Performance prediction and procurement in practice: Assessing the suitability of commodity cluster components for wavefront codes

The cost of state-of-the-art supercomputing resources makes each individual purchase a length and expensive process. Often each candidate architecture will need to be benchmarked using a variety of tools to assess likely performance. However, benchmarking alone only provides a limited insight into the suitability of each architecture for key codes and will give potentially misleading results when assessing their scalability. In this study the authors present a case study of the application of recently developed performance models of the Chimaera benchmarking code written by the United Kingdom Atomic Weapons Establishment (AWE), with a view to analysing how the code will perform and scale on a medium sized, commodity-based InfiniBand cluster. The models are validated and demonstrate a greater than 90 accuracy for an existing InfiniBand machine; the models are then used as the basis for predicting code performance on a variety of alternative hardware configurations which include changes in the underlying network, the use of faster processors and the use of a higher core density per processor. The results demonstrate the compute-bound nature of Chimaera and its sensitivity to network latency at increased processor counts. By using these insights the authors are able to discuss potential strategies which may be employed during the procurement of future mid-range clusters for wavefront-rich workloads.