Modeling And Predicting Performance Of High Performance Computing Applications On Hardware Accelerators

Hybrid-core systems speedup applications by offloading certain compute operations that can run faster on hardware accelerators. However, such systems require significant programming and porting effort to gain a performance benefit from the accelerators. Therefore, prior to porting it is prudent to investigate the predicted performance benefit of accelerators for a given workload. To address this problem we present a performance-modeling framework that predicts the application performance rapidly and accurately for hybrid-core systems. We present predictions for two full-scale HPC applications-HYCOM and Milc. Our results for two accelerators (GPU and FPGA) show that gather/scatter and stream operations can speedup by as much as a factor of 15 and overall compute time of Milc and HYCOM improve by 3.4% and 20%, respectively. We also show that in order to benefit from the accelerators, 70% of the latency of data transfer time between the CPU and the accelerators needs to be overcome.