Characterization and cross-platform analysis of high-throughput accelerators
Keitaro Oka, Wenhao Jia, Margaret Martonosi, Koji Inoue · 2015
Today's computer systems often employ high-throughput accelerators (such as Intel Xeon Phi coprocessors and NVIDIA Tesla GPUs) to improve the performance of some applications or portions of applications. While such accelerators are useful for suitable applications, it remains challenging to predict which workloads will run well on these platforms and to predict the resulting performance trends for varying input. This paper provides detailed characterizations on such platforms across a range of programs and input sizes. Furthermore, we show opportunities for cross-platform performance analysis and comparison between Xeon Phi and Tesla. Our crossplatform comparison has three steps. First, we build Xeon Phi performance regression models as a function of important Xeon Phi performance counters to identify critical architectural resources that highly affect a benchmark's performance. Then, cross-platform Tesla performance regression models are built to relate the Tesla performance trends of the benchmark to the Xeon Phi performance counter measurements of the benchmark. Finally, we compare the counters most important for Xeon Phi models to those most important for Tesla's models; this reveals similarities and distinctions of dynamic application behaviors on the two platforms.