Workflow for Profiling with Extrae and Paraver

This page is work in progress!!!

Introduction

This page describes a basic workflow for performance analysis based on Extrae and Paraver. The best-practised presented here are tailored to HLRS' Hawk system.

More specifically, we describe steps and commands necessary for

1. setting up a suitable use-case,
2. determining the non-instrumented performance,
3. configuration of Extrae,
4. obtaining traces,
5. determining instrumentation overhead,
6. quick efficiency metrics,
7. trace visualization with Paraver.

If you get stuck or need further explanation, please get in touch with HLRS user support.

On Hawk load the required modules with

$ module load extrae bsc_tools

Setting up a suitable use-case

Tracing can produce a huge amount of performance analysis data. Typically, when doing tracing it is sufficient to run your code for a few timesteps/iterations only. In most cases, it is good practise to run the code between 1 and 10 minutes.

However, the performance characteristics of a code depend critically on the scale, i.e. number of cores used, and the problem size. Try to keep you performance analysis use-case as close as possible to a realistic use-case of your interest. Where practical, reduce the execution time (and thus the tracing data volume) by reducing the amount of timesteps/iterations, not by reducing the problem size.

Determine the non-instrumented performance

Running your application under the control of a performance analysis tool can incur significant overhead, i.e. your code will take noticeably longer to execute. At the same time, such overhead will have an impact on the quality of your performance analysis and the robustness of your conclusions. Always be aware of the amount of overhead and try to keep it small where possible. In many cases it is possible to reduce the overhead below 5% of the execution time, which is the same order of magnitude of expected performance variability between runs. If your overhead is larger, be aware that performance metrics may be off by at least as much.

It is therefore important to measure the performance of your code for the particular use-case before applying any performance analysis tools. We refer to this as non-instrumented performance.

At the very least you should determine the elapsed time of run. Do for instance

$ time mpirun ... ./app

and record the "User time" portion of the output.

Many codes keep track of an application-specific performance metric, such as for instance iterations per second, or similar. Often, this a better than the raw elapsed time, as it will disregard initialisation and shutdown phases which are negligible for longer production runs, but not for short analysis use-cases. If your code reports such a metric, record this as well in addition to the elapsed time. You may consider adding an application-specific metric to your code, if not available yet.

Consider doing not just one run, but several to get a feeling for the variation of the non-instrumented performance across runs.

Configuration of Extrae

Extrae is a library which is able to record a wide range of relevant performance metrics. It is configured through an XML configuration file. At HLRS we have prepared a template which should be OK for most users, at least initially. Let's have a look at it:

$ cat $EXTRAE_HOME/../share/extrae_detail.xml

This template is set up to record events related to MPI, OpenMP and some useful hardware counters. It does not record events related to Pthreads, memory usage, call stack information, etc. If you need any of those, take a copy of the template into your working directory. If the defaults are fine, you will not need a copy of the configuration file.

Obtaining traces

Extrae does not need instrumentation of source code. It attaches to the binary through LD_PRELOADing. Usually, this is done by a tracing wrapper script. Again, we have prepared a template for the wrapper script which should be sufficient for most users. The wrapper script is located at:

$ cat $EXTRAE_HOME/../share/trace_extrae.sh

Again, most user will not have to change or even copy it.

To obtain traces, you just need to place the wrapper script in front of your application binary. For instance, suppose your jobs script is:

time mpirun -n XX ... ./app app_arg1 app_arg2

just replace this with

$ module load extrae
time mpirun -n XX ... $EXTRAE_HOME/../share/trace_extrae.sh ./app app_arg1 app_arg2

Note, that you need to load the extrae module in your job script.

After running your job, you will find a few files and directories in your working directory

$ ls -ld TRACE.* set-?/

These contain intermediate trace files, which need to be merged with the command

$ mpi2prv -f TRACE.mpits -o app_my_trace.prv

Make sure to replace app_my_trace with a meaningful name for the resulting trace. If you used the wrapper script above, it will suggest to use the name of the binary plus a timestamp. Feel free to any other.