pmu tools is a collection of tools for profile collection and performance analysis on Intel CPUs on top of Linux perf. This uses performance counters in the CPU.
- The "ocperf" wrapper to "perf" that provides a full core performance counter event list for common Intel CPUs. This allows to use all the Intel events, not just the builtin events of perf. Can be also used as a library from other python programs
- The "toplev.py" tool to do cycle decomposition for a workload. It can measure where in the CPU pipe line the bottleneck of a workload occurs. This implements the TopDown methology.
- The "ucevent" tool to manage and compute uncore performance events on Intel Xeon E5 2600 series (SandyBridge EP). Uncore is the part of the CPU that is not core. Supports many metrics for power management, IO, QPI (interconnect), caches, and others. ucevent automatically generates event descriptions for the perf uncore driver and pretty prints the output. It also supports computing higher level metrics derived from multiple events.
- A library for self profiling with Linux since Linux 3.3. Self profiling is a program monitoring its own execution, either for controlled benchmarking or to optimize itself. For self-profiling on older kernels you can use simple-pmu
- Support for Intel "offcore" events on older Linux systems where the kernel perf subsystem does not support them natively. Offcore events allow to categorize memory accesses that go outside the core.
- Workarounds for some issues with offcore events on Sandy Bridge EP (Intel Xeon E5 v1) This is automatically enabled for the respective events with ocperf, and also available as a standalone program or python library.
- A variety of tools for plotting and post processing perf stat -I1000 -x, or toplev.py -I1000 -x, interval measurements.
- An example program for address profiling on Nehalem and later Intel CPUs (addr)
- Some utility programs to access pci space or msrs on the command line
- A utility program to program the PMU directly from user space (pmumon.py) for counting. This is mainly useful for testing and experimental purposes.
- A program to print the currently running events (event-rmap)
- Support for analyzing the raw PEBS records with perf.
- A pandas/scipy data model for perf.data analytics (work in progress)
Check out the repository. Run the tools from the directory you checked out (but it does not need to be the current directory) They automatically search for other modules and data files in the same directory the script was located in.
ocperf is a wrapper to "perf" that provides a full core event list for common Intel CPUs. This allows to use all the Intel defined events, not just the builtin events of perf.
A more detailed introduction is in Andi's blog
ocperf.py list List all the events perf and ocperf supports on the current CPU
ocperf.py stat -e eventname ... ocperf.py record -c default -e eventname ... ocperf.py report
When a older kernel is used with offcore events (events that count types of memory accesses outside the CPU core) that does not support offcore events natively, ocperf has to run as root and only one such profiling can be active on a machine.
When "-c default" is specified for record, the default sampling overflow value will be filled in for the sampling period. This option needs to be specified before the events and is not supported for all CPUs. By default perf uses a dynamic sampling period, which can cause varying (and sometimes large) overhead. The fixed period minimizes this problem.
If you have trouble with one of the many acronyms in the event list descriptions, the Intel optimization manual describes many of them.
ocperf.py can be also used as a python module to convert or list events for the current CPU:
import ocperf emap = ocperf.find_emap() if not emap: sys.exit("Unknown CPU or cannot find event table") ev = emap.getevent("BR_MISP_EXEC.ANY") if ev: print "name:", ev.output() print "raw form:", ev.output(use_raw=True) print "description:, ev.desc
To retrieve data for other CPUs set the EVENTMAP environment variable to the csv file of the CPU before calling find_emap()
The msr.py, pci.py, latego.py can be used as standalone programs or python modules to change MSRs, PCI config space or enable/disable the workarounds.
For example to set the MSR 0x123 on all CPUs to value 1 use:
$ sudo ./msr.py 0x123 1
To read MSR 0x123 on CPU 0
$ sudo ./msr.py 0x123
To read MSR 0x123 on CPU 3:
$ sudo python >>> import msr >>> msr.readmsr(0x123, 3)
To set bit 0 in MSR 0x123 on all CPUs:
$ sudo python >>> import msr >>> msr.writemsr(0x123, msr.readmsr(0x123) | 1)
(this assumes the MSR has the same value on all CPUs, otherwise iterate the readmsr over the CPUs)
Do cycle decomposition on a workload: estimate on which part of the CPU pipeline the bottle neck occurs. The bottlenecks are expressed as a tree with different levels (max 4). Each bottleneck is only meaningful if the parent higher level crossed the threshold (it acts similar to a binary search). The tool automatically only prints meaningful ratios, unless -v is specified.
This follows the "Top Down" methology described in B.3.2 of the Intel optimization manual and in this article and in Ahmad Yasin's presentation at a ISCA workshop. I didn't invent it, I'm just implementing it.
A more gentle introduction is in andi's blog
toplev.py only supports counting, that is it cannot tell you where in the program the problem occurred, just what happened.
Requires an Intel Sandy Bridge, Ivy Bridge, Haswell CPU (Core 2rd, 3rd, 4th generation) On Sandy Bridge E/EP (Xeon E5, Core i7 39xx) only level 1 is supported currently.
It works best on Ivy Bridge currently. By default the simple high level model is used. The detailed model is selected with -d, and the max level specified with -l (max 4, default 2).
./toplev.py [-lX] [-v] [-d] [-o logfile] program measure program ./toplev.py [-lX] [-v] [-d] [-o logfile] -a sleep X measure whole system for X seconds ./toplev.py [-lX] [-v] [-d] [-o logfile] -p PID measure pid PID
-o set output file -v print everything -d use detailed model if available (only Ivy Bridge currently) -lLEVEL only use events upto max level (max 4) -x, Enable CSV mode with separator , -Ixxx Enable interval mode, measure every xxx ms --kernel Only measure kernel code --user Only measure user code
Other perf arguments allowed (see the perf documentation) After -- perf arguments conflicting with toplevel can be used.
The lower levels of the measurement tree are less reliable than the higher levels. They also rely on counter multi-plexing and cannot use groups, which can cause larger measurement errors with non steady state workloads.
(If you don't understand this terminology; it means measurements in higher levels are less accurate and it works best with programs that primarily do the same thing over and over)
It's recommended to measure the work load only after the startup phase by using interval mode or attaching later. level 1 or running without -d is generally the most reliable. If your perf stat is new enough (3.12+) the --initial-delay option is useful to skip the startup phase.
One of the events (even used by level 1) requires a recent enough kernel that understands its counter constraints. 3.10+ is safe.
If you are on SandyBridge E/EP which does not have a detailed model currently it's possible to force normal SandyBridge with "FORCECPU=snb ./toplev.py ..." to get higher levels working. However some of the estimated latencies will be wrong and some tree nodes may not work correctly. You have been warned.
Please see the ucevent documentation
interval-plot.py can plot the output of perf stat -I1000 -x
Requires matplotlib to be installed.
Below is the level 2 toplev measurement of a Linux kernel compile. Note that tl-barplot below is normally better to plot toplev output.
This converts the output of perf stat -Ixxx -x, / toplev.py -Ixxx -x, to a normalized output (one column for each event). This allows easier plotting and processing with other tools (spreadsheets, R, JMP, gnuplot etc.)
Plot an already normalized data file. Requires pyplot to be installed.
Plot output from toplev.py -I 1000 -v -x, --output file.csv -lLEVELS toplev outputs percentages, so it's better to use a stacked plot, instead of the absolute values interval-plot displays. tl-barplot implements a stacked barplot plot.
Requires matplotlib to be installed.
toplev.py -I 100 -v -x, -l3 --output grep.3.csv grep -r foo /usr/* tl-barplot.py grep.3.csv --title "GNU grep"
With a new enough matplotlib you can also enable xkcd mode (install Humor Sans first)
toplev.py -I 100 -v -x, -l2 --output povray.2.csv povray --benchmark tl-barplot.py povray.2.csv --title "povray" --output povray-xkcd.png --xkcd
Self is a simple library to support self-profiling of programs, that is programs that measure their own execution.
cd self make
Read the documentation.
rdpmc is the basic facility to access raw counters.
ocperf can be used to generate raw perf numbers for your CPU to pass to rdpmc_open()
ocperf list | less
DIRECT_MSR=1 ./ocperf.py stat -e eventname true
XXX is the needed event number in hex. Note that self does not support offcore or uncore events.
Also the event numbers are CPU specific, so you may need a /proc/cpuinfo model check for portable programs (see the ocperf source for example)
Example (replace EVENTNUMBER with your intended event from above or a perf event like PERF_COUNT_HW_CPU_CYCLES).
This is very simplified, for a real benchmark you almost certainly want some warmup, multiple iterations, possibly context switch filtering and some filler code to avoid cache effects.
#include "rdpmc.h" struct rdpmc_ctx ctx; unsigned long long start, end; if (rdpmc_open(EVENTNUMBER, &ctx) < 0) ... error ... start = rdpmc_read(&ctx); ... your workload ... end = rdpmc_read(&ctx);
supports event group profiling.
interrupts provides functions for a common use case of filtering out context switches and interrupts from micro benchmarks. These only work on Intel Ivy and Sandy Bridge CPUs.
Link the object files and include the header files in your program
/sys/devices/cpu/rdpmc must be 1.
rtest.c and test2.c provide examples. http://halobates.de/modern-pmus-yokohama.pdf provides some additional general information on cycle counting. The techniques used with simple-pmu described there can be used with self too.
Older perf doesn't export the raw PEBS output, which contains a lot of useful information. PEBS is a sampling format generated by Intel CPUs for some events.
Update: As of kernel 3.12 basically all PEBS information can be accessed natively with perf. So pebs-grabber is obsolete. However it's still useful if you're on an older kernel and maybe for some special applications.
pebs-grabber gabs PEBS data from perf. This assumes the perf pebs handler is running, we just also do trace points with the raw data.
May need some minor tweaks as kernel interface change, and will also not likely work on very old kernels.
This will create two new trace points trace_pebs_v1 and trace_pebs_v2 that log the complete PEBS record. When the CPU supports PEBSv2 (Haswell) the additional fields will be logged in pebs_v2.
make [KDIR=/my/kernel/build/dir] insmod pebs-grabber.ko # needs to record as root perf record -e cycles:p,pebs_v1,pebs\_v2 [command, -a for all etc.] perf report perf script to display pebs data # alternatively trace-cmd and kernelshark can be also used to dump # the pebs data
See http://download.intel.com/products/processor/manual/253669.pdf 18.10.2 for a description of the PEBS fields.
Note this doesn't work with standard FC18 kernels, as they broke trace points in modules. It works with later and earlier kernels.
addr is a basic frame work for self profiling of memory addresses accessed by the program. Requires a Linux 3.10+ kernel and a supported CPU.
event-rmap [cpu] prints the currently running events. This provides an easier answer to question Q2j in Vince Weaver's perf events FAQ.
ocperf, toplev, ucevent are under GPLv2, self and addr under the modified BSD license.
Andi Kleen email@example.com