HPC User: Key Points

Introduction

The HPC User lesson will help to understand basic concepts affecting performance in programming.

The time function can always be used externally to measure performance but has limitted accuracy of around 1 millisecond.
Internally there are precise clock routines that can be used to measure the performance of each part of a code. These are different for each programming language, but the use is always the same.
Scaling studies can help determine how many cores or nodes we can efficiently use for a parallel job of a given problem size.
Measuring memory use can be done from outside a program, but you may also be able to calculate total memory for simple programs.

A computer’s view of code is more complex than a user’s view.
It’s important to understand a little about what goes on when code actually runs, but you don’t need to be able to program at that level.
Whenever possible, use code written and optimized by experts instead of writing your own version.

Multi-threaded computing is powerful and fairly easy to use but only works on one compute node.
Understand key factors that can limit the efficient scaling of multi-threaded programs.

Distributed-memory computing is very flexible, extremely scalable, but more difficult to program.
Understand key factors that can limit the efficient scaling of message-passing programs.

Performance is just one criteria we need to understand when choosing the best language for a given project.

Array jobs provide an easy way submit and manage large numbers of similar jobs.
Array jobs are another way to do parallel computing, but by running lots of small jobs individually.
Test your script carefully on a few array IDs before submitting the full job.