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Profilers (gprof)

In software engineering, profiling is the investigation of a program's behavior using information gathered as the program executes. The usual purpose of this analisys is to determine which sections of a program to optimize - to increase its overall speed, decrease its memory requirement or sometimes both.

A (code) profiler is a performance analisys tool that, most commonly, measures only the frequency and duration of function calls, but there are other specific types of profilers (e.g. memory profilers) in addition to more comprehensive profilers, capable of gathering extensive performance data.

gprof

The GNU profiler gprof is a useful tool for measuring the performance of a program. It records the number of calls to each function and the amount of time spent there, on a per-function basis. Functions which consume a large fraction of the run-time can be identified easily from the output of gprof. Efforts to speed up a program should concentrate first on those functions which dominate the total run-time.

gprof uses data collected by the -pg compiler flag to construct a text display of the functions within your application (call tree and CPU time spent in every subroutine). It also provides quick access to the profiled data, which let you identify the functions that are the most CPU-intensive. The text display also lets you manipulate the display in order to focus on the application's critical areas.

Usage:

>  gfortran -pg -O3 -o myexec myprog.f90
> ./myexec
> ls -ltr
   .......
   -rw-r--r-- 1 aer0 cineca-staff    506 Apr  6 15:33 gmon.out
> gprof myexec gmon.out

It is also possible to profile at code line-level (see "man gprof" for other options). In this case you must use also the “-g” flag at compilation time:

>  gfortran -pg -g -O3 -o myexec myprog.f90
> ./myexec
> ls -ltr
   .......
   -rw-r--r-- 1 aer0 cineca-staff    506 Apr  6 15:33 gmon.out
> gprof -annotated-source myexec gmon.out

It is possilbe to profile MPI programs. In this case the environment variable GMON_OUT_PREFIX must be defined in order to allow to each task to write a different statistical file. Setting

export GMON_OUT_PREFIX=<name>

 once the run is finished each task will create a file with its process ID (PID) extension

<name>.$PID

 If the environmental variable is not set every task will write the same gmon.out file.

Intel VTUNE Amplifier

Intel performance analisys toolkit that can be used to identify bottlenecks in an application. The tool can be used to perform different types of analisys.

To start the analisys use the command line interface:

module load autoload vtune
amplxe-cl -collect hotspots -r <application_path> <vtune_options> <application>

To explore the results of the analisys performed use the Intel VTune Amplifier GUI:

amplxe-gui <application_path>

Please note that to get a correct result in terms of CPU time it is suggested to use of the advanced-hotspots analisys on both Marconi KNL and SKL partitions. For example:

amplxe-cl -collect advanced-hotspots --target-duration-type veryshort <executable>

Scientific libraries

MKL

In order to exploit the hardware event-based sampling (EBS) you need to request the "vtune" feature via the following directive:

#SBATCH -C vtune

In the EBS mode the Intel VTune Profiler profiles your application using the counter overflow feature of the Performance Monitoring Unit (PMU). The data collector interrupts a process and captures the IP of the interrupted process at the time of the interrupt. Statistically collected IPs of active processes enable the viewer to show statistically important code regions that affect software performance.

Scientific libraries

MKL

The Intel Math Kernel Library (Intel MKL) enables improving performance of The Intel Math Kernel Library (Intel MKL) enables improving performance of scientific, engineering, and financial software that solves large computational problems. Intel MKL provides a set of linear algebra routines, fast Fourier transforms, as well as vectorized math and random number generation functions, all optimized for the latest Intel processors, including processors with multiple cores.

Intel MKL is thread-safe and extensively threaded using the OpenMP technology

documentation can be found in:

${MKLROOT}/../Documentation/en_US/mklexamples

To use the MKL in your code you to load the module, then to define includes and libraries at compile and linking time:

> module load mkl
> icc -I$MKL_INC -L$MKL_LIB  -lmkl_intel_lp64 -lmkl_core -lmkl_sequential

For more inormation , please refer to the documentationIntel oneAPI Math Kernel Library Link Line Advisor.