Recently, GPGPUs have positioned themselves in the mainstream processor arena with their potential to perform a massive number of jobs in parallel. At the same time, many GPGPU benchmark suites have been proposed to evaluate the performance of GPGPUs. Both academia and industry have been introducing new sets of benchmarks each year while some already published benchmarks have been updated periodically. However, some benchmark suites contain benchmarks that are duplicates of each other or use the same underlying algorithm. This results in an excess of workloads in the same performance spectrum. In this paper, we provide a methodology to obtain a set of new GPGPU benchmarks that are located in the unexplored region of the performance spectrum. Our proposal uses statistical methods to understand the performance spectrum coverage and uniqueness of existing benchmark suites. Later we show techniques to identify areas that are not explored by existing benchmarks by visually showing the performance spectrum coverage. Finding unique key metrics for future benchmarks to broaden its performance spectrum coverage is also explored using hierarchical clustering and ranking by Hotel ling's T2 method. Finally, key metrics are categorized into GPGPU performance related components to show how future benchmarks can stress each of the categorized metrics to distinguish themselves in the performance spectrum. Our methodology can serve as a performance spectrum oriented guidebook for designing future GPGPU benchmarks.
['Jee Ho Ryoo', 'Saddam Quirem', 'Michael LeBeane', 'Reena Panda', 'Shuang Song', 'Lizy Kurian John']
GPGPU Benchmark Suites: How Well Do They Sample the Performance Spectrum?