Communication Optimization on GPU: A Case Study of Sequence Alignment Algorithms

Jie Wang, Xinfeng Xie, Jason Cong

Abstract: Data movement is increasingly becoming the bottleneck of both performance and energy efficiency in modern computation. Until recently, it was the case that there is limited freedom for communication optimization on GPUs, as conventional GPUs only provide two types of methods for inter-thread communication: using shared memory or global memory. However, a new warp shuffle instruction has been introduced since the Kepler architecture on Nvidia GPUs, which enables threads within the same warp to directly exchange data in registers. This brought new performance optimization opportunities for algorithms with intensive inter-thread communication. In this work, we deploy register shuffle in the application domain of sequence alignment (or similarly, string matching), and conduct a quantitative analysis of the opportunities and limitations of using register shuffle. We select two sequence alignment algorithms, Smith-Waterman (SW) and Pairwise-Hidden-Markov-Model (PairHMM), from the widely used Genome Analysis Toolkit (GATK) as case studies. Compared to implementations using shared memory, we obtain a significant speed-up of 1.2× and 2.1× by using shuffle instructions for SW and PairHMM. Furthermore, we develop a performance model for analyzing the kernel performance based on the measured shuffle latency from a suite of microbenchmarks. Our model provides valuable insights for CUDA programmers into how to best use shuffle instructions for performance optimization.