AMM: Scalable Memory Reuse Model to Predict the Performance of Physics Codes
Gopinath Chennupati, Nandakishore Santhi, Stephan Eidenbenz, Sunil Thulasidasan · 2017
As the US Department of Energy (DOE) invests in exascale computing, scalable performance modeling of physics codes on CPUs remains a hard challenge in computational codesign due to advanced design features of processors such as the memory hierarchy, instruction pipelining, and speculative execution. Reuse distance is a powerful (but unscalable) characteristic that helps to predict cache hit-rates. We propose, Analytical Memory Model (AMM), a novel hardware model based on cache memory hierarchies. AMM efficiently computes close approximations of reuse distance distributions through a combination of static analysis of basic code blocks and sampling from very small code instances. The results show that AMM accurately predicts reuse profiles of scientific mini-applications (for example, matrix multiplication). Coupling AMM with the Performance Prediction Toolkit (PPT), we further show a scalable runtime prediction of scientific codes on Intel Xeon.