An Empirical Performance Evaluation of Scalable Scientific Applications

Abstract

We investigate the scalability, architectural requirements,a nd performance characteristics of eight scalable scientific applications. Our analysis is driven by empirical measurements using statistical and tracing instrumentation for both communication and computation. Based on these measurements, we refine our analysis into precise explanations of the factors that influence performance and scalability for each application; we distill these factors into common traits and overall recommendations for both users and designers of scalable platforms. Our experiments demonstrate that some traits, such as improvements in the scaling and performance of MPI's collective operations, will benefit most applications. We also find specific characteristics of some applications that limit performance. For example, one application's intensive use of a 64-bit, floating-point divide instruction, which has high latency and is not pipelined on the POWER3, limits the performance of the application's primary computation.