Evaluating Power and Energy Efficiency of Bitonic Mergesort on Graphics Processing Unit

Abstract

Excessive power consumption is expected to be the major obstacle to achieve exascale performance within a reasonable power budget in the upcoming years. In addition, graphics processing units (GPUs) are expected to become a significant ingredient in the pursuit of exascale computing due to their fine-grained, highly parallel architecture and advancements in performance and power efficiency. To address the power obstacle of exascale systems, we suggest evaluating power and energy consumption of the fundamental software building blocks. We experimentally investigate power consumption, energy consumption, and kernel runtime of Bitonic Mergesort (a promising sort for parallel architectures) under various workloads on NVIDIA K40 GPU. The results show some insights in terms of power and energy consumption advantage of Bitonic Mergesort compared with NVIDIA's Advanced Quicksort (a highly optimized parallel quicksort).

Keywords

• Energy measurement
• power measurement
• exascale computing
• GPU
• sorting