OpenMP

Or why debugging is hard and parallel debugging doubly so

Debugging programs is hard. I give a lecture on debugging for the Programming Skills module of EPCC's MScs in HPC and HPC with Data Science where we try to point out common programming mistakes, programming strategies for making bugs less likely, and the skills and tools required for investigating, identifying, and fixing bugs.

It's not often that the internecine rivalries of the HPC research and development community spill over into the public arena. However, a video recently posted on YouTube (and the associated comments), ostensibly a light-hearted advert for a SC15 tutorial on heterogenous programming, shows how real and deep these rivalries can be.

Do you use scientific codes in your research? Are the things you can do with it limited by the execution time? The code has been parallelised but does not scale well? How should you go about improving the performance? What can you do when you do not have full understanding of the code? There are some general steps that can be taken to improve the performance of parallelised codes. In this article I will describe briefly the process I have undertaken to optimise the parallel performance of a computational chemistry package, TINKER, as part of the EPCC/SSI APES project.

17-18 July 2013
EPCC, The University of Edinburgh

OpenMP is the industry standard for shared-memory programming, which enables serial programs to be parallelised using compiler directives.

This two-day course is aimed at programmers seeking to deepen their understanding of OpenMP and explore some of its more recent and advanced features.