Posted: 3 Jul 2014 | 11:42
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.
Posted: 23 Apr 2013 | 10:23
The picture of a great ape cousin hoarding food at Edinburgh Zoo is deliberately misleading! The "APES" acronym (pronounced "A-PES") actually stands for Advanced Potential Energy Surfaces, and refers to a new project that EPCC is involved in. The project in question is an NSF-EPSRC funded US-UK collaboration that aims to incorporate APES into a range of computational chemistry packages. EPCC's main contribution will be to parallelise software to take advantage of the large-scale compute resources offered by supercomputing clusters such as HECToR and its upcoming successor, ARCHER, as well as NFS-provided resources in the US. This should equip researchers with better tools to advance their understanding of the structure and function of molecules such as, hypothetically, the smell molecule isoamyl acetate (shown), which interacts with simian olfactory receptors to give bananas their irresistible allure.