Developing a Parameterized Performance Proxy for Sequential Scientific Kernels

A simple, synthetic performance proxy for scientific applications is of great interest to the scientific computing community for the development of new products, procurements, and performance related questions in general. To develop such a performance proxy, we enhance the capability of the memory performance benchmark, Apex-MAP, by adding new concepts to capture the effects of computational details and programming styles. We test the fidelity of using Apex-MAP as a performance proxy with five sequential kernels on three different platforms with five inputs each. The relative performance difference between the kernels and Apex-MAP configured with corresponding parameters is generally within 10%. The quality of prediction measured by the coefficient of determination R^2 is over 98% for most cases. We also discuss experiences we gained during this study about how to improve the current version of Apex-MAP without affecting its basic concepts and designs so that it can reliably be used across platforms.