Workflow for Profiling with mpiP

This page is work in progress!!!

Introduction

This page describes a basic workflow for performance analysis based on mpiP. The best-practises 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 mpiP,
4. obtaining profiles,
5. determining instrumentation overhead,
6. quick efficiency metrics.,

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 mpip

Setting up a suitable use-case

In contrast to full tracing, profiling will in general not produce such huge amounts of performance analysis data. It is therefore in most cases not necessary to tailor use-cases for low amounts of performance trace data. Often, profiling can be done on the same configuration as production runs. In many cases however, users will still want to generate special use-cases of short duration for profiling in order to save compute resources.

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.

On the other hand, the number of timesteps/iterations should not be too small. For profiling in particular, it is important to make sure, that the total execution time is dominated by the main computational loop while the initialisation and shutdown phases take only a small fraction of the execution time. As a rule of thumb, aim for less than 5% init and shutdown phase where possible.

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.