University of Florence
The GPUs are emerging as a general-purpose high-performance computing device. Growing GPGPU research has made numerous GPGPU workloads available. However, a systematic approach to characterize these benchmarks and analyze their implication on GPU microarchitecture design evaluation is still lacking. In this paper, the authors propose a set of microarchitecture agnostic GPGPU workload characteristics to represent them in a microarchitecture independent space. Correlated dimensionality reduction process and clustering analysis are used to understand these workloads. In addition, they propose a set of evaluation metrics to accurately evaluate the GPGPU design space.