On Power-Performance Characterization of Concurrent Throughput Kernels

Abstract

Growing deployment of power and energy efficient throughput accelerators (GPU) in data centers pushes the envelope of power-performance co-optimization capabilities of GPUs. Realization of exascale computing using accelerators demands further improvements in power efficiency. With hardwired kernel concurrency enablement in accelerators, inter- and intra-workload simultaneous kernels computation predicts increased throughput at lower energy budget. To improve Performance-per-Watt metric of the architectures, a systematic empirical study of real-world throughput workloads (with simultaneous kernel execution) is required. To this end, we propose a multi-kernel throughput workload generation framework that will facilitate aggressive energy and performance management of exascale data centers and will stimulate synergistic power-performance co-optimization of throughput architectures.