Profiling
There are a number of different ways to access profiling data on ARCHER2. In this section, we discuss the HPE Cray profiling tools, CrayPat-lite and CrayPat. We also show how to get usage data on currently running jobs from Slurm batch system.
You can also use the Linaro Forge tool to profile applications on ARCHER2.
If you are specifically interested in profiling IO, then you may want to look at the Darshan IO profiling tool.
CrayPat-lite
CrayPat-lite is a simplified and easy-to-use version of the Cray Performance Measurement and Analysis Tool (CrayPat). CrayPat-lite provides basic performance analysis information automatically, with a minimum of user interaction, and yet offers information useful to users wishing to explore a program's behaviour further using the full CrayPat suite.
How to use CrayPat-lite
-
Ensure the
perftools-basemodule is loaded.module list -
Load the
perftools-litemodule.module load perftools-lite -
Compile your application normally. An informational message from CrayPat-lite will appear indicating that the executable has been instrumented.
cc -h std=c99 -o myapplication.x myapplication.cINFO: creating the CrayPat-instrumented executable 'myapplication.x' (lite-samples) ...OK -
Run the generated executable normally by submitting a job.
#!/bin/bash #SBATCH --job-name=CrayPat_test #SBATCH --nodes=4 #SBATCH --ntasks-per-node=128 #SBATCH --cpus-per-task=1 #SBATCH --time=00:20:00 #SBATCH --account=[budget code] #SBATCH --partition=standard #SBATCH --qos=standard export OMP_NUM_THREADS=1 export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK # Launch the parallel program srun --hint=nomultithread --distribution=block:block mpi_test.x -
Analyse the data.
After the job finishes executing, CrayPat-lite output should be printed to stdout (i.e. at the end of the job's output file). A new directory will also be created containing
.rptand.ap2files. The.rptfiles are text files that contain the same information printed in the job's output file and the.ap2files can be used to obtain more detailed information, which can be visualized using the Cray Apprentice2 tool.
Further help
CrayPat
The Cray Performance Analysis Tool (CrayPat) is a powerful framework for analysing a parallel application’s performance on Cray supercomputers. It can provide very detailed information about the timing and performance of individual application procedures.
CrayPat can perform two types of performance analysis, sampling experiments and tracing experiments. A sampling experiment probes the code at a predefined interval and produces a report based on the data collected. A tracing experiment explicitly monitors the code performance within named routines. Typically, the overhead associated with a tracing experiment is higher than that associated with a sampling experiment but provides much more detailed information. The key to getting useful data out of a sampling experiment is to run your profiling for a representative length of time.
Sampling analysis
-
Ensure the
perftools-basemodule is loaded.module list -
Load
perftoolsmodule.module load perftools -
Compile your code in the standard way always using the Cray compiler wrappers (ftn, cc and CC). Object files need to be made available to CrayPat to correctly build an instrumented executable for profiling or tracing, this means that the compile and link stage should be separated by using the
-ccompile flag.auser@ln01:/work/t01/t01/auser> cc -h std=c99 -c jacobi.c auser@ln01:/work/t01/t01/auser> cc jacobi.o -o jacobi -
To instrument the binary, run the
pat_buildcommand. This will generate a new binary with+patappended to the end (e.g.jacobi+pat).auser@ln:/work/t01/t01/auser> pat_build jacobi -
Run the new executable with
+patappended as you would with the regular executable. Each run will produce its own 'experiment directory' containing the performance data as.xffiles inside a subdirectory calledxf-files(e.g. running thejacobi+patinstrumented executable might producejacobi+pat+12265-1573s/xf-files). - Generate report data with
pat_report.
The .xf files contain the raw sampling data from the run and need to
be post-processed to produce useful results. This is done using the
pat_report tool which converts all the raw data into a summarised and
readable form. You should provide the name of the experiment directory as
the argument to pat_report.
auser@ln:/work/t01/t01/auser> pat_report jacobi+pat+12265-1573s
Table 1: Profile by Function (limited entries shown)
Samp% | Samp | Imb. | Imb. | Group
| | Samp | Samp% | Function
| | | | PE=HIDE
100.0% | 849.5 | -- | -- | Total
|--------------------------------------------------
| 56.7% | 481.4 | -- | -- | MPI
||-------------------------------------------------
|| 48.7% | 414.1 | 50.9 | 11.0% | MPI_Allreduce
|| 4.4% | 37.5 | 118.5 | 76.6% | MPI_Waitall
|| 3.0% | 25.2 | 44.8 | 64.5% | MPI_Isend
||=================================================
| 29.9% | 253.9 | 55.1 | 18.0% | USER
||-------------------------------------------------
|| 29.9% | 253.9 | 55.1 | 18.0% | main
||=================================================
| 13.4% | 114.1 | -- | -- | ETC
||-------------------------------------------------
|| 13.4% | 113.9 | 26.1 | 18.8% | __cray_memcpy_SNB
|==================================================
This report will generate more files with the extension .ap2 in the
experiment directory. These hold the same data as the .xf files but
in the post-processed form. Another file produced has an .apa extension
and is a text file with a suggested configuration for generating a
traced experiment.
The .ap2 files generated are used to view performance
data graphically with the Cray Apprentice2 tool.
The pat_report command is able to produce many different profile
reports from the profiling data. You can select a predefined report with
the -O flag to pat_report. A selection of the most generally useful
predefined report types are:= listed below.
- ca+src - Show the callers (bottom-up view) leading to the routines that have a high use in the report and include source code line numbers for the calls and time-consuming statements.
- load_balance - Show load-balance statistics for the high-use routines in the program. Parallel processes with minimum, maximum and median times for routines will be displayed. Only available with tracing experiments.
- mpi_callers - Show MPI message statistics. Only available with tracing experiments.
Example output:
auser@ln01:/work/t01/t01/auser> pat_report -O ca+src,load_balance jacobi+pat+12265-1573s
Table 1: Profile by Function and Callers, with Line Numbers (limited entries shown)
Samp% | Samp | Imb. | Imb. | Group
| | Samp | Samp% | Function
| | | | PE=HIDE
100.0% | 849.5 | -- | -- | Total
|--------------------------------------------------
|--------------------------------------
| 56.7% | 481.4 | MPI
||-------------------------------------
|| 48.7% | 414.1 | MPI_Allreduce
3| | | main:jacobi.c:line.80
|| 4.4% | 37.5 | MPI_Waitall
3| | | main:jacobi.c:line.73
|| 3.0% | 25.2 | MPI_Isend
|||------------------------------------
3|| 1.6% | 13.2 | main:jacobi.c:line.65
3|| 1.4% | 12.0 | main:jacobi.c:line.69
||=====================================
| 29.9% | 253.9 | USER
||-------------------------------------
|| 29.9% | 253.9 | main
|||------------------------------------
3|| 18.7% | 159.0 | main:jacobi.c:line.76
3|| 9.1% | 76.9 | main:jacobi.c:line.84
|||====================================
||=====================================
| 13.4% | 114.1 | ETC
||-------------------------------------
|| 13.4% | 113.9 | __cray_memcpy_SNB
3| | | __cray_memcpy_SNB
|======================================
Tracing analysis
Automatic Program Analysis (APA)
We can produce a focused tracing experiment based on the results from
the sampling experiment using pat_build with the .apa file
produced during the sampling.
auser@ln01:/work/t01/t01/auser> pat_build -O jacobi+pat+12265-1573s/build-options.apa
This will produce a third binary with extension +apa. This binary
should once again be run on the compute nodes and the name of the
executable changed to jacobi+apa. As with the sampling analysis, a
report can be produced using pat_report. For example:
auser@ln01:/work/t01/t01/auser> pat_report jacobi+apa+13955-1573t
Table 1: Profile by Function Group and Function (limited entries shown)
Time% | Time | Imb. | Imb. | Calls | Group
| | Time | Time% | | Function
| | | | | PE=HIDE
100.0% | 12.987762 | -- | -- | 1,387,544.9 | Total
|-------------------------------------------------------------------------
| 44.9% | 5.831320 | -- | -- | 2.0 | USER
||------------------------------------------------------------------------
|| 44.9% | 5.831229 | 0.398671 | 6.4% | 1.0 | main
||========================================================================
| 29.2% | 3.789904 | -- | -- | 199,111.0 | MPI_SYNC
||------------------------------------------------------------------------
|| 29.2% | 3.789115 | 1.792050 | 47.3% | 199,109.0 | MPI_Allreduce(sync)
||========================================================================
| 25.9% | 3.366537 | -- | -- | 1,188,431.9 | MPI
||------------------------------------------------------------------------
|| 18.0% | 2.334765 | 0.164646 | 6.6% | 199,109.0 | MPI_Allreduce
|| 3.7% | 0.486714 | 0.882654 | 65.0% | 199,108.0 | MPI_Waitall
|| 3.3% | 0.428731 | 0.557342 | 57.0% | 395,104.9 | MPI_Isend
|=========================================================================
Manual Program Analysis
CrayPat allows you to manually choose your profiling preference. This is particularly useful if the APA mode does not meet your tracing analysis requirements.
The entire program can be traced as a whole using -w:
auser@ln01:/work/t01/t01/auser> pat_build -w jacobi
Using -g, a program can be instrumented to trace all function entry
point references belonging to the trace function group (mpi, libsci,
lapack, scalapack, heap, etc):
auser@ln01:/work/t01/t01/auser> pat_build -w -g mpi jacobi
Dynamically-linked binaries
CrayPat allows you to profile un-instrumented, dynamically linked
binaries with the pat_run utility. pat_run delivers profiling
information for codes that cannot easily be rebuilt. To use pat_run:
-
Load the
perftools-basemodule if it is not already loaded.module load perftools-base -
Run your application normally including the
pat_runcommand right after yoursrunoptions.srun [srun-options] pat_run [pat_run-options] program [program-options] -
Use
pat_reportto examine any data collected during the execution of your application.auser@ln01:/work/t01/t01/auser> pat_report jacobi+pat+12265-1573s
Some useful pat_run options are as follows.
-wCollect data by tracing.-gTrace functions belonging to group names. See the -g option in pat_build(1) for a list of valid tracegroup values.-rGenerate a text report upon successful execution.
Further help
Cray Apprentice2
Cray Apprentice2 is an optional GUI tool that is used to visualize and manipulate the performance analysis data captured during program execution. Cray Apprentice2 can display a wide variety of reports and graphs, depending on the type of program being analyzed, the way in which the program was instrumented for data capture, and the data that was collected during program execution.
You will need to use CrayPat to first instrument your program and
capture performance analysis data, and then pat_report to generate
the .ap2 files from the results. You may then use Cray Apprentice2 to
visualize and explore those files.
The number and appearance of the reports that can be generated using Cray Apprentice2 is determined by the kind and quantity of data captured during program execution, which in turn is determined by the way in which the program was instrumented and the environment variables in effect at the time of program execution. For example, changing the PAT_RT_SUMMARY environment variable to 0 before executing the instrumented program nearly doubles the number of reports available when analyzing the resulting data in Cray Apprentice2.
export PAT_RT_SUMMARY=0
To use Cray Apprentice2 (app2), load perftools-base module if it is
not already loaded.
module load perftools-base
Next, open the experiment directory generated during the instrumentation phase with Apprentice2.
auser@ln01:/work/t01/t01/auser> app2 jacobi+pat+12265-1573s
Hardware Performance Counters
Hardware performance counters can be used to monitor CPU and power events on ARCHER2 compute nodes. The monitoring and reporting of hardware counter events is integrated with CrayPat - users should use CrayPat as described earlier in this section to run profiling experiments to gather data from hardware counter events and to analyse the data.
Counters and counter groups available
You can explore which event counters are available on compute nodes by running the following
commands (replace t01 with a valid budget code for your account):
module load perftools
srun --ntasks=1 --partition=standard --qos=short --account=t01 papi_avail
For convenience, the CrayPat tool provides predetermined groups of hardware event counters. You can get more information on the hardware event counters available through CrayPat with the following commands (on a login or compute node):
module load perftools
pat_help counters rome groups
If you want information on which hardware event counters are included in a group
you can type the group name at the prompt you get after running the command above.
Once you have finished browsing the help, type . to quit back to the command line.
Power/energy counters available
You can also access counters on power/energy consumption. To list the counters available
to monitor power/energy use you can use the command (replace t01 with a valid budget
code for your account):
module load perftools
srun --ntasks=1 --partition=standard --qos=short --account=t01 papi_native_avail -i cray_pm
Enabling hardware counter data collection
You enable the collection of hardware event counter data as part of a CrayPat
experiment by setting the environment variable PAT_RT_PERFCTR to a comma separated
list of the groups/counters that you wish to measure.
For example, you could set (usually in your job submission script):
export PAT_RT_PERFCTR=1
to use the 1 counter group (summary with branch activity).
Analysing hardware counter data
If you enabled collection of hardware event counters when running your profiling
experiment, you will automatically get a report on the data when you use the
pat_report command to analyse the profile experiment data file.
You will see information similar to the following in the output from CrayPat for
different sections of your code (this example if for the case where
export PAT_RT_PERFCTR=1, counter group: summary with branch activity, was
set in the job submission script):
==============================================================================
USER / main
------------------------------------------------------------------------------
Time% 88.3%
Time 446.113787 secs
Imb. Time 33.094417 secs
Imb. Time% 6.9%
Calls 0.002 /sec 1.0 calls
PAPI_BR_TKN 0.240G/sec 106,855,535,005.863 branch
PAPI_TOT_INS 5.679G/sec 2,533,386,435,314.367 instr
PAPI_BR_INS 0.509G/sec 227,125,246,394.008 branch
PAPI_TOT_CYC 1,243,344,265,012.828 cycles
Instr per cycle 2.04 inst/cycle
MIPS 1,453,770.20M/sec
Average Time per Call 446.113787 secs
CrayPat Overhead : Time 0.2%
Using the CrayPAT API to gather hardware counter data
The CrayPAT API features a particular function, PAT_counters, that allows you to
obtain the values of specific hardware counters at specific points within your code.
For convenience, we have developed an MPI-based wrapper for this aspect of the CrayPAT
API, called pat_mpi_lib, which can be found via the link below.
https://github.com/cresta-eu/pat_mpi_lib
The PAT MPI Library makes it possible to monitor a user-defined set of hardware performance
counters during the execution of an MPI code running across multiple compute nodes. The
library is lightweight, containing just four functions, and is intended to be straightforward
to use. Once you've defined the hooks in your code for recording counter values, you can
control which counters are read at runtime by setting the PAT_RT_PERFCTR environment
variable in the job submission script. As your code executes, the defined set of counters
will be read at various points. After each reading, the counter values are summed by rank 0
(via an MPI reduction) before being output to a log file.
Further information along with test harnesses and example scripts can be found by reading the PAT MPI Library readme file.
More information on hardware counters
More information on using hardware counters can be found in the appropriate section of the HPE documentation:
Also available are two MPI-based wrapper libraries, one for Power Management (PM) counters that cover such properties as point-in-time power, cumulative energy use and temperature; and one that provides access to PAPI counters. See the links below for further details.
Performance and profiling data in Slurm
Slurm commands on the login nodes can be used to quickly and simply retrieve information about memory usage for currently running and completed jobs.
There are three commands you can use on ARCHER2 to query job data from Slurm, two are standard Slurm commands and one is a script that provides information on running jobs:
- The
sstatcommand is used to display status information of a running job or job step - The
sacctcommand is used to display accounting data for all finished jobs and job steps within the Slurm job database. - The
archer2jobloadcommand is used to show CPU and memory usage information for running jobs. (This script is based on one originally written for the COSMA HPC facility at the University of Durham.)
We provide examples of the use of these three commands below.
For the sacct and sstat command, the memory properties we print out below are:
AveRSS- The mean memory use per process over the length of the jobMaxRSS- The maximum memory use by an individual process measured during the jobMaxRSSTask- The process ID associated with the maximum memory use measured during the jobMaxRSSNode- The node ID associated with the maximum memory use measured during the jobTRESUsageInTot- Totals of various properties for the job. For example, the total memory use of the job is available in themem=property
Tip
Slurm polls for the memory use in a job, this means that short-term changes in memory use may not be captured in the Slurm data.
Example 1: sstat for running jobs
To display the current memory use of a running job with the ID 123456:
sstat --format=JobID,AveCPU,AveRSS,MaxRSS,MaxRSSTask,MaxRSSNode,TRESUsageInTot%150 -j 123456
Example 2: sacct for finished jobs
To display the memory use of a completed job with the ID 123456:
sacct --format=JobID,JobName,AveRSS,MaxRSS,MaxRSSTask,MaxRSSNode,TRESUsageInTot%150 -j 123456
Another usage of sacct is to display when a job was submitted, started running and ended for a particular user:
sacct --format=JobID,Submit,Start,End -u auser
Example 3: archer2jobload for running jobs
Using the archer2jobload command on its own with no options will show the current
CPU and memory use across compute nodes for all running jobs.
More usefully, you can provide a job ID to archer2jobload and it will show a summary
of the CPU and memory use for a specific job. For example, to get the usage data for job
123456, you would use:
auser@ln01:~> archer2jobload 123456
# JOB: 123456
CPU_LOAD MEMORY ALLOCMEM FREE_MEM TMP_DISK NODELIST
127.35-127.86 256000 239872 169686-208172 0 nid[001481,001638-00
This shows the minimum CPU load on a compute node is 126.04 (close to the limit of 128 cores) with the maximum load 127.41 (indicating all the nodes are being used evenly). The minimum free memory is 171893 MB and the maximum free memory is 177224 MB.
If you add the -l option, you will see a breakdown per node:
auser@ln01:~> archer2jobload -l 276236
# JOB: 123456
NODELIST CPU_LOAD MEMORY ALLOCMEM FREE_MEM TMP_DISK
nid001481 127.86 256000 239872 169686 0
nid001638 127.60 256000 239872 171060 0
nid001639 127.64 256000 239872 171253 0
nid001677 127.85 256000 239872 173820 0
nid001678 127.75 256000 239872 173170 0
nid001891 127.63 256000 239872 173316 0
nid001921 127.65 256000 239872 207562 0
nid001922 127.35 256000 239872 208172 0
Further help with Slurm
The definitions of any variables discussed here and more usage information can be found in the man pages of sstat and sacct.
AMD μProf
The AMD μProf tool provides capabilities for low-level profiling on AMD processors, see:
Linaro Forge
The Linaro Forge tool also provides profiling capabilities. See:
Darshan IO profiling
The Darshan lightweight IO profiling tool provides a quick way to profile the IO part of your software: