event subsystem.
Tying It Together: Dynamic tracepoints are implemented under the covers by kprobes and uprobes. kprobes and
uprobes are also used by and in fact are the main focus of SystemTap.
Documentation
Online versions of the man pages for the commands discussed in this section can be found here:
- The ‘perf stat‘ manpage.
- The ‘perf record‘ manpage.
- The ‘perf report‘ manpage.
- The ‘perf probe‘ manpage.
- The ‘perf script‘ manpage.
- Documentation on using the ‘perf script‘ python binding.
- The top-level perf(1) manpage.
Normally, you should be able to invoke the man pages via perf itself e.g. ‘perf help‘ or ‘perf help record‘.
However, by default Yocto doesn‘t install man pages, but perf invokes the man pages for most help functionality. This is a bug and is being addressed by a Yocto bug: Bug 3388 - perf: enable man pages for basic ‘help‘ functionality
The man pages in text form, along with some other files, such as a a set of examples, can be found in the ‘perf‘ directory of the kernel tree:
tools/perf/Documentation
There‘s also a nice perf tutorial on the perf wiki that goes into more detail than we do here in certain areas: Perf Tutorial
ftrace
‘ftrace‘ literally refers to the ‘ftrace function tracer‘ but in reality this encompasses a number of related tracers along with the infrastructure that they all make use of.
Setup
For this section, we‘ll assume you‘ve already performed the basic setup outlined in the General Setupsection.
ftrace, trace-cmd, and kernelshark run on the target system, and are ready to go out-of-the-box - no additional setup is necessary. For the rest of this section we assume you‘ve ssh‘ed to the host and will be running ftrace on the target. kernelshark is a GUI application and if you use the ‘-X‘ option to ssh you can have the kernelshark GUI run on the target but display remotely on the host if you want.
Basic ftrace usage
‘ftrace‘ essentially refers to everything included in the /tracing directory of the mounted debugfs filesystem (Yocto follows the standard convention and mounts it at /sys/kernel/debug). Here‘s a listing of all the files found in /sys/kernel/debug/tracing on a Yocto system.:
[email protected]:/sys/kernel/debug/tracing# ls README kprobe_events trace available_events kprobe_profile trace_clock available_filter_functions options trace_marker available_tracers per_cpu trace_options buffer_size_kb printk_formats trace_pipe buffer_total_size_kb saved_cmdlines tracing_cpumask current_tracer set_event tracing_enabled dyn_ftrace_total_info set_ftrace_filter tracing_on enabled_functions set_ftrace_notrace tracing_thresh events set_ftrace_pid free_buffer set_graph_function
The files listed above are used for various purposes - some relate directly to the tracers themselves, others are used to set tracing options, and yet others actually contain the tracing output when a tracer is in effect. Some of the functions can be guessed from their names, others need explanation; in any case, we‘ll cover some of the files we see here below but for an explanation of the others, please see the ftrace documentation.
We‘ll start by looking at some of the available built-in tracers.
cat‘ing the ‘available_tracers‘ file lists the set of available tracers:
[email protected]:/sys/kernel/debug/tracing# cat available_tracers blk function_graph function nop
The ‘current_tracer‘ file contains the tracer currently in effect:
[email protected]:/sys/kernel/debug/tracing# cat current_tracer nop
The above listing of current_tracer shows that the ‘nop‘ tracer is in effect, which is just another way of saying that there‘s actually no tracer currently in effect.
echo‘ing one of the available_tracers into current_tracer makes the specified tracer the current tracer:
[email protected]:/sys/kernel/debug/tracing# echo function > current_tracer [email protected]:/sys/kernel/debug/tracing# cat current_tracer function
The above sets the current tracer to be the ‘function tracer‘. This tracer traces every function call in the kernel and makes it available as the contents of the ‘trace‘ file. Reading the ‘trace‘ file lists the currently buffered function calls that have been traced by the function tracer:
[email protected]:/sys/kernel/debug/tracing# cat trace | less
# tracer: function
#
# entries-in-buffer/entries-written: 310629/766471 #P:8
#
# _-----=> irqs-off
# / _----=> need-resched
# | / _---=> hardirq/softirq
# || / _--=> preempt-depth
# ||| / delay
# TASK-PID CPU# |||| TIMESTAMP FUNCTION
# | | | |||| | |
<idle>-0 [004] d..1 470.867169: ktime_get_real <-intel_idle
<idle>-0 [004] d..1 470.867170: getnstimeofday <-ktime_get_real
<idle>-0 [004] d..1 470.867171: ns_to_timeval <-intel_idle
<idle>-0 [004] d..1 470.867171: ns_to_timespec <-ns_to_timeval
<idle>-0 [004] d..1 470.867172: smp_apic_timer_interrupt <-apic_timer_interrupt
<idle>-0 [004] d..1 470.867172: native_apic_mem_write <-smp_apic_timer_interrupt
<idle>-0 [004] d..1 470.867172: irq_enter <-smp_apic_timer_interrupt
<idle>-0 [004] d..1 470.867172: rcu_irq_enter <-irq_enter
<idle>-0 [004] d..1 470.867173: rcu_idle_exit_common.isra.33 <-rcu_irq_enter
<idle>-0 [004] d..1 470.867173: local_bh_disable <-irq_enter
<idle>-0 [004] d..1 470.867173: add_preempt_count <-local_bh_disable
<idle>-0 [004] d.s1 470.867174: tick_check_idle <-irq_enter
<idle>-0 [004] d.s1 470.867174: tick_check_oneshot_broadcast <-tick_check_idle
<idle>-0 [004] d.s1 470.867174: ktime_get <-tick_check_idle
<idle>-0 [004] d.s1 470.867174: tick_nohz_stop_idle <-tick_check_idle
<idle>-0 [004] d.s1 470.867175: update_ts_time_stats <-tick_nohz_stop_idle
<idle>-0 [004] d.s1 470.867175: nr_iowait_cpu <-update_ts_time_stats
<idle>-0 [004] d.s1 470.867175: tick_do_update_jiffies64 <-tick_check_idle
<idle>-0 [004] d.s1 470.867175: _raw_spin_lock <-tick_do_update_jiffies64
<idle>-0 [004] d.s1 470.867176: add_preempt_count <-_raw_spin_lock
<idle>-0 [004] d.s2 470.867176: do_timer <-tick_do_update_jiffies64
<idle>-0 [004] d.s2 470.867176: _raw_spin_lock <-do_timer
<idle>-0 [004] d.s2 470.867176: add_preempt_count <-_raw_spin_lock
<idle>-0 [004] d.s3 470.867177: ntp_tick_length <-do_timer
<idle>-0 [004] d.s3 470.867177: _raw_spin_lock_irqsave <-ntp_tick_length
.
.
.
Each line in the trace above shows what was happening in the kernel on a given cpu, to the level of detail of function calls. Each entry shows the function called, followed by its caller (after the arrow).
The function tracer gives you an extremely detailed idea of what the kernel was doing at the point in time the trace was taken, and is a great way to learn about how the kernel code works in a dynamic sense.
Tying It Together: The ftrace function tracer is also available from within perf, as the ftrace:function tracepoint.
It is a little more difficult to follow the call chains than it needs to be - luckily there‘s a variant of the function tracer that displays the callchains explicitly, called the ‘function_graph‘ tracer:
[email protected]:/sys/kernel/debug/tracing# echo function_graph > current_tracer
[email protected]:/sys/kernel/debug/tracing# cat trace | less
tracer: function_graph
CPU DURATION FUNCTION CALLS
| | | | | | |
7) 0.046 us | pick_next_task_fair();
7) 0.043 us | pick_next_task_stop();
7) 0.042 us | pick_next_task_rt();
7) 0.032 us | pick_next_task_fair();
7) 0.030 us | pick_next_task_idle();
7) | _raw_spin_unlock_irq() {
7) 0.033 us | sub_preempt_count();
7) 0.258 us | }
7) 0.032 us | sub_preempt_count();
7) + 13.341 us | } /* __schedule */
7) 0.095 us | } /* sub_preempt_count */
7) | schedule() {
7) | __schedule() {
7) 0.060 us | add_preempt_count();
7) 0.044 us | rcu_note_context_switch();
7) | _raw_spin_lock_irq() {
7) 0.033 us | add_preempt_count();
7) 0.247 us | }
7) | idle_balance() {
7) | _raw_spin_unlock() {
7) 0.031 us | sub_preempt_count();
7) 0.246 us | }
7) | update_shares() {
7) 0.030 us | __rcu_read_lock();
7) 0.029 us | __rcu_read_unlock();
7) 0.484 us | }
7) 0.030 us | __rcu_read_lock();
7) | load_balance() {
7) | find_busiest_group() {
7) 0.031 us | idle_cpu();
7) 0.029 us | idle_cpu();
7) 0.035 us | idle_cpu();
7) 0.906 us | }
7) 1.141 us | }
7) 0.022 us | msecs_to_jiffies();
7) | load_balance() {
7) | find_busiest_group() {
7) 0.031 us | idle_cpu();
.
.
.
4) 0.062 us | msecs_to_jiffies();
4) 0.062 us | __rcu_read_unlock();
4) | _raw_spin_lock() {
4) 0.073 us | add_preempt_count();
4) 0.562 us | }
4) + 17.452 us | }
4) 0.108 us | put_prev_task_fair();
4) 0.102 us | pick_next_task_fair();
4) 0.084 us | pick_next_task_stop();
4) 0.075 us | pick_next_task_rt();
4) 0.062 us | pick_next_task_fair();
4) 0.066 us | pick_next_task_idle();
------------------------------------------
4) kworker-74 => <idle>-0
------------------------------------------
4) | finish_task_switch() {
4) | _raw_spin_unlock_irq() {
4) 0.100 us | sub_preempt_count();
4) 0.582 us | }
4) 1.105 us | }
4) 0.088 us | sub_preempt_count();
4) ! 100.066 us | }
.
.
.
3) | sys_ioctl() {
3) 0.083 us | fget_light();
3) | security_file_ioctl() {
3) 0.066 us | cap_file_ioctl();
3) 0.562 us | }
3) | do_vfs_ioctl() {
3) | drm_ioctl() {
3) 0.075 us | drm_ut_debug_printk();
3) | i915_gem_pwrite_ioctl() {
3) | i915_mutex_lock_interruptible() {
3) 0.070 us | mutex_lock_interruptible();
3) 0.570 us | }
3) | drm_gem_object_lookup() {
3) | _raw_spin_lock() {
3) 0.080 us | add_preempt_count();
3) 0.620 us | }
3) | _raw_spin_unlock() {
3) 0.085 us | sub_preempt_count();
3) 0.562 us | }
3) 2.149 us | }
3) 0.133 us | i915_gem_object_pin();
3) | i915_gem_object_set_to_gtt_domain() {
3) 0.065 us | i915_gem_object_flush_gpu_write_domain();
3) 0.065 us | i915_gem_object_wait_rendering();
3) 0.062 us | i915_gem_object_flush_cpu_write_domain();
3) 1.612 us | }
3) | i915_gem_object_put_fence() {
3) 0.097 us | i915_gem_object_flush_fence.constprop.36();
3) 0.645 us | }
3) 0.070 us | add_preempt_count();
3) 0.070 us | sub_preempt_count();
3) 0.073 us | i915_gem_object_unpin();
3) 0.068 us | mutex_unlock();
3) 9.924 us | }
3) + 11.236 us | }
3) + 11.770 us | }
3) + 13.784 us | }
3) | sys_ioctl() {
As you can see, the function_graph display is much easier to follow. Also note that in addition to the function calls and associated braces, other events such as scheduler events are displayed in context. In fact, you can freely include any tracepoint available in the trace events subsystem described in the next section by simply enabling those events, and they‘ll appear in context in the function graph display. Quite a powerful tool for understanding kernel dynamics.
Also notice that there are various annotations on the left hand side of the display. For example if the total time it took for a given function to execute is above a certain threshold, and exclamation point or plus sign appears on the left hand side. Please see the ftrace documentation for details on all these fields.
The ‘trace events‘ subsystem
One especially important directory contained within the /sys/kernel/debug/tracing directory is the ‘events‘ subdirectory, which contains representations of every tracepoint in the system. Listing out the contents of the ‘events‘ subdirectory, we see mainly another set of subdirectories:
[email protected]:/sys/kernel/debug/tracing# cd events [email protected]:/sys/kernel/debug/tracing/events# ls -al drwxr-xr-x 38 root root 0 Nov 14 23:19 . drwxr-xr-x 5 root root 0 Nov 14 23:19 .. drwxr-xr-x 19 root root 0 Nov 14 23:19 block drwxr-xr-x 32 root root 0 Nov 14 23:19 btrfs drwxr-xr-x 5 root root 0 Nov 14 23:19 drm -rw-r--r-- 1 root root 0 Nov 14 23:19 enable drwxr-xr-x 40 root root 0 Nov 14 23:19 ext3 drwxr-xr-x 79 root root 0 Nov 14 23:19 ext4 drwxr-xr-x 14 root root 0 Nov 14 23:19 ftrace drwxr-xr-x 8 root root 0 Nov 14 23:19 hda -r--r--r-- 1 root root 0 Nov 14 23:19 header_event -r--r--r-- 1 root root 0 Nov 14 23:19 header_page drwxr-xr-x 25 root root 0 Nov 14 23:19 i915 drwxr-xr-x 7 root root 0 Nov 14 23:19 irq drwxr-xr-x 12 root root 0 Nov 14 23:19 jbd drwxr-xr-x 14 root root 0 Nov 14 23:19 jbd2 drwxr-xr-x 14 root root 0 Nov 14 23:19 kmem drwxr-xr-x 7 root root 0 Nov 14 23:19 module drwxr-xr-x 3 root root 0 Nov 14 23:19 napi drwxr-xr-x 6 root root 0 Nov 14 23:19 net drwxr-xr-x 3 root root 0 Nov 14 23:19 oom drwxr-xr-x 12 root root 0 Nov 14 23:19 power drwxr-xr-x 3 root root 0 Nov 14 23:19 printk drwxr-xr-x 8 root root 0 Nov 14 23:19 random drwxr-xr-x 4 root root 0 Nov 14 23:19 raw_syscalls drwxr-xr-x 3 root root 0 Nov 14 23:19 rcu drwxr-xr-x 6 root root 0 Nov 14 23:19 rpm drwxr-xr-x 20 root root 0 Nov 14 23:19 sched drwxr-xr-x 7 root root 0 Nov 14 23:19 scsi drwxr-xr-x 4 root root 0 Nov 14 23:19 signal drwxr-xr-x 5 root root 0 Nov 14 23:19 skb drwxr-xr-x 4 root root 0 Nov 14 23:19 sock drwxr-xr-x 10 root root 0 Nov 14 23:19 sunrpc drwxr-xr-x 538 root root 0 Nov 14 23:19 syscalls drwxr-xr-x 4 root root 0 Nov 14 23:19 task drwxr-xr-x 14 root root 0 Nov 14 23:19 timer drwxr-xr-x 3 root root 0 Nov 14 23:19 udp drwxr-xr-x 21 root root 0 Nov 14 23:19 vmscan drwxr-xr-x 3 root root 0 Nov 14 23:19 vsyscall drwxr-xr-x 6 root root 0 Nov 14 23:19 workqueue drwxr-xr-x 26 root root 0 Nov 14 23:19 writeback
Each one of these subdirectories corresponds to a ‘subsystem‘ and contains yet again more subdirectories, each one of those finally corresponding to a tracepoint. For example, here are the contents of the ‘kmem‘ subsystem:
[email protected]:/sys/kernel/debug/tracing/events# cd kmem [email protected]:/sys/kernel/debug/tracing/events/kmem# ls -al drwxr-xr-x 14 root root 0 Nov 14 23:19 . drwxr-xr-x 38 root root 0 Nov 14 23:19 .. -rw-r--r-- 1 root root 0 Nov 14 23:19 enable -rw-r--r-- 1 root root 0 Nov 14 23:19 filter drwxr-xr-x 2 root root 0 Nov 14 23:19 kfree drwxr-xr-x 2 root root 0 Nov 14 23:19 kmalloc drwxr-xr-x 2 root root 0 Nov 14 23:19 kmalloc_node drwxr-xr-x 2 root root 0 Nov 14 23:19 kmem_cache_alloc drwxr-xr-x 2 root root 0 Nov 14 23:19 kmem_cache_alloc_node drwxr-xr-x 2 root root 0 Nov 14 23:19 kmem_cache_free drwxr-xr-x 2 root root 0 Nov 14 23:19 mm_page_alloc drwxr-xr-x 2 root root 0 Nov 14 23:19 mm_page_alloc_extfrag drwxr-xr-x 2 root root 0 Nov 14 23:19 mm_page_alloc_zone_locked drwxr-xr-x 2 root root 0 Nov 14 23:19 mm_page_free drwxr-xr-x 2 root root 0 Nov 14 23:19 mm_page_free_batched drwxr-xr-x 2 root root 0 Nov 14 23:19 mm_page_pcpu_drain
Let‘s see what‘s inside the subdirectory for a specific tracepoint, in this case the one for kmalloc:
[email protected]:/sys/kernel/debug/tracing/events/kmem# cd kmalloc [email protected]:/sys/kernel/debug/tracing/events/kmem/kmalloc# ls -al drwxr-xr-x 2 root root 0 Nov 14 23:19 . drwxr-xr-x 14 root root 0 Nov 14 23:19 .. -rw-r--r-- 1 root root 0 Nov 14 23:19 enable -rw-r--r-- 1 root root 0 Nov 14 23:19 filter -r--r--r-- 1 root root 0 Nov 14 23:19 format -r--r--r-- 1 root root 0 Nov 14 23:19 id
The ‘format‘ file for the tracepoint describes the event in memory, which is used by the various tracing tools that now make use of these tracepoint to parse the event and make sense of it, along with a ‘print fmt‘ field that allows tools like ftrace to display the event as text. Here‘s what the format of the kmalloc event looks like:
[email protected]:/sys/kernel/debug/tracing/events/kmem/kmalloc# cat format
name: kmalloc
ID: 313
format:
field:unsigned short common_type; offset:0; size:2; signed:0;
field:unsigned char common_flags; offset:2; size:1; signed:0;
field:unsigned char common_preempt_count; offset:3; size:1; signed:0;
field:int common_pid; offset:4; size:4; signed:1;
field:int common_padding; offset:8; size:4; signed:1;
field:unsigned long call_site; offset:16; size:8; signed:0;
field:const void * ptr; offset:24; size:8; signed:0;
field:size_t bytes_req; offset:32; size:8; signed:0;
field:size_t bytes_alloc; offset:40; size:8; signed:0;
field:gfp_t gfp_flags; offset:48; size:4; signed:0;
print fmt: "call_site=%lx ptr=%p bytes_req=%zu bytes_alloc=%zu gfp_flags=%s", REC->call_site, REC->ptr, REC->bytes_req, REC->bytes_alloc,
(REC->gfp_flags) ? __print_flags(REC->gfp_flags, "|", {(unsigned long)(((( gfp_t)0x10u) | (( gfp_t)0x40u) | (( gfp_t)0x80u) | ((
gfp_t)0x20000u) | (( gfp_t)0x02u) | (( gfp_t)0x08u)) | (( gfp_t)0x4000u) | (( gfp_t)0x10000u) | (( gfp_t)0x1000u) | (( gfp_t)0x200u) | ((
gfp_t)0x400000u)), "GFP_TRANSHUGE"}, {(unsigned long)((( gfp_t)0x10u) | (( gfp_t)0x40u) | (( gfp_t)0x80u) | (( gfp_t)0x20000u) | ((
gfp_t)0x02u) | (( gfp_t)0x08u)), "GFP_HIGHUSER_MOVABLE"}, {(unsigned long)((( gfp_t)0x10u) | (( gfp_t)0x40u) | (( gfp_t)0x80u) | ((
gfp_t)0x20000u) | (( gfp_t)0x02u)), "GFP_HIGHUSER"}, {(unsigned long)((( gfp_t)0x10u) | (( gfp_t)0x40u) | (( gfp_t)0x80u) | ((
gfp_t)0x20000u)), "GFP_USER"}, {(unsigned long)((( gfp_t)0x10u) | (( gfp_t)0x40u) | (( gfp_t)0x80u) | (( gfp_t)0x80000u)), GFP_TEMPORARY"},
{(unsigned long)((( gfp_t)0x10u) | (( gfp_t)0x40u) | (( gfp_t)0x80u)), "GFP_KERNEL"}, {(unsigned long)((( gfp_t)0x10u) | (( gfp_t)0x40u)),
"GFP_NOFS"}, {(unsigned long)((( gfp_t)0x20u)), "GFP_ATOMIC"}, {(unsigned long)((( gfp_t)0x10u)), "GFP_NOIO"}, {(unsigned long)((
gfp_t)0x20u), "GFP_HIGH"}, {(unsigned long)(( gfp_t)0x10u), "GFP_WAIT"}, {(unsigned long)(( gfp_t)0x40u), "GFP_IO"}, {(unsigned long)((
gfp_t)0x100u), "GFP_COLD"}, {(unsigned long)(( gfp_t)0x200u), "GFP_NOWARN"}, {(unsigned long)(( gfp_t)0x400u), "GFP_REPEAT"}, {(unsigned
long)(( gfp_t)0x800u), "GFP_NOFAIL"}, {(unsigned long)(( gfp_t)0x1000u), "GFP_NORETRY"}, {(unsigned long)(( gfp_t)0x4000u), "GFP_COMP"},
{(unsigned long)(( gfp_t)0x8000u), "GFP_ZERO"}, {(unsigned long)(( gfp_t)0x10000u), "GFP_NOMEMALLOC"}, {(unsigned long)(( gfp_t)0x20000u),
"GFP_HARDWALL"}, {(unsigned long)(( gfp_t)0x40000u), "GFP_THISNODE"}, {(unsigned long)(( gfp_t)0x80000u), "GFP_RECLAIMABLE"}, {(unsigned
long)(( gfp_t)0x08u), "GFP_MOVABLE"}, {(unsigned long)(( gfp_t)0), "GFP_NOTRACK"}, {(unsigned long)(( gfp_t)0x400000u), "GFP_NO_KSWAPD"},
{(unsigned long)(( gfp_t)0x800000u), "GFP_OTHER_NODE"} ) : "GFP_NOWAIT"
The ‘enable‘ file in the tracepoint directory is what allows the user (or tools such as trace-cmd) to actually turn the tracepoint on and off. When enabled, the corresponding tracepoint will start appearing in the ftrace ‘trace‘ file described previously. For example, this turns on the kmalloc tracepoint:
[email protected]:/sys/kernel/debug/tracing/events/kmem/kmalloc# echo 1 > enable
At the moment, we‘re not interested in the function tracer or some other tracer that might be in effect, so we first turn it off, but if we do that, we still need to turn tracing on in order to see the events in the output buffer:
[email protected]:/sys/kernel/debug/tracing# echo nop > current_tracer [email protected]:/sys/kernel/debug/tracing# echo 1 > tracing_on
Now, if we look at the the ‘trace‘ file, we see nothing but the kmalloc events we just turned on:
[email protected]:/sys/kernel/debug/tracing# cat trace | less
# tracer: nop
#
# entries-in-buffer/entries-written: 1897/1897 #P:8
#
# _-----=> irqs-off
# / _----=> need-resched
# | / _---=> hardirq/softirq
# || / _--=> preempt-depth
# ||| / delay
# TASK-PID CPU# |||| TIMESTAMP FUNCTION
# | | | |||| | |
dropbear-1465 [000] ...1 18154.620753: kmalloc: call_site=ffffffff816650d4 ptr=ffff8800729c3000 bytes_req=2048 bytes_alloc=2048 gfp_flags=GFP_KERNEL
<idle>-0 [000] ..s3 18154.621640: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d555800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
<idle>-0 [000] ..s3 18154.621656: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d555800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
matchbox-termin-1361 [001] ...1 18154.755472: kmalloc: call_site=ffffffff81614050 ptr=ffff88006d5f0e00 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_KERNEL|GFP_REPEAT
Xorg-1264 [002] ...1 18154.755581: kmalloc: call_site=ffffffff8141abe8 ptr=ffff8800734f4cc0 bytes_req=168 bytes_alloc=192 gfp_flags=GFP_KERNEL|GFP_NOWARN|GFP_NORETRY
Xorg-1264 [002] ...1 18154.755583: kmalloc: call_site=ffffffff814192a3 ptr=ffff88001f822520 bytes_req=24 bytes_alloc=32 gfp_flags=GFP_KERNEL|GFP_ZERO
Xorg-1264 [002] ...1 18154.755589: kmalloc: call_site=ffffffff81419edb ptr=ffff8800721a2f00 bytes_req=64 bytes_alloc=64 gfp_flags=GFP_KERNEL|GFP_ZERO
matchbox-termin-1361 [001] ...1 18155.354594: kmalloc: call_site=ffffffff81614050 ptr=ffff88006db35400 bytes_req=576 bytes_alloc=1024 gfp_flags=GFP_KERNEL|GFP_REPEAT
Xorg-1264 [002] ...1 18155.354703: kmalloc: call_site=ffffffff8141abe8 ptr=ffff8800734f4cc0 bytes_req=168 bytes_alloc=192 gfp_flags=GFP_KERNEL|GFP_NOWARN|GFP_NORETRY
Xorg-1264 [002] ...1 18155.354705: kmalloc: call_site=ffffffff814192a3 ptr=ffff88001f822520 bytes_req=24 bytes_alloc=32 gfp_flags=GFP_KERNEL|GFP_ZERO
Xorg-1264 [002] ...1 18155.354711: kmalloc: call_site=ffffffff81419edb ptr=ffff8800721a2f00 bytes_req=64 bytes_alloc=64 gfp_flags=GFP_KERNEL|GFP_ZERO
<idle>-0 [000] ..s3 18155.673319: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d555800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
dropbear-1465 [000] ...1 18155.673525: kmalloc: call_site=ffffffff816650d4 ptr=ffff8800729c3000 bytes_req=2048 bytes_alloc=2048 gfp_flags=GFP_KERNEL
<idle>-0 [000] ..s3 18155.674821: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d554800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
<idle>-0 [000] ..s3 18155.793014: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d554800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
dropbear-1465 [000] ...1 18155.793219: kmalloc: call_site=ffffffff816650d4 ptr=ffff8800729c3000 bytes_req=2048 bytes_alloc=2048 gfp_flags=GFP_KERNEL
<idle>-0 [000] ..s3 18155.794147: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d555800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
<idle>-0 [000] ..s3 18155.936705: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d555800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
dropbear-1465 [000] ...1 18155.936910: kmalloc: call_site=ffffffff816650d4 ptr=ffff8800729c3000 bytes_req=2048 bytes_alloc=2048 gfp_flags=GFP_KERNEL
<idle>-0 [000] ..s3 18155.937869: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d554800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
matchbox-termin-1361 [001] ...1 18155.953667: kmalloc: call_site=ffffffff81614050 ptr=ffff88006d5f2000 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_KERNEL|GFP_REPEAT
Xorg-1264 [002] ...1 18155.953775: kmalloc: call_site=ffffffff8141abe8 ptr=ffff8800734f4cc0 bytes_req=168 bytes_alloc=192 gfp_flags=GFP_KERNEL|GFP_NOWARN|GFP_NORETRY
Xorg-1264 [002] ...1 18155.953777: kmalloc: call_site=ffffffff814192a3 ptr=ffff88001f822520 bytes_req=24 bytes_alloc=32 gfp_flags=GFP_KERNEL|GFP_ZERO
Xorg-1264 [002] ...1 18155.953783: kmalloc: call_site=ffffffff81419edb ptr=ffff8800721a2f00 bytes_req=64 bytes_alloc=64 gfp_flags=GFP_KERNEL|GFP_ZERO
<idle>-0 [000] ..s3 18156.176053: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d554800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
dropbear-1465 [000] ...1 18156.176257: kmalloc: call_site=ffffffff816650d4 ptr=ffff8800729c3000 bytes_req=2048 bytes_alloc=2048 gfp_flags=GFP_KERNEL
<idle>-0 [000] ..s3 18156.177717: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d555800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
<idle>-0 [000] ..s3 18156.399229: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d555800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
dropbear-1465 [000] ...1 18156.399434: kmalloc: call_site=ffffffff816650d4 ptr=ffff8800729c3000 bytes_req=2048 bytes_alloc=2048 gfp_flags=GFP_KERNEL
<idle>-0 [000] ..s3 18156.400660: kmalloc: call_site=ffffffff81619b36 ptr=ffff88006d554800 bytes_req=512 bytes_alloc=512 gfp_flags=GFP_ATOMIC
matchbox-termin-1361 [001] ...1 18156.552800: kmalloc: call_site=ffffffff81614050 ptr=ffff88006db34800 bytes_req=576 bytes_alloc=1024 gfp_flags=GFP_KERNEL|GFP_REPEAT
To again disable the kmalloc event, we need to send 0 to the enable file:
[email protected]:/sys/kernel/debug/tracing/events/kmem/kmalloc# echo 0 > enable
You can enable any number of events or complete subsystems (by using the ‘enable‘ file in the subsystem directory) and get am arbitrarily fine-grained idea of what‘s going on in the system by enabling as many of the appropriate tracepoints as applicable.
A number of the tools described in this HOWTO do just that, including trace-cmd and kernelshark in the next section.
Tying It Together: These tracepoints and their representation are used not only by ftrace, but by many
of the other tools covered in this document and they form a central point of integration
for the various tracers available in Linux. They form a central part of the instrumentation
for the following tools: perf, lttng, ftrace, blktrace and SystemTap
Tying It Together: Eventually all the special-purpose tracers currently available in /sys/kernel/debug/tracing
will be removed and replaced with equivalent tracers based on the ‘trace events‘ subsystem.
trace-cmd/kernelshark
trace-cmd is essentially an extensive command-line ‘wrapper‘ interface that hides the details of all the individual files in /sys/kernel/debug/tracing, allowing users to specify specific particular events within the /sys/kernel/debug/tracing/events/ subdirectory and to collect traces and avoiding having to deal with those details directly.
As yet another layer on top of that, kernelshark provides a GUI that allows users to start and stop traces and specify sets of events using an intuitive interface, and view the output as both trace events and as a per-cpu graphical display. It directly uses ‘trace-cmd‘ as the plumbing that accomplishes all that underneath the covers (and actually displays the trace-cmd command it uses, as we‘ll see).
To start a trace using kernelshark, first start kernelshark:
[email protected]:~# kernelshark
The bring up the ‘Capture‘ dialog by choosing from the kernelshark menu:
Capture | Record
That will display the following dialog, which allows you to choose on or more events (or even one or more complete subsystems) to trace:
Note that these are exactly the same set of events described in the previous trace events subsystem section, and in fact is where trace-cmd gets them for kernelshark.
In the above screenshot, we‘ve decided to explore the graphics subsystem a bit and so have chosen to trace all the tracepoints contained within the ‘i915‘ and ‘drm‘ subsystems.
After doing that, we can start and stop the trace using the ‘Run‘ and ‘Stop‘ button on the lower right corner of the dialog (the same button will turn into the ‘Stop‘ button after the trace has started):
Notice that the right-hand pane shows the exact trace-cmd command-line that‘s used to run the trace, along with the results of the trace-cmd run.
Once the ‘Stop‘ button is pressed, the graphical view magically fills up with a colorful per-cpu display of the trace data, along with the detailed event listing below that:
Here‘s another example, this time a display resulting from tracing ‘all events‘:
The tool is pretty self-explanatory, but for more detailed information on navigating through the data, see the kernelshark website.
Documentation
The documentation for ftrace can be found in the kernel Documentation directory:
Documentation/trace/ftrace.txt
The documentation for the trace event subsystem can also be found in the kernel Documentation directory:
Documentation/trace/events.txt
There are a nice series of articles on using ftrace and trace-cmd at LWN:
- Debugging the kernel using Ftrace - part 1
- Debugging the kernel using Ftrace - part 2
- trace-cmd: A front-end for Ftrace
There‘s more detailed documentation kernelshark usage here: KernelShark
An amusing yet useful README (a tracing mini-HOWTO) can be found in /sys/kernel/debug/tracing/README.
systemtap
SystemTap is a system-wide script-based tracing and profiling tool.
SystemTap scripts are C-like programs that are executed in the kernel to gather/print/aggregate data extracted from the context they end up being invoked under.
For example, this probe from the SystemTap tutorial [1] simply prints a line every time any process on the system open()s a file. For each line, it prints the executable name of the program that opened the file, along with its pid, and the name of the file it opened (or tried to open), which it extracts from the open syscall‘s argstr.
probe syscall.open
{
printf ("%s(%d) open (%s)\n", execname(), pid(), argstr)
}
probe timer.ms(4000) # after 4 seconds
{
exit ()
}
Normally, to execute this probe, you‘d simply install systemtap on the system you want to probe, and directly run the probe on that system e.g. assuming the name of the file containing the above text is trace_open.stp:
# stap trace_open.stp
What systemtap does under the covers to run this probe is 1) parse and convert the probe to an equivalent ‘C‘ form, 2) compile the ‘C‘ form into a kernel module, 3) insert the module into the kernel, which arms it, and 4) collect the data generated by the probe and display it to the user.
In order to accomplish steps 1 and 2, the ‘stap‘ program needs access to the kernel build system that produced the kernel that the probed system is running. In the case of a typical embedded system (the ‘target‘), the kernel build system unfortunately isn‘t typically part of the image running on the target. It is normally available on the ‘host‘ system that produced the target image however; in such cases, steps 1 and 2 are executed on the host system, and steps 3 and 4 are executed on the target system, using only the systemtap ‘runtime‘.
The systemtap support in Yocto assumes that only steps 3 and 4 are run on the target; it is possible to do everything on the target, but this section assumes only the typical embedded use-case.
So basically what you need to do in order to run a systemtap script on the target is to 1) on the host system, compile the probe into a kernel module that makes sense to the target, 2) copy the module onto the target system and 3) insert the module into the target kernel, which arms it, and 4) collect the data generated by the probe and display it to the user.
Setup
Those are a lot of steps and a lot of details, but fortunately Yocto includes a script called ‘crosstap‘ that will take care of those details, allowing you to simply execute a systemtap script on the remote target, with arguments if necessary.
In order to do this from a remote host, however, you need to have access to the build for the image you booted. The ‘crosstap‘ script provides details on how to do this if you run the script on the host without having done a build:
$ crosstap [email protected] trace_open.stp
Error: No target kernel build found.
Did you forget to create a local build of your image?
‘crosstap‘ requires a local sdk build of the target system
(or a build that includes ‘tools-profile‘) in order to build
kernel modules that can probe the target system.
Practically speaking, that means you need to do the following:
- If you‘re running a pre-built image, download the release
and/or BSP tarballs used to build the image.
- If you‘re working from git sources, just clone the metadata
and BSP layers needed to build the image you‘ll be booting.
- Make sure you‘re properly set up to build a new image (see
the BSP README and/or the widely available basic documentation
that discusses how to build images).
- Build an -sdk version of the image e.g.:
$ bitbake core-image-sato-sdk
OR
- Build a non-sdk image but include the profiling tools:
[ edit local.conf and add ‘tools-profile‘ to the end of
the EXTRA_IMAGE_FEATURES variable ]
$ bitbake core-image-sato
[ NOTE that ‘crosstap‘ needs to be able to ssh into the target
system, which isn‘t enabled by default in -minimal images. ]
Once you‘ve build the image on the host system, you‘re ready to
boot it (or the equivalent pre-built image) and use ‘crosstap‘
to probe it (you need to source the environment as usual first):
$ source oe-init-build-env
$ cd ~/my/systemtap/scripts
$ crosstap [email protected] myscript.stp
So essentially what you need to do is build an SDK image or image with ‘tools-profile‘ as detailed in the General Setup section of this wiki, and boot the resulting target image.
NOTE: if you have a build directory containing multiple machines, you need to have the MACHINE you‘re connecting to selected in local.conf, and the kernel in that machine‘s build directory must match the kernel on the booted system exactly, or you‘ll get the above ‘crosstap‘ message when you try to invoke a script.
Running a script on the target
Once you‘ve done that, you should be able to run a systemtap script on the target:
$ cd /path/to/yocto $ source oe-init-build-env
### Shell environment set up for builds. ### You can now run ‘bitbake <target>‘ Common targets are: core-image-minimal core-image-sato meta-toolchain meta-toolchain-sdk adt-installer meta-ide-support You can also run generated qemu images with a command like ‘runqemu qemux86‘
Once you‘ve done that, you can cd to whatever directory contains your scripts and use ‘crosstap‘ to run the script:
$ cd /path/to/my/systemap/script $ crosstap [email protected] trace_open.stp
If you get an error connecting to the target e.g.:
$ crosstap [email protected] trace_open.stp error establishing ssh connection on remote ‘[email protected]‘
Try ssh‘ing to the target and see what happens:
$ ssh [email protected]
A lot of the time, connection problems are due specifying a wrong IP address or having a ‘host key verification error‘.
If everything worked as planned, you should see something like this (enter the password when prompted, or press enter if its set up to use no password):
$ crosstap [email protected] trace_open.stp
[email protected]‘s password:
matchbox-termin(1036) open ("/tmp/vte3FS2LW", O_RDWR|O_CREAT|O_EXCL|O_LARGEFILE, 0600)
matchbox-termin(1036) open ("/tmp/vteJMC7LW", O_RDWR|O_CREAT|O_EXCL|O_LARGEFILE, 0600)
Documentation
The SystemTap language reference can be found here: SystemTap Language Reference
Links to other SystemTap documents, tutorials, and examples can be found here: SystemTap documentation page
oprofile
Setup
For this section, we‘ll assume you‘ve already performed the basic setup outlined in the General Setupsection.
oprofile itself is a command-line application that runs on the target system. For the the section that deals with oprofile from the command-line, we assume you‘ve ssh‘ed to the host and will be running oprofile on the target.
oprofileui (oprofile-viewer) is a GUI-based program that runs on the host and interacts remotely with the target. See the oprofileui section for the exact steps needed to install oprofileui on the host.
Basic Usage
Oprofile as configured in Yocto is a system-wide profiler (i.e. the version in Yocto doesn‘t yet make use of the perf_events interface which would allow it to profile specific processes and workloads). It‘s relies on hardware counter support in the hardware (but can fall back to a timer-based mode), which means that it doesn‘t take advantage of tracepoints or other event sources for example.
It consists of a kernel module that collects samples and a userspace daemon that writes the sample data to disk.
The ‘opcontrol‘ shell script is used for transparently managing these components and starting and stopping profiles, and the ‘opreport‘ command is used to display the results.
The oprofile daemon should already be running, but before you start profiling, you may need to change some settings and some of these settings may require the daemon not be running. One of these settings is the path the the vmlinux file, which you‘ll want to set using the --vmlinux option if you want the kernel profiled:
[email protected]:~# opcontrol --vmlinux=/boot/vmlinux-`uname -r` The profiling daemon is currently active, so changes to the configuration will be used the next time you restart oprofile after a --shutdown or --deinit.
You can check if vmlinux file: is set using opcontrol --status:
[email protected]:~# opcontrol --status Daemon paused: pid 1334 Separate options: library vmlinux file: none Image filter: none Call-graph depth: 6
If it‘s not, you need to shutdown the daemon, add the setting and restart the daemon:
[email protected]:~# opcontrol --shutdown Killing daemon. [email protected]:~# opcontrol --vmlinux=/boot/vmlinux-`uname -r` [email protected]:~# opcontrol --start-daemon Using default event: CPU_CLK_UNHALTED:100000:0:1:1 Using 2.6+ OProfile kernel interface. Reading module info. Using log file /var/lib/oprofile/samples/oprofiled.log Daemon started.
If we get the status again we now see our updated settings:
[email protected]:~# opcontrol --status Daemon paused: pid 1649 Separate options: library vmlinux file: /boot/vmlinux-3.4.11-yocto-standard Image filter: none Call-graph depth: 6
We‘re now in a position to run a profile. For that we used ‘opcontrol --start‘:
[email protected]:~# opcontrol --start Profiler running.
In another window, run our wget workload:
[email protected]:~# rm linux-2.6.19.2.tar.bz2; wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2; sync Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |*******************************| 41727k 0:00:00 ETA
To stop the profile we use ‘opcontrol --shudown‘, which not only stops the profile but shuts down the daemon as well:
[email protected]:~# opcontrol --start Stopping profiling. Killing daemon.
Oprofile writes sample data to /var/lib/oprofile/samples, which you can look at if you‘re interested in seeing how the samples are structured. This is also interesting because it‘s related to how you dive down to get further details about specific executables in OProfile.
To see the default display output for a profile, simply type ‘opreport‘, which will show the results using the data in /var/lib/oprofile/samples:
[email protected]:~# opreport
WARNING! The OProfile kernel driver reports sample buffer overflows.
Such overflows can result in incorrect sample attribution, invalid sample
files and other symptoms. See the oprofiled.log for details.
You should adjust your sampling frequency to eliminate (or at least minimize)
these overflows.
CPU: Intel Architectural Perfmon, speed 1.3e+06 MHz (estimated)
Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 100000
CPU_CLK_UNHALT...|
samples| %|
------------------
464365 79.8156 vmlinux-3.4.11-yocto-standard
65108 11.1908 oprofiled
CPU_CLK_UNHALT...|
samples| %|
------------------
64416 98.9372 oprofiled
692 1.0628 libc-2.16.so
36959 6.3526 no-vmlinux
4378 0.7525 busybox
CPU_CLK_UNHALT...|
samples| %|
------------------
2844 64.9612 libc-2.16.so
1337 30.5391 busybox
193 4.4084 ld-2.16.so
2 0.0457 libnss_compat-2.16.so
1 0.0228 libnsl-2.16.so
1 0.0228 libnss_files-2.16.so
4344 0.7467 bash
CPU_CLK_UNHALT...|
samples| %|
------------------
2657 61.1648 bash
1665 38.3287 libc-2.16.so
18 0.4144 ld-2.16.so
3 0.0691 libtinfo.so.5.9
1 0.0230 libdl-2.16.so
3118 0.5359 nf_conntrack
686 0.1179 matchbox-terminal
CPU_CLK_UNHALT...|
samples| %|
------------------
214 31.1953 libglib-2.0.so.0.3200.4
114 16.6181 libc-2.16.so
79 11.5160 libcairo.so.2.11200.2
78 11.3703 libgdk-x11-2.0.so.0.2400.8
51 7.4344 libpthread-2.16.so
45 6.5598 libgobject-2.0.so.0.3200.4
29 4.2274 libvte.so.9.2800.2
25 3.6443 libX11.so.6.3.0
19 2.7697 libxcb.so.1.1.0
17 2.4781 libgtk-x11-2.0.so.0.2400.8
12 1.7493 librt-2.16.so
3 0.4373 libXrender.so.1.3.0
671 0.1153 emgd
411 0.0706 nf_conntrack_ipv4
391 0.0672 iptable_nat
378 0.0650 nf_nat
263 0.0452 Xorg
CPU_CLK_UNHALT...|
samples| %|
------------------
106 40.3042 Xorg
53 20.1521 libc-2.16.so
31 11.7871 libpixman-1.so.0.27.2
26 9.8859 emgd_drv.so
16 6.0837 libemgdsrv_um.so.1.5.15.3226
11 4.1825 libEMGD2d.so.1.5.15.3226
9 3.4221 libfb.so
7 2.6616 libpthread-2.16.so
1 0.3802 libudev.so.0.9.3
1 0.3802 libdrm.so.2.4.0
1 0.3802 libextmod.so
1 0.3802 mouse_drv.so
.
.
.
9 0.0015 connmand
CPU_CLK_UNHALT...|
samples| %|
------------------
4 44.4444 libglib-2.0.so.0.3200.4
2 22.2222 libpthread-2.16.so
1 11.1111 connmand
1 11.1111 libc-2.16.so
1 11.1111 librt-2.16.so
6 0.0010 oprofile-server
CPU_CLK_UNHALT...|
samples| %|
------------------
3 50.0000 libc-2.16.so
1 16.6667 oprofile-server
1 16.6667 libpthread-2.16.so
1 16.6667 libglib-2.0.so.0.3200.4
5 8.6e-04 gconfd-2
CPU_CLK_UNHALT...|
samples| %|
------------------
2 40.0000 libdbus-1.so.3.7.2
2 40.0000 libglib-2.0.so.0.3200.4
1 20.0000 libc-2.16.so
The output above shows the breakdown or samples by both number of samples and percentage for each executable. Within an executable, the sample counts are broken down further into executable and shared libraries (DSOs) used by the executable.
To get even more detailed breakdowns by function, we need to have the full paths to the DSOs, which we can get by using -f with opreport:
[email protected]:~# opreport -f CPU: Intel Architectural Perfmon, speed 1.3e+06 MHz (estimated) Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 100000 CPU_CLK_UNHALT...| samples| %|
464365 79.8156 /boot/vmlinux-3.4.11-yocto-standard
65108 11.1908 /usr/bin/oprofiled
CPU_CLK_UNHALT...|
samples| %|
------------------
64416 98.9372 /usr/bin/oprofiled
692 1.0628 /lib/libc-2.16.so
36959 6.3526 /no-vmlinux
4378 0.7525 /bin/busybox
CPU_CLK_UNHALT...|
samples| %|
------------------
2844 64.9612 /lib/libc-2.16.so
1337 30.5391 /bin/busybox
193 4.4084 /lib/ld-2.16.so
2 0.0457 /lib/libnss_compat-2.16.so
1 0.0228 /lib/libnsl-2.16.so
1 0.0228 /lib/libnss_files-2.16.so
4344 0.7467 /bin/bash
CPU_CLK_UNHALT...|
samples| %|
------------------
2657 61.1648 /bin/bash
1665 38.3287 /lib/libc-2.16.so
18 0.4144 /lib/ld-2.16.so
3 0.0691 /lib/libtinfo.so.5.9
1 0.0230 /lib/libdl-2.16.so
.
.
.
Using the paths shown in the above output and the -l option to opreport, we can see all the functions that have hits in the profile and their sample counts and percentages. Here‘s a portion of what we get for the kernel:
[email protected]:~# opreport -l /boot/vmlinux-3.4.11-yocto-standard CPU: Intel Architectural Perfmon, speed 1.3e+06 MHz (estimated) Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 100000 samples % symbol name 233981 50.3873 intel_idle 15437 3.3243 rb_get_reader_page 14503 3.1232 ring_buffer_consume 14092 3.0347 mutex_spin_on_owner 13024 2.8047 read_hpet 8039 1.7312 sub_preempt_count 7096 1.5281 ioread32 6997 1.5068 add_preempt_count 3985 0.8582 rb_advance_reader 3488 0.7511 add_event_entry 3303 0.7113 get_parent_ip 3104 0.6684 rb_buffer_peek 2960 0.6374 op_cpu_buffer_read_entry 2614 0.5629 sync_buffer 2545 0.5481 debug_smp_processor_id 2456 0.5289 ohci_irq 2397 0.5162 memset 2349 0.5059 __copy_to_user_ll 2185 0.4705 ring_buffer_event_length 1918 0.4130 in_lock_functions 1850 0.3984 __schedule 1767 0.3805 __copy_from_user_ll_nozero 1575 0.3392 rb_event_data_length 1256 0.2705 memcpy 1233 0.2655 system_call 1213 0.2612 menu_select
Notice that above we see an entry for the __copy_to_user_ll() function that we‘ve looked at with other profilers as well.
Here‘s what we get when we do the same thing for the busybox executable:
CPU: Intel Architectural Perfmon, speed 1.3e+06 MHz (estimated) Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 100000 samples % image name symbol name 349 8.4198 busybox retrieve_file_data 308 7.4306 libc-2.16.so _IO_file_xsgetn 283 6.8275 libc-2.16.so __read_nocancel 235 5.6695 libc-2.16.so syscall 233 5.6212 libc-2.16.so clearerr 215 5.1870 libc-2.16.so fread 181 4.3667 libc-2.16.so __write_nocancel 158 3.8118 libc-2.16.so __underflow 151 3.6429 libc-2.16.so _dl_addr 150 3.6188 busybox progress_meter 150 3.6188 libc-2.16.so __poll_nocancel 148 3.5706 libc-2.16.so [email protected]@GLIBC_2.1 137 3.3052 busybox safe_poll 125 3.0157 busybox bb_progress_update 122 2.9433 libc-2.16.so __x86.get_pc_thunk.bx 95 2.2919 busybox full_write 81 1.9542 busybox safe_write 77 1.8577 busybox xwrite 72 1.7370 libc-2.16.so _IO_file_read 71 1.7129 libc-2.16.so _IO_sgetn 67 1.6164 libc-2.16.so poll 52 1.2545 libc-2.16.so _IO_switch_to_get_mode 45 1.0856 libc-2.16.so read 34 0.8203 libc-2.16.so write 32 0.7720 busybox monotonic_sec 25 0.6031 libc-2.16.so vfprintf 22 0.5308 busybox get_mono 14 0.3378 ld-2.16.so strcmp 14 0.3378 libc-2.16.so __x86.get_pc_thunk.cx . . .
Since we recorded the profile with a callchain depth of 6, we should be able to see our __copy_to_user_ll() callchains in the output, and indeed we can if we search around a bit in the ‘opreport --callgraph‘ output:
[email protected]:~# opreport --callgraph /boot/vmlinux-3.4.11-yocto-standard 392 6.9639 vmlinux-3.4.11-yocto-standard sock_aio_read 736 13.0751 vmlinux-3.4.11-yocto-standard __generic_file_aio_write 3255 57.8255 vmlinux-3.4.11-yocto-standard inet_recvmsg 785 0.1690 vmlinux-3.4.11-yocto-standard tcp_recvmsg 1790 31.7940 vmlinux-3.4.11-yocto-standard local_bh_enable 1238 21.9893 vmlinux-3.4.11-yocto-standard __kfree_skb 992 17.6199 vmlinux-3.4.11-yocto-standard lock_sock_nested 785 13.9432 vmlinux-3.4.11-yocto-standard tcp_recvmsg [self] 525 9.3250 vmlinux-3.4.11-yocto-standard release_sock 112 1.9893 vmlinux-3.4.11-yocto-standard tcp_cleanup_rbuf 72 1.2789 vmlinux-3.4.11-yocto-standard skb_copy_datagram_iovec 170 0.0366 vmlinux-3.4.11-yocto-standard skb_copy_datagram_iovec 1491 73.3038 vmlinux-3.4.11-yocto-standard memcpy_toiovec 327 16.0767 vmlinux-3.4.11-yocto-standard skb_copy_datagram_iovec 170 8.3579 vmlinux-3.4.11-yocto-standard skb_copy_datagram_iovec [self] 20 0.9833 vmlinux-3.4.11-yocto-standard copy_to_user 2588 98.2909 vmlinux-3.4.11-yocto-standard copy_to_user 2349 0.5059 vmlinux-3.4.11-yocto-standard __copy_to_user_ll 2349 89.2138 vmlinux-3.4.11-yocto-standard __copy_to_user_ll [self] 166 6.3046 vmlinux-3.4.11-yocto-standard do_page_fault
Remember that by default OProfile sessions are cumulative i.e. if you start and stop a profiling session, then start a new one, the new one will not erase the previous run(s) but will build on it. If you want to restart a profile from scratch, you need to reset:
[email protected]:~# opcontrol --reset
operf
On systems with a kernel that supports it, you can more simply use the ‘operf‘ command to record profiles. ‘operf‘ also allows you to easily profile a single application by running it as the last part of the ‘operf‘ command. You can tell if your kernel supports ‘operf‘ by simply running it with no arguments:
[email protected]:~# operf usage: operf [ options ] [ --system-wide | --pid <pid> | [ command [ args ] ] ] See operf man page for details.
If it doesn‘t support operf, you‘ll get a message that says the kernel doesn‘t support it. You can run a system-wide profile by using operf with the -s (system-wide) option:
[email protected]:~# operf -k /boot/vmlinux-`uname -r` -s -g operf: Profiler started operf: Press Ctl-c or ‘kill -SIGINT 1222‘ to stop profiling ^C* * * * WARNING: Profiling rate was throttled back by the kernel * * * * The number of samples actually recorded is less than expected, but is probably still statistically valid. Decreasing the sampling rate is the best option if you want to avoid throttling. See the /home/root/oprofile_data/samples/operf.log file for statistics about lost samples. Profiling done.
You can use ‘opreport‘ to get the results just as you would with ‘legacy oprofile‘:
[email protected]:~# opreport
Using /home/root/oprofile_data/samples/ for samples directory.
CPU: Intel Architectural Perfmon, speed 1.3e+06 MHz (estimated)
Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 90000
CPU_CLK_UNHALT...|
samples| %|
------------------
3431 99.2766 operf
CPU_CLK_UNHALT...|
samples| %|
------------------
1822 53.1041 vmlinux-3.4.26-yocto-standard
809 23.5791 operf
680 19.8193 libc-2.16.so
117 3.4101 libstdc++.so.6.0.17
3 0.0874 emgd
13 0.3762 dropbearmulti
CPU_CLK_UNHALT...|
samples| %|
------------------
9 69.2308 vmlinux-3.4.26-yocto-standard
3 23.0769 dropbearmulti
1 7.6923 libc-2.16.so
11 0.3183 matchbox-terminal
CPU_CLK_UNHALT...|
samples| %|
------------------
6 54.5455 vmlinux-3.4.26-yocto-standard
3 27.2727 libglib-2.0.so.0.3400.3
1 9.0909 libc-2.16.so
1 9.0909 librt-2.16.so
1 0.0289 init.sysvinit
CPU_CLK_UNHALT...|
samples| %|
------------------
1 100.000 vmlinux-3.4.26-yocto-standard
You can also, as always, display the results with callgraphs:
[email protected]:~# opreport --callgraph Using /home/root/oprofile_data/samples/ for samples directory. warning: /emgd could not be found. CPU: Intel Architectural Perfmon, speed 1.3e+06 MHz (estimated) Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 90000 samples % image name app name symbol name ------------------------------------------------------------------------------- 809 23.4086 operf operf /usr/bin/operf 809 100.000 operf operf /usr/bin/operf [self] ------------------------------------------------------------------------------- 680 19.6759 libc-2.16.so operf /lib/libc-2.16.so 680 100.000 libc-2.16.so operf /lib/libc-2.16.so [self] ------------------------------------------------------------------------------- 117 3.3854 libstdc++.so.6.0.17 operf /usr/lib/libstdc++.so.6.0.17 117 100.000 libstdc++.so.6.0.17 operf /usr/lib/libstdc++.so.6.0.17 [self] ------------------------------------------------------------------------------- 1 0.4717 vmlinux-3.4.26-yocto-standard operf kmem_cache_alloc 1 0.4717 vmlinux-3.4.26-yocto-standard operf ext3_getblk 4 1.8868 vmlinux-3.4.26-yocto-standard operf do_mpage_readpage 5 2.3585 vmlinux-3.4.26-yocto-standard operf kmem_cache_alloc_trace 95 44.8113 vmlinux-3.4.26-yocto-standard operf get_page_from_freelist 106 50.0000 vmlinux-3.4.26-yocto-standard operf memset 106 3.0671 vmlinux-3.4.26-yocto-standard operf memset 106 50.0000 vmlinux-3.4.26-yocto-standard operf memset 106 50.0000 vmlinux-3.4.26-yocto-standard operf memset [self]
Here‘s an example of recording a single application:
[email protected]:~# operf -k /boot/vmlinux-`uname -r` -g /usr/bin/wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2 operf: Profiler started Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |****************************************| 41727k 0:00:00 ETA See the /home/root/oprofile_data/samples/operf.log file for statistics about lost samples. Profiling done.
And again display the results with opreport:
[email protected]:~# opreport
Using /home/root/oprofile_data/samples/ for samples directory.
CPU: Intel Architectural Perfmon, speed 1.3e+06 MHz (estimated)
Counted CPU_CLK_UNHALTED events (Clock cycles when not halted) with a unit mask of 0x00 (No unit mask) count 90000
CPU_CLK_UNHALT...|
samples| %|
------------------
48835 100.000 wget
CPU_CLK_UNHALT...|
samples| %|
------------------
45778 93.7401 vmlinux-3.4.26-yocto-standard
1921 3.9337 libc-2.16.so
1097 2.2463 busybox
33 0.0676 emgd
4 0.0082 ld-2.16.so
1 0.0020 libnss_files-2.16.so
1 0.0020 libresolv-2.16.so
OProfileUI - A GUI for OProfile
Yocto also supports a graphical UI for controlling and viewing OProfile traces, called OProfileUI. To use it, you first need to clone the oprofileui git repo, then configure, build, and install it:
[[email protected] tmp]$ git clone git://git.yoctoproject.org/oprofileui [[email protected] tmp]$ cd oprofileui [[email protected] oprofileui]$ ./autogen.sh [[email protected] oprofileui]$ sudo make install
OprofileUI replaces the ‘opreport‘ functionality with a GUI, and normally doesn‘t require the user to use ‘opcontrol‘ either. If you want to profile the kernel, however, you need to either use the UI to specify a vmlinux or use ‘opcontrol‘ to specify it on the target:
First, on the target, check if vmlinux file: is set:
[email protected]:~# opcontrol --status
If not:
[email protected]:~# opcontrol --shutdown [email protected]:~# opcontrol --vmlinux=/boot/vmlinux-`uname -r` [email protected]:~# opcontrol --start-daemon
Now, start the oprofile UI on the host system:
[[email protected] oprofileui]$ oprofile-viewer
To run a profile on the remote system, first connect to the remote system by pressing the ‘Connect‘ button and supplying the IP address and port of the remote system (the default port is 4224).
The oprofile server should automatically be started already. If not, the connection will fail and you either typed in the wrong IP address and port (see below), or you need to start the server yourself:
[email protected]:~# oprofile-server
Or, to specify a specific port:
[email protected]:~# oprofile-server --port 8888
Once connected, press the ‘Start‘ button and then run the wget workload on the remote system:
[email protected]:~# rm linux-2.6.19.2.tar.bz2; wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2; sync Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |*******************************| 41727k 0:00:00 ETA
Once the workload completes, press the ‘Stop‘ button. At that point the OProfile viewer will download the profile files it‘s collected (this may take some time, especially if the kernel was profiled). While it downloads the files, you should see something like the following:
Once the profile files have been retrieved, you should see a list of the processes that were profiled:
If you select one of them, you should see all the symbols that were hit during the profile. Selecting one of them will show a list of callers and callees of the chosen function in two panes below the top pane. For example, here‘s what we see when we select __copy_to_user_ll():
As another example, we can look at the busybox process and see that the progress meter made a system call:
Tying It Together: oprofile does have build options to enable use of the perf_event subsystem and
benefit from the perf_event infrastructure by adding support for something other than
system-wide profiling i.e. per-process or workload profiling, but the version in danny
doesn‘t yet take advantage of those capabilities.
Documentation
Yocto already has some information on setting up and using OProfile and oprofileui. As this document doesn‘t cover everything in detail, it may be worth taking a look: Yocto Project Development Manual - Profiling with OProfile
The OProfile manual can be found here: OProfile manual
The OProfile website contains links to the above manual and bunch of other items including an extensive set of examples: About OProfile
Sysprof
Sysprof is a very easy to use system-wide profiler that consists of a single window with three panes and a few buttons which allow you to start, stop, and view the profile from one place.
Setup
For this section, we‘ll assume you‘ve already performed the basic setup outlined in the General Setupsection.
Sysprof is a GUI-based application that runs on the target system. For the rest of this document we assume you‘ve ssh‘ed to the host and will be running Sysprof on the target (you can use the ‘-X‘ option to ssh and have the Sysprof GUI run on the target but display remotely on the host if you want).
Basic Usage
To start profiling the system, you simply press the ‘Start‘ button. To stop profiling and to start viewing the profile data in one easy step, press the ‘Profile‘ button.
Once you‘ve pressed the profile button, the three panes will fill up with profiling data:
The left pane shows a list of functions and processes. Selecting one of those expands that function in the right pane, showing all its callees. Note that this caller-oriented display is essentially the inverse of perf‘s default callee-oriented callchain display.
In the screenshot above, we‘re focusing on __copy_to_user_ll() and looking up the callchain we can see that one of the callers of __copy_to_user_ll is sys_read() and the complete callpath between them. Notice that this is essentially a portion of the same information we saw in the perf display shown in the perf section of this page.
Similarly, the above is a snapshot of the Sysprof display of a copy-from-user callchain.
Finally, looking at the third Sysprof pane in the lower left, we can see a list of all the callers of a particular function selected in the top left pane. In this case, the lower pane is showing all the callers of __mark_inode_dirty:
Double-clicking on one of those functions will in turn change the focus to the selected function, and so on.
Tying It Together: If you like sysprof‘s ‘caller-oriented‘ display, you may be able to approximate
it in other tools as well. For example, ‘perf report‘ has the -g (--call-graph) option
that you can experiment with; one of the options is ‘caller‘ for an inverted caller-based
callgraph display.
Tying It Together: sysprof does have build options to enable use of the perf_event subsystem and
benefit from the perf_event infrastructure by adding support for something other than
system-wide profiling i.e. per-process or workload profiling, but the version in danny
doesn‘t yet take advantage of those capabilities (sysprof officially added the ability.
to make use of perf_events just as we were going to press).
Documentation
There doesn‘t seem to be any documentation for Sysprof, but maybe that‘s because it‘s pretty self-explanatory. The Sysprof website, however, is here:
Sysprof, System-wide Performance Profiler for Linux
LTTng (Linux Trace Toolkit, next generation)
Setup
NOTE: The lttng support in Yocto 1.3 (danny) needs the following poky commits applied in order to work:
- http://git.yoctoproject.org/cgit/cgit.cgi/poky-contrib/commit/?h=tzanussi/switch-to-lttng2&id=ea602300d9211669df0acc5c346e4486d6bf6f67
- http://git.yoctoproject.org/cgit/cgit.cgi/poky-contrib/commit/?h=tzanussi/lttng-fixes.0&id=1d0dc88e1635cfc24612a3e97d0391facdc2c65f
If you also want to view the LTTng traces graphically, you also need to download and install/run the ‘SR1‘ or later Juno release of eclipse e.g.:
Collecting and Viewing Traces
Once you‘ve applied the above commits and built and booted your image (you need to build the core-image-sato-sdk image or the other methods described in the General Setup section), you‘re ready to start tracing.
Collecting and viewing a trace on the target (inside a shell)
First, from the target, ssh to the target:
$ ssh -l root 192.168.1.47 The authenticity of host ‘192.168.1.47 (192.168.1.47)‘ can‘t be established. RSA key fingerprint is 23:bd:c8:b1:a8:71:52:00:ee:00:4f:64:9e:10:b9:7e. Are you sure you want to continue connecting (yes/no)? yes Warning: Permanently added ‘192.168.1.47‘ (RSA) to the list of known hosts. [email protected]‘s password:
Once on the target, use these steps to create a trace:
[email protected]:~# lttng create Spawning a session daemon Session auto-20121015-232120 created. Traces will be written in /home/root/lttng-traces/auto-20121015-232120
Enable the events you want to trace (in this case all kernel events):
[email protected]:~# lttng enable-event --kernel --all All kernel events are enabled in channel channel0
Start the trace:
[email protected]:~# lttng start Tracing started for session auto-20121015-232120
And then stop the trace after awhile or after running a particular workload that you want to trace:
[email protected]:~# lttng stop Tracing stopped for session auto-20121015-232120
You can now view the trace in text form on the target:
[email protected]:~# lttng view
[23:21:56.989270399] (+?.?????????) sys_geteuid: { 1 }, { }
[23:21:56.989278081] (+0.000007682) exit_syscall: { 1 }, { ret = 0 }
[23:21:56.989286043] (+0.000007962) sys_pipe: { 1 }, { fildes = 0xB77B9E8C }
[23:21:56.989321802] (+0.000035759) exit_syscall: { 1 }, { ret = 0 }
[23:21:56.989329345] (+0.000007543) sys_mmap_pgoff: { 1 }, { addr = 0x0, len = 10485760, prot = 3, flags = 131362, fd = 4294967295, pgoff = 0 }
[23:21:56.989351694] (+0.000022349) exit_syscall: { 1 }, { ret = -1247805440 }
[23:21:56.989432989] (+0.000081295) sys_clone: { 1 }, { clone_flags = 0x411, newsp = 0xB5EFFFE4, parent_tid = 0xFFFFFFFF, child_tid = 0x0 }
[23:21:56.989477129] (+0.000044140) sched_stat_runtime: { 1 }, { comm = "lttng-consumerd", tid = 1193, runtime = 681660, vruntime = 43367983388 }
[23:21:56.989486697] (+0.000009568) sched_migrate_task: { 1 }, { comm = "lttng-consumerd", tid = 1193, prio = 20, orig_cpu = 1, dest_cpu = 1 }
[23:21:56.989508418] (+0.000021721) hrtimer_init: { 1 }, { hrtimer = 3970832076, clockid = 1, mode = 1 }
[23:21:56.989770462] (+0.000262044) hrtimer_cancel: { 1 }, { hrtimer = 3993865440 }
[23:21:56.989771580] (+0.000001118) hrtimer_cancel: { 0 }, { hrtimer = 3993812192 }
[23:21:56.989776957] (+0.000005377) hrtimer_expire_entry: { 1 }, { hrtimer = 3993865440, now = 79815980007057, function = 3238465232 }
[23:21:56.989778145] (+0.000001188) hrtimer_expire_entry: { 0 }, { hrtimer = 3993812192, now = 79815980008174, function = 3238465232 }
[23:21:56.989791695] (+0.000013550) softirq_raise: { 1 }, { vec = 1 }
[23:21:56.989795396] (+0.000003701) softirq_raise: { 0 }, { vec = 1 }
[23:21:56.989800635] (+0.000005239) softirq_raise: { 0 }, { vec = 9 }
[23:21:56.989807130] (+0.000006495) sched_stat_runtime: { 1 }, { comm = "lttng-consumerd", tid = 1193, runtime = 330710, vruntime = 43368314098 }
[23:21:56.989809993] (+0.000002863) sched_stat_runtime: { 0 }, { comm = "lttng-sessiond", tid = 1181, runtime = 1015313, vruntime = 36976733240 }
[23:21:56.989818514] (+0.000008521) hrtimer_expire_exit: { 0 }, { hrtimer = 3993812192 }
[23:21:56.989819631] (+0.000001117) hrtimer_expire_exit: { 1 }, { hrtimer = 3993865440 }
[23:21:56.989821866] (+0.000002235) hrtimer_start: { 0 }, { hrtimer = 3993812192, function = 3238465232, expires = 79815981000000, softexpires = 79815981000000 }
[23:21:56.989822984] (+0.000001118) hrtimer_start: { 1 }, { hrtimer = 3993865440, function = 3238465232, expires = 79815981000000, softexpires = 79815981000000 }
[23:21:56.989832762] (+0.000009778) softirq_entry: { 1 }, { vec = 1 }
[23:21:56.989833879] (+0.000001117) softirq_entry: { 0 }, { vec = 1 }
[23:21:56.989838069] (+0.000004190) timer_cancel: { 1 }, { timer = 3993871956 }
[23:21:56.989839187] (+0.000001118) timer_cancel: { 0 }, { timer = 3993818708 }
[23:21:56.989841492] (+0.000002305) timer_expire_entry: { 1 }, { timer = 3993871956, now = 79515980, function = 3238277552 }
[23:21:56.989842819] (+0.000001327) timer_expire_entry: { 0 }, { timer = 3993818708, now = 79515980, function = 3238277552 }
[23:21:56.989854831] (+0.000012012) sched_stat_runtime: { 1 }, { comm = "lttng-consumerd", tid = 1193, runtime = 49237, vruntime = 43368363335 }
[23:21:56.989855949] (+0.000001118) sched_stat_runtime: { 0 }, { comm = "lttng-sessiond", tid = 1181, runtime = 45121, vruntime = 36976778361 }
[23:21:56.989861257] (+0.000005308) sched_stat_sleep: { 1 }, { comm = "kworker/1:1", tid = 21, delay = 9451318 }
[23:21:56.989862374] (+0.000001117) sched_stat_sleep: { 0 }, { comm = "kworker/0:0", tid = 4, delay = 9958820 }
[23:21:56.989868241] (+0.000005867) sched_wakeup: { 0 }, { comm = "kworker/0:0", tid = 4, prio = 120, success = 1, target_cpu = 0 }
[23:21:56.989869358] (+0.000001117) sched_wakeup: { 1 }, { comm = "kworker/1:1", tid = 21, prio = 120, success = 1, target_cpu = 1 }
[23:21:56.989877460] (+0.000008102) timer_expire_exit: { 1 }, { timer = 3993871956 }
[23:21:56.989878577] (+0.000001117) timer_expire_exit: { 0 }, { timer = 3993818708 }
.
.
.
You can now safely destroy the trace session (note that this doesn‘t delete the trace - it‘s still there in ~/lttng-traces):
[email protected]:~# lttng destroy Session auto-20121015-232120 destroyed at /home/root
Note that the trace is saved in a directory of the same name as returned by ‘lttng create‘, under the ~/lttng-traces directory (note that you can change this by supplying your own name to ‘lttng create‘):
[email protected]:~# ls -al ~/lttng-traces drwxrwx--- 3 root root 1024 Oct 15 23:21 . drwxr-xr-x 5 root root 1024 Oct 15 23:57 .. drwxrwx--- 3 root root 1024 Oct 15 23:21 auto-20121015-232120
Collecting and viewing a userspace trace on the target (inside a shell)
For lttng userspace tracing, you need to have a properly instrumented userspace program. For this example, we‘ll use the ‘hello‘ test program generated by the lttng-ust build.
The ‘hello‘ test program isn‘t installed on the rootfs by the lttng-ust build, so we need to copy it over manually. First cd into the build directory that contains the hello executable:
$ cd build/tmp/work/core2-poky-linux/lttng-ust/2.0.5-r0/git/tests/hello/.libs
Copy that over to the target machine:
$ scp hello [email protected]:
You now have the instrumented lttng ‘hello world‘ test program on the target, ready to test.
First, from the target, ssh to the target:
$ ssh -l root 192.168.1.47 The authenticity of host ‘192.168.1.47 (192.168.1.47)‘ can‘t be established. RSA key fingerprint is 23:bd:c8:b1:a8:71:52:00:ee:00:4f:64:9e:10:b9:7e. Are you sure you want to continue connecting (yes/no)? yes Warning: Permanently added ‘192.168.1.47‘ (RSA) to the list of known hosts. [email protected]‘s password:
Once on the target, use these steps to create a trace:
[email protected]:~# lttng create Session auto-20190303-021943 created. Traces will be written in /home/root/lttng-traces/auto-20190303-021943
Enable the events you want to trace (in this case all userspace events):
[email protected]:~# lttng enable-event --userspace --all All UST events are enabled in channel channel0
Start the trace:
[email protected]:~# lttng start Tracing started for session auto-20190303-021943
Run the instrumented hello world program:
[email protected]:~# ./hello Hello, World! Tracing... done.
And then stop the trace after awhile or after running a particular workload that you want to trace:
[email protected]:~# lttng stop Tracing stopped for session auto-20190303-021943
You can now view the trace in text form on the target:
[email protected]:~# lttng view
[02:31:14.906146544] (+?.?????????) hello:1424 ust_tests_hello:tptest: { cpu_id = 1 }, { intfield = 0, intfield2 = 0x0, longfield = 0, netintfield = 0, netintfieldhex = 0x0, arrfield1 = [ [0] = 1, [1] = 2, [2] = 3 ], arrfield2 = "test", _seqfield1_length = 4, seqfield1 = [ [0] = 116, [1] = 101, [2] = 115, [3] = 116 ], _seqfield2_length = 4, seqfield2 = "test", stringfield = "test", floatfield = 2222, doublefield = 2, boolfield = 1 }
[02:31:14.906170360] (+0.000023816) hello:1424 ust_tests_hello:tptest: { cpu_id = 1 }, { intfield = 1, intfield2 = 0x1, longfield = 1, netintfield = 1, netintfieldhex = 0x1, arrfield1 = [ [0] = 1, [1] = 2, [2] = 3 ], arrfield2 = "test", _seqfield1_length = 4, seqfield1 = [ [0] = 116, [1] = 101, [2] = 115, [3] = 116 ], _seqfield2_length = 4, seqfield2 = "test", stringfield = "test", floatfield = 2222, doublefield = 2, boolfield = 1 }
[02:31:14.906183140] (+0.000012780) hello:1424 ust_tests_hello:tptest: { cpu_id = 1 }, { intfield = 2, intfield2 = 0x2, longfield = 2, netintfield = 2, netintfieldhex = 0x2, arrfield1 = [ [0] = 1, [1] = 2, [2] = 3 ], arrfield2 = "test", _seqfield1_length = 4, seqfield1 = [ [0] = 116, [1] = 101, [2] = 115, [3] = 116 ], _seqfield2_length = 4, seqfield2 = "test", stringfield = "test", floatfield = 2222, doublefield = 2, boolfield = 1 }
[02:31:14.906194385] (+0.000011245) hello:1424 ust_tests_hello:tptest: { cpu_id = 1 }, { intfield = 3, intfield2 = 0x3, longfield = 3, netintfield = 3, netintfieldhex = 0x3, arrfield1 = [ [0] = 1, [1] = 2, [2] = 3 ], arrfield2 = "test", _seqfield1_length = 4, seqfield1 = [ [0] = 116, [1] = 101, [2] = 115, [3] = 116 ], _seqfield2_length = 4, seqfield2 = "test", stringfield = "test", floatfield = 2222, doublefield = 2, boolfield = 1 }
.
.
.
You can now safely destroy the trace session (note that this doesn‘t delete the trace - it‘s still there in ~/lttng-traces):
[email protected]:~# lttng destroy Session auto-20190303-021943 destroyed at /home/root
Manually copying a trace to the host and viewing it in Eclipse (i.e. using Eclipse without network support)
If you already have an LTTng trace on a remote target and would like to view it in Eclipse on the host, you can easily copy it from the target to the host and import it into Eclipse to view it using the LTTng Eclipse plugin already bundled in the Eclipse (Juno SR1 or greater).
Using the trace we created in the previous section, archive it and copy it to your host system:
[email protected]:~/lttng-traces# tar zcvf auto-20121015-232120.tar.gz auto-20121015-232120 auto-20121015-232120/ auto-20121015-232120/kernel/ auto-20121015-232120/kernel/metadata auto-20121015-232120/kernel/channel0_1 auto-20121015-232120/kernel/channel0_0
$ scp [email protected]:lttng-traces/auto-20121015-232120.tar.gz . [email protected]‘s password: auto-20121015-232120.tar.gz 100% 1566KB 1.5MB/s 00:01
Unarchive it on the host:
$ gunzip -c auto-20121015-232120.tar.gz | tar xvf - auto-20121015-232120/ auto-20121015-232120/kernel/ auto-20121015-232120/kernel/metadata auto-20121015-232120/kernel/channel0_1 auto-20121015-232120/kernel/channel0_0
We can now import the trace into Eclipse and view it:
- First, start eclipse and open the ‘LTTng Kernel‘ perspective by selecting the following menu item:
Window | Open Perspective | Other...
- In the dialog box that opens, select ‘LTTng Kernel‘ from the list.
- Back at the main menu, select the following menu item:
File | New | Project...
- In the dialog box that opens, select the ‘Tracing | Tracing Project‘ wizard and press ‘Next>‘.
- Give the project a name and press ‘Finish‘.
- In the ‘Project Explorer‘ pane under the project you created, right click on the ‘Traces‘ item.
- Select ‘Import..." and in the dialog that‘s displayed:
- Browse the filesystem and find the select the ‘kernel‘ directory containing the trace you copied from the target e.g. auto-20121015-232120/kernel
- ‘Checkmark‘ the directory in the tree that‘s displayed for the trace
- Below that, select ‘Common Trace Format: Kernel Trace‘ for the ‘Trace Type‘
- Press ‘Finish‘ to close the dialog
- Back in the ‘Project Explorer‘ pane, double-click on the ‘kernel‘ item for the trace you just imported under ‘Traces‘
You should now see your trace data displayed graphically in several different views in Eclipse:
You can access extensive help information on how to use the LTTng plugin to search and analyze captured traces via the Eclipse help system:
Help | Help Contents | LTTng Plug-in User Guide
Collecting and viewing a trace in Eclipse
NOTE: This section on collecting traces remotely doesn‘t currently work because of Eclipse ‘RSE‘ connectivity problems. Manually tracing on the target, copying the trace files to the host, and viewing the trace in Eclipse on the host as outlined in previous steps does work however - please use the manual steps outlined above to view traces in Eclipse.
In order to trace a remote target, you also need to add a ‘tracing‘ group on the target and connect as a user who‘s part of that group e.g:
# adduser tomz # groupadd -r tracing # usermod -a -G tracing tomz
- First, start eclipse and open the ‘LTTng Kernel‘ perspective by selecting the following menu item:
Window | Open Perspective | Other...
- In the dialog box that opens, select ‘LTTng Kernel‘ from the list.
- Back at the main menu, select the following menu item:
File | New | Project...
- In the dialog box that opens, select the ‘Tracing | Tracing Project‘ wizard and press ‘Next>‘.
- Give the project a name and press ‘Finish‘.
That should result in an entry in the ‘Project‘ subwindow.
- In the ‘Control‘ subwindow just below it, press ‘New Connection‘.
- Add a new connection, giving it the hostname or IP address of the target system.
Also provide the username and password of a qualified user (a member of the ‘tracing‘ group) or root account on the target system.
Also, provide appropriate answers to whatever else is asked for e.g. ‘secure storage password‘ can be anything you want
If you get an ‘RSE Error‘ it may be due to proxies. It may be possible to get around the problem by changing the following setting:
Window | Preferences | Network Connections
Switch ‘Active Provider‘ to ‘Direct‘
Documentation
There doesn‘t seem to be any current documentation covering LTTng 2.0, but maybe that‘s because the project is in transition. The LTTng 2.0 website, however, is here: LTTng Project
You can access extensive help information on how to use the LTTng plug-in to search and analyze captured traces via the Eclipse help system:
Help | Help Contents | LTTng Plug-in User Guide
blktrace
blktrace is a tool for tracing and reporting low-level disk I/O. blktrace provides the tracing half of the equation; its output can be piped into the blkparse program, which renders the data in a human-readable form and does some basic analysis:
Setup
For this section, we‘ll assume you‘ve already performed the basic setup outlined in the General Setupsection.
blktrace is an application that runs on the target system. You can run the entire blktrace and blkparse pipeline on the target, or you can run blktrace in ‘listen‘ mode on the target and have blktrace and blkparse collect and analyze the data on the host (see the section on using blktrace remotely below). For the rest of this section we assume you‘ve ssh‘ed to the host and will be running blkrace on the target.
Basic Usage
To record a trace, simply run the ‘blktrace‘ command, giving it the name of the block device you want to trace activity on:
[email protected]:~# blktrace /dev/sdc
In another shell, execute a workload you want to trace.
[email protected]:/media/sdc# rm linux-2.6.19.2.tar.bz2; wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2; sync Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |*******************************| 41727k 0:00:00 ETA
Press Ctrl-C in the blktrace shell to stop the trace. It will display how many events were logged, along with the per-cpu file sizes (blktrace records traces in per-cpu kernel buffers and simply dumps them to userspace for blkparse to merge and sort later).
^C=== sdc === CPU 0: 7082 events, 332 KiB data CPU 1: 1578 events, 74 KiB data Total: 8660 events (dropped 0), 406 KiB data
If you examine the files saved to disk, you see multiple files, one per CPU and with the device name as the first part of the filename:
[email protected]:~# ls -al drwxr-xr-x 6 root root 1024 Oct 27 22:39 . drwxr-sr-x 4 root root 1024 Oct 26 18:24 .. -rw-r--r-- 1 root root 339938 Oct 27 22:40 sdc.blktrace.0 -rw-r--r-- 1 root root 75753 Oct 27 22:40 sdc.blktrace.1
To view the trace events, simply invoke ‘blkparse‘ in the directory containing the trace files, giving it the device name that forms the first part of the filenames:
[email protected]:~# blkparse sdc
8,32 1 1 0.000000000 1225 Q WS 3417048 + 8 [jbd2/sdc-8] 8,32 1 2 0.000025213 1225 G WS 3417048 + 8 [jbd2/sdc-8] 8,32 1 3 0.000033384 1225 P N [jbd2/sdc-8] 8,32 1 4 0.000043301 1225 I WS 3417048 + 8 [jbd2/sdc-8] 8,32 1 0 0.000057270 0 m N cfq1225 insert_request 8,32 1 0 0.000064813 0 m N cfq1225 add_to_rr 8,32 1 5 0.000076336 1225 U N [jbd2/sdc-8] 1 8,32 1 0 0.000088559 0 m N cfq workload slice:150 8,32 1 0 0.000097359 0 m N cfq1225 set_active wl_prio:0 wl_type:1 8,32 1 0 0.000104063 0 m N cfq1225 Not idling. st->count:1 8,32 1 0 0.000112584 0 m N cfq1225 fifo= (null) 8,32 1 0 0.000118730 0 m N cfq1225 dispatch_insert 8,32 1 0 0.000127390 0 m N cfq1225 dispatched a request 8,32 1 0 0.000133536 0 m N cfq1225 activate rq, drv=1 8,32 1 6 0.000136889 1225 D WS 3417048 + 8 [jbd2/sdc-8] 8,32 1 7 0.000360381 1225 Q WS 3417056 + 8 [jbd2/sdc-8] 8,32 1 8 0.000377422 1225 G WS 3417056 + 8 [jbd2/sdc-8] 8,32 1 9 0.000388876 1225 P N [jbd2/sdc-8] 8,32 1 10 0.000397886 1225 Q WS 3417064 + 8 [jbd2/sdc-8] 8,32 1 11 0.000404800 1225 M WS 3417064 + 8 [jbd2/sdc-8] 8,32 1 12 0.000412343 1225 Q WS 3417072 + 8 [jbd2/sdc-8] 8,32 1 13 0.000416533 1225 M WS 3417072 + 8 [jbd2/sdc-8] 8,32 1 14 0.000422121 1225 Q WS 3417080 + 8 [jbd2/sdc-8] 8,32 1 15 0.000425194 1225 M WS 3417080 + 8 [jbd2/sdc-8] 8,32 1 16 0.000431968 1225 Q WS 3417088 + 8 [jbd2/sdc-8] 8,32 1 17 0.000435251 1225 M WS 3417088 + 8 [jbd2/sdc-8] 8,32 1 18 0.000440279 1225 Q WS 3417096 + 8 [jbd2/sdc-8] 8,32 1 19 0.000443911 1225 M WS 3417096 + 8 [jbd2/sdc-8] 8,32 1 20 0.000450336 1225 Q WS 3417104 + 8 [jbd2/sdc-8] 8,32 1 21 0.000454038 1225 M WS 3417104 + 8 [jbd2/sdc-8] 8,32 1 22 0.000462070 1225 Q WS 3417112 + 8 [jbd2/sdc-8] 8,32 1 23 0.000465422 1225 M WS 3417112 + 8 [jbd2/sdc-8] 8,32 1 24 0.000474222 1225 I WS 3417056 + 64 [jbd2/sdc-8] 8,32 1 0 0.000483022 0 m N cfq1225 insert_request 8,32 1 25 0.000489727 1225 U N [jbd2/sdc-8] 1 8,32 1 0 0.000498457 0 m N cfq1225 Not idling. st->count:1 8,32 1 0 0.000503765 0 m N cfq1225 dispatch_insert 8,32 1 0 0.000512914 0 m N cfq1225 dispatched a request 8,32 1 0 0.000518851 0 m N cfq1225 activate rq, drv=2 . . . 8,32 0 0 58.515006138 0 m N cfq3551 complete rqnoidle 1 8,32 0 2024 58.516603269 3 C WS 3156992 + 16 [0] 8,32 0 0 58.516626736 0 m N cfq3551 complete rqnoidle 1 8,32 0 0 58.516634558 0 m N cfq3551 arm_idle: 8 group_idle: 0 8,32 0 0 58.516636933 0 m N cfq schedule dispatch 8,32 1 0 58.516971613 0 m N cfq3551 slice expired t=0 8,32 1 0 58.516982089 0 m N cfq3551 sl_used=13 disp=6 charge=13 iops=0 sect=80 8,32 1 0 58.516985511 0 m N cfq3551 del_from_rr 8,32 1 0 58.516990819 0 m N cfq3551 put_queue CPU0 (sdc): Reads Queued: 0, 0KiB Writes Queued: 331, 26,284KiB Read Dispatches: 0, 0KiB Write Dispatches: 485, 40,484KiB Reads Requeued: 0 Writes Requeued: 0 Reads Completed: 0, 0KiB Writes Completed: 511, 41,000KiB Read Merges: 0, 0KiB Write Merges: 13, 160KiB Read depth: 0 Write depth: 2 IO unplugs: 23 Timer unplugs: 0 CPU1 (sdc): Reads Queued: 0, 0KiB Writes Queued: 249, 15,800KiB Read Dispatches: 0, 0KiB Write Dispatches: 42, 1,600KiB Reads Requeued: 0 Writes Requeued: 0 Reads Completed: 0, 0KiB Writes Completed: 16, 1,084KiB Read Merges: 0, 0KiB Write Merges: 40, 276KiB Read depth: 0 Write depth: 2 IO unplugs: 30 Timer unplugs: 1 Total (sdc): Reads Queued: 0, 0KiB Writes Queued: 580, 42,084KiB Read Dispatches: 0, 0KiB Write Dispatches: 527, 42,084KiB Reads Requeued: 0 Writes Requeued: 0 Reads Completed: 0, 0KiB Writes Completed: 527, 42,084KiB Read Merges: 0, 0KiB Write Merges: 53, 436KiB IO unplugs: 53 Timer unplugs: 1 Throughput (R/W): 0KiB/s / 719KiB/s Events (sdc): 6,592 entries Skips: 0 forward (0 - 0.0%) Input file sdc.blktrace.0 added Input file sdc.blktrace.1 added
The report shows each event that was found in the blktrace data, along with a summary of the overall block I/O traffic during the run. You can look at the blkparse manpage to learn the meaning of each field displayed in the trace listing.
Live Mode
blktrace and blkparse are designed from the ground up to be able to operate together in a ‘pipe mode‘ where the stdout of blktrace can be fed directly into the stdin of blkparse:
[email protected]:~# blktrace /dev/sdc -o - | blkparse -i -
This enables long-lived tracing sessions to run without writing anything to disk, and allows the user to look for certain conditions in the trace data in ‘real-time‘ by viewing the trace output as it scrolls by on the screen or by passing it along to yet another program in the pipeline such as grep which can be used to identify and capture conditions of interest.
There‘s actually another blktrace command that implements the above pipeline as a single command, so the user doesn‘t have to bother typing in the above command sequence:
[email protected]:~# btrace /dev/sdc
Using blktrace remotely
Because blktrace traces block I/O and at the same time normally writes its trace data to a block device, and in general because it‘s not really a great idea to make the device being traced the same as the device the tracer writes to, blktrace provides a way to trace without perturbing the traced device at all by providing native support for sending all trace data over the network.
To have blktrace operate in this mode, start blktrace on the target system being traced with the -l option, along with the device to trace:
[email protected]:~# blktrace -l /dev/sdc server: waiting for connections...
On the host system, use the -h option to connect to the target system, also passing it the device to trace:
$ blktrace -d /dev/sdc -h 192.168.1.43 blktrace: connecting to 192.168.1.43 blktrace: connected!
On the target system, you should see this:
server: connection from 192.168.1.43
In another shell, execute a workload you want to trace.
[email protected]:/media/sdc# rm linux-2.6.19.2.tar.bz2; wget http://downloads.yoctoproject.org/mirror/sources/linux-2.6.19.2.tar.bz2; sync Connecting to downloads.yoctoproject.org (140.211.169.59:80) linux-2.6.19.2.tar.b 100% |*******************************| 41727k 0:00:00 ETA
When it‘s done, do a Ctrl-C on the host system to stop the trace:
^C=== sdc === CPU 0: 7691 events, 361 KiB data CPU 1: 4109 events, 193 KiB data Total: 11800 events (dropped 0), 554 KiB data
On the target system, you should also see a trace summary for the trace just ended:
server: end of run for 192.168.1.43:sdc === sdc === CPU 0: 7691 events, 361 KiB data CPU 1: 4109 events, 193 KiB data Total: 11800 events (dropped 0), 554 KiB data
The blktrace instance on the host will save the target output inside a hostname-timestamp directory:
$ ls -al drwxr-xr-x 10 root root 1024 Oct 28 02:40 . drwxr-sr-x 4 root root 1024 Oct 26 18:24 .. drwxr-xr-x 2 root root 1024 Oct 28 02:40 192.168.1.43-2012-10-28-02:40:56
cd into that directory to see the output files:
$ ls -l -rw-r--r-- 1 root root 369193 Oct 28 02:44 sdc.blktrace.0 -rw-r--r-- 1 root root 197278 Oct 28 02:44 sdc.blktrace.1
And run blkparse on the host system using the device name:
$ blkparse sdc 8,32 1 1 0.000000000 1263 Q RM 6016 + 8 [ls] 8,32 1 0 0.000036038 0 m N cfq1263 alloced 8,32 1 2 0.000039390 1263 G RM 6016 + 8 [ls] 8,32 1 3 0.000049168 1263 I RM 6016 + 8 [ls] 8,32 1 0 0.000056152 0 m N cfq1263 insert_request 8,32 1 0 0.000061600 0 m N cfq1263 add_to_rr 8,32 1 0 0.000075498 0 m N cfq workload slice:300 . . . 8,32 0 0 177.266385696 0 m N cfq1267 arm_idle: 8 group_idle: 0 8,32 0 0 177.266388140 0 m N cfq schedule dispatch 8,32 1 0 177.266679239 0 m N cfq1267 slice expired t=0 8,32 1 0 177.266689297 0 m N cfq1267 sl_used=9 disp=6 charge=9 iops=0 sect=56 8,32 1 0 177.266692649 0 m N cfq1267 del_from_rr 8,32 1 0 177.266696560 0 m N cfq1267 put_queue CPU0 (sdc): Reads Queued: 0, 0KiB Writes Queued: 270, 21,708KiB Read Dispatches: 59, 2,628KiB Write Dispatches: 495, 39,964KiB Reads Requeued: 0 Writes Requeued: 0 Reads Completed: 90, 2,752KiB Writes Completed: 543, 41,596KiB Read Merges: 0, 0KiB Write Merges: 9, 344KiB Read depth: 2 Write depth: 2 IO unplugs: 20 Timer unplugs: 1 CPU1 (sdc): Reads Queued: 688, 2,752KiB Writes Queued: 381, 20,652KiB Read Dispatches: 31, 124KiB Write Dispatches: 59, 2,396KiB Reads Requeued: 0 Writes Requeued: 0 Reads Completed: 0, 0KiB Writes Completed: 11, 764KiB Read Merges: 598, 2,392KiB Write Merges: 88, 448KiB Read depth: 2 Write depth: 2 IO unplugs: 52 Timer unplugs: 0 Total (sdc): Reads Queued: 688, 2,752KiB Writes Queued: 651, 42,360KiB Read Dispatches: 90, 2,752KiB Write Dispatches: 554, 42,360KiB Reads Requeued: 0 Writes Requeued: 0 Reads Completed: 90, 2,752KiB Writes Completed: 554, 42,360KiB Read Merges: 598, 2,392KiB Write Merges: 97, 792KiB IO unplugs: 72 Timer unplugs: 1 Throughput (R/W): 15KiB/s / 238KiB/s Events (sdc): 9,301 entries Skips: 0 forward (0 - 0.0%)
You should see the trace events and summary just as you would have if you‘d run the same command on the target.
Tracing Block I/O via ‘ftrace‘
It‘s also possible to trace block I/O using only trace events subsystem, which can be useful for casual tracing if you don‘t want bother dealing with the userspace tools.
To enable tracing for a given device, use /sys/block/xxx/trace/enable, where xxx is the device name. This for example enables tracing for /dev/sdc:
[email protected]:/sys/kernel/debug/tracing# echo 1 > /sys/block/sdc/trace/enable
Once you‘ve selected the device(s) you want to trace, selecting the ‘blk‘ tracer will turn the blk tracer on:
[email protected]:/sys/kernel/debug/tracing# cat available_tracers blk function_graph function nop
[email protected]:/sys/kernel/debug/tracing# echo blk > current_tracer
Execute the workload you‘re interested in:
[email protected]:/sys/kernel/debug/tracing# cat /media/sdc/testfile.txt
And look at the output (note here that we‘re using ‘trace_pipe‘ instead of trace to capture this trace - this allows us to wait around on the pipe for data to appear):
[email protected]:/sys/kernel/debug/tracing# cat trace_pipe
cat-3587 [001] d..1 3023.276361: 8,32 Q R 1699848 + 8 [cat]
cat-3587 [001] d..1 3023.276410: 8,32 m N cfq3587 alloced
cat-3587 [001] d..1 3023.276415: 8,32 G R 1699848 + 8 [cat]
cat-3587 [001] d..1 3023.276424: 8,32 P N [cat]
cat-3587 [001] d..2 3023.276432: 8,32 I R 1699848 + 8 [cat]
cat-3587 [001] d..1 3023.276439: 8,32 m N cfq3587 insert_request
cat-3587 [001] d..1 3023.276445: 8,32 m N cfq3587 add_to_rr
cat-3587 [001] d..2 3023.276454: 8,32 U N [cat] 1
cat-3587 [001] d..1 3023.276464: 8,32 m N cfq workload slice:150
cat-3587 [001] d..1 3023.276471: 8,32 m N cfq3587 set_active wl_prio:0 wl_type:2
cat-3587 [001] d..1 3023.276478: 8,32 m N cfq3587 fifo= (null)
cat-3587 [001] d..1 3023.276483: 8,32 m N cfq3587 dispatch_insert
cat-3587 [001] d..1 3023.276490: 8,32 m N cfq3587 dispatched a request
cat-3587 [001] d..1 3023.276497: 8,32 m N cfq3587 activate rq, drv=1
cat-3587 [001] d..2 3023.276500: 8,32 D R 1699848 + 8 [cat]
And this turns off tracing for the specified device:
[email protected]:/sys/kernel/debug/tracing# echo 0 > /sys/block/sdc/trace/enable
Documentation
Online versions of the man pages for the commands discussed in this section can be found here:
- http://linux.die.net/man/8/blktrace
- http://linux.die.net/man/1/blkparse
- http://linux.die.net/man/8/btrace
The above manpages, along with manpages for the other blktrace utilities (btt, blkiomon, etc) can be found in the /doc directory of the blktrace tools git repo:
$ git clone git://git.kernel.dk/blktrace.git
Real-world Examples
Slow write speed on live images
In one of our previous releases (denzil), users noticed that booting off of a live image and writing to disk was noticeably slower. This included the boot itself, especially the first one, since first boots tend to do a significant amount of writing due to certain post-install scripts.
The problem (and solution) was discovered by using the Yocto tracing tools, in this case ‘perf stat‘, ‘perf script‘, ‘perf record‘ and ‘perf report‘.
See all the unvarnished details of how this bug was diagnosed and solved here: Yocto Bug #3049
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