Difference between revisions of "Runtime profiling"
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== How to use gprof == | == How to use gprof == | ||
| − | The first step is to compile and link the program with profiling enabled. For most compilers this is achieved by setting the | + | The first step is to compile and link the program with profiling enabled. For most compilers this is achieved by setting the '''-pg''' flag. For Intel compilers it is important to set the optimization flag afterward as the default optimization level is set to '''-O0''' when enabling profiling. |
Example for build options with Intel tool chain: | Example for build options with Intel tool chain: | ||
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</syntaxhighlight> | </syntaxhighlight> | ||
| − | The generated application is executed and generates a | + | The generated application is executed and generates a '''gmon.out''' file containing the profiling output. |
| − | To analyse the profile the tool | + | To analyse the profile the tool '''gprof''' is used with the executable as argument: |
<syntaxhighlight lang="sh"> | <syntaxhighlight lang="sh"> | ||
Revision as of 15:09, 2 April 2019
Introduction
The initial task in any performance analysis is to figure out in which parts of the code the runtime is spent. One wants to focus for optimisation on those regions of the code to achieve an overall speedup of the code. The tool helping to get an overview of where the time is spent is called a runtime profiler. There exist two flavours: Instrumentation based and sampling based profilers. Instrumentation based profilers insert function calls to measure the time at points in the program. Additional tasks may be performed as e.g. determining the function call stack. While it is possible to insert instrumentation calls on the binary level the common way is that the compiler adds instrumentation functions. The standard tool in this area is gprof and almost any compiler supports to instrument the code for gprof. Statistical sampling based profiling on the other hand are based on probing of the programs call stack triggered by operating system interrupts at regular intervals. A widespread tool for sampling based profiling is the perf tool which builds on the builtin profiling infrastructure in recent Linux kernels. Both approaches have advantages and disadvantages: Instrumentation produces more accurate results but introduces more overhead and sampling has less overhead but produce less accurate results. Special care is necessary for runtime profiling of parallel (OpenMP or MPI) applications.
How to use gprof
The first step is to compile and link the program with profiling enabled. For most compilers this is achieved by setting the -pg flag. For Intel compilers it is important to set the optimization flag afterward as the default optimization level is set to -O0 when enabling profiling.
Example for build options with Intel tool chain:
icc -pg -O3 -c myfile1.c
icc -pg -O3 -c myfile2.c
icc -o a.out -pg myfile1.o myfile2.o
The generated application is executed and generates a gmon.out file containing the profiling output. To analyse the profile the tool gprof is used with the executable as argument:
gprof ./miniMD
Refer to the gprof man page for additional command line options. The result will be printed on stdout. It is recommended to either redirect the output to a file or use a pipe to the less pager command.
gprof ./miniMD | less
The default output consists of three parts: A flat profile, the call graph, and an alphabetical index of routines. For most purposes the flat profile is what you are looking for.
Example output of the flat profile for the Mantevo miniMD proxy app:
Each sample counts as 0.01 seconds.
% cumulative self self total
time seconds seconds calls s/call s/call name
66.86 26.14 26.14 502 0.05 0.05 ForceLJ::compute(Atom&, Neighbor&, Comm&, int)
30.77 38.17 12.03 26 0.46 0.46 Neighbor::build(Atom&)
1.43 38.73 0.56 1 0.56 38.46 Integrate::run(Atom&, Force*, Neighbor&, Comm&, Thermo&, Timer&)
0.36 38.87 0.14 2850 0.00 0.00 Atom::pack_comm(int, int*, double*, int*)
0.15 38.93 0.06 2850 0.00 0.00 Atom::unpack_comm(int, int, double*)
0.13 38.98 0.05 26 0.00 0.00 Atom::pbc()
0.10 39.02 0.04 __intel_ssse3_rep_memcpy
0.08 39.05 0.03 25 0.00 0.00 Atom::sort(Neighbor&)
0.08 39.08 0.03 1 0.03 0.03 create_atoms(Atom&, int, int, int, double)
0.05 39.10 0.02 26 0.00 0.00 Comm::borders(Atom&)
0.00 39.10 0.00 1221559 0.00 0.00 Atom::pack_border(int, double*, int*)
0.00 39.10 0.00 1221559 0.00 0.00 Atom::unpack_border(int, double*)
0.00 39.10 0.00 131072 0.00 0.00 Atom::addatom(double, double, double, double, double, double)
0.00 39.10 0.00 1025 0.00 0.00 Timer::stamp(int)
0.00 39.10 0.00 502 0.00 0.00 Thermo::compute(int, Atom&, Neighbor&, Force*, Timer&, Comm&)
0.00 39.10 0.00 500 0.00 0.00 Timer::stamp()
0.00 39.10 0.00 475 0.00 0.00 Comm::communicate(Atom&)
0.00 39.10 0.00 26 0.00 0.00 Comm::exchange(Atom&)
0.00 39.10 0.00 25 0.00 0.00 Timer::stamp_extra_stop(int)
0.00 39.10 0.00 25 0.00 0.00 Timer::stamp_extra_start()
0.00 39.10 0.00 25 0.00 0.00 Neighbor::binatoms(Atom&, int)
0.00 39.10 0.00 7 0.00 0.00 Timer::barrier_stop(int)
0.00 39.10 0.00 1 0.00 0.00 create_box(Atom&, int, int, int, double)
0.00 39.10 0.00 1 0.00 0.00 create_velocity(double, Atom&, Thermo&)
The output is sorted according to the total time spent in it. The interesting columns are self seconds (the time spent in the routine itself), calls (how often it was called) and self s/call (how much time was spent per call).
How to use perf
Runtime profiling with perf is very simple. You execute your executable wrapped with the perf call:
perf record ./miniMD
After the application finished the results can be analysed with
perf report
which opens a ncurses based presentation of the results:
Samples: 30K of event 'cycles:uppp', Event count (approx.): 20629160088
Overhead Command Shared Object Symbol
64.19% miniMD-ICC miniMD-ICC [.] ForceLJ::compute
31.54% miniMD-ICC miniMD-ICC [.] Neighbor::build
1.47% miniMD-ICC miniMD-ICC [.] Integrate::run
0.67% miniMD-ICC [kernel] [k] irq_return
0.40% miniMD-ICC miniMD-ICC [.] Atom::pack_comm
0.35% mpiexec [kernel] [k] sysret_check
0.21% miniMD-ICC miniMD-ICC [.] create_atoms
0.18% miniMD-ICC miniMD-ICC [.] Atom::unpack_comm
0.15% miniMD-ICC [kernel] [k] sysret_check
0.15% miniMD-ICC miniMD-ICC [.] Comm::borders
0.10% miniMD-ICC miniMD-ICC [.] __intel_ssse3_rep_memcpy
0.09% miniMD-ICC miniMD-ICC [.] Atom::sort
0.07% miniMD-ICC miniMD-ICC [.] Neighbor::binatoms
0.05% mpiexec [kernel] [k] irq_return
0.04% miniMD-ICC miniMD-ICC [.] Atom::pbc
0.03% miniMD-ICC miniMD-ICC [.] Atom::unpack_border
0.03% miniMD-ICC miniMD-ICC [.] Atom::addatom
0.02% miniMD-ICC miniMD-ICC [.] Atom::pack_border
0.02% hydra_pmi_proxy [kernel] [k] sysret_check
0.01% miniMD-ICC miniMD-ICC [.] create_velocity
0.01% mpiexec libc-2.17.so [.] vfprintf
0.01% miniMD-ICC ld-2.17.so [.] _dl_lookup_symbol_x
0.01% miniMD-ICC ld-2.17.so [.] do_lookup_x
0.01% hydra_bstrap_pr [kernel] [k] irq_return
0.01% hydra_pmi_proxy [kernel] [k] irq_return
0.01% hydra_bstrap_pr [kernel] [k] sysret_check
0.01% miniMD-ICC libmpi.so.12.0.0 [.] MPIR_T_CVAR_REGISTER_impl
0.01% miniMD-ICC libc-2.17.so [.] getenv
0.01% miniMD-ICC libmpi.so.12.0.0 [.] MPL_wtime
As can be seen the result is similar but not identical. Perf also reports on time spent in external entities as the kernel, mpiexec and system libraries.
Runtime profiling for parallel applications
gprof as well as perf are designed to be used for single process applications. While there are possibilities to use gprof in a MPI settings (see the LRZ documentation) a lightweight alternative to the more complex to use full trace tools is the command line version of Intel Amplifier. It is also using the builtin perf infrastructure in the Linux kernel.
Example usage with MPI:
mpirun -np 1 amplxe-cl -collect hotspots -result-dir myresults -- ../miniMD-ICC --half_neigh 0
The analysis is printed on stdout:
Elapsed Time: 8.650s
CPU Time: 8.190s
Effective Time: 8.190s
Idle: 0.020s
Poor: 8.170s
Ok: 0s
Ideal: 0s
Over: 0s
Spin Time: 0s
Overhead Time: 0s
Total Thread Count: 2
Paused Time: 0s
Top Hotspots
Function Module CPU Time
--------------------------- ---------- --------
ForceLJ::compute_fullneigh miniMD-ICC 4.940s
Neighbor::build miniMD-ICC 2.820s
Integrate::finalIntegrate miniMD-ICC 0.100s
Integrate::initialIntegrate miniMD-ICC 0.060s
__intel_ssse3_rep_memcpy miniMD-ICC 0.040s
[Others] N/A 0.230s
Effective Physical Core Utilization: 4.9% (0.976 out of 20)
| The metric value is low, which may signal a poor physical CPU cores
| utilization caused by:
| - load imbalance
| - threading runtime overhead
| - contended synchronization
| - thread/process underutilization
| - incorrect affinity that utilizes logical cores instead of physical
| cores
| Explore sub-metrics to estimate the efficiency of MPI and OpenMP parallelism
| or run the Locks and Waits analysis to identify parallel bottlenecks for
| other parallel runtimes.
|
Effective Logical Core Utilization: 2.4% (0.976 out of 40)
| The metric value is low, which may signal a poor logical CPU cores
| utilization. Consider improving physical core utilization as the first
| step and then look at opportunities to utilize logical cores, which in
| some cases can improve processor throughput and overall performance of
| multi-threaded applications.
|
Collection and Platform Info
Application Command Line: ../miniMD-ICC "--half_neigh" "0"
Operating System: 3.10.0-957.10.1.el7.x86_64 NAME="CentOS Linux" VERSION="7 (Core)" ID="centos" ID_LIKE="rhel fedora" VERSION_ID="7" PRETTY_NAME="CentOS Linux 7 (Core)" ANSI_COLOR="0;31" CPE_NAME="cpe:/o:centos:centos:7" HOME_URL="https://www.centos.org/" BUG_REPORT_URL="https://bugs.centos.org/" CENTOS_MANTISBT_PROJECT="CentOS-7" CENTOS_MANTISBT_PROJECT_VERSION="7" REDHAT_SUPPORT_PRODUCT="centos" REDHAT_SUPPORT_PRODUCT_VERSION="7"
Computer Name: e1125
Result Size: 3 MB
Collection start time: 13:11:20 02/04/2019 UTC
Collection stop time: 13:11:29 02/04/2019 UTC
Collector Type: Driverless Perf per-process counting,User-mode sampling and tracing
CPU
Name: Intel(R) Xeon(R) E5/E7 v2 Processor code named Ivytown
Frequency: 2.200 GHz
Logical CPU Count: 40
This was also the only tool that resolved the inlined C++ routine. There are many more collect modules available including hardware performance monitoring metrics. Please refer to the Intel documentation for more detailed information.