CuHMMer: A load-balanced CPU-GPU cooperative bioinformatics application

Abstract—GPUs have recently been used to accelerate dataparallel applications for they provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power. Most of those applications use CPU as a controller who decides when GPUs run the computing-intensive tasks. This CPU-control-GPUcompute pattern wastes much of CPU’s computational power. In this paper, we present a new CPU-GPU cooperative pattern for bioinformatics applications which can use both of CPU and GPU to compute. This pattern includes two parts: 1) the load-balanced data structure which manages data to keep the computational efficiency of GPU high enough when the length distribution of sequences in a sequence database is very uneven; 2) multi-threaded code partition which schedules computing on CPU and GPU in a cooperative way. Using this pattern, we develop CuHMMer based on HMMER which is one of the most important algorithms in bioinformatics. The experimen...

• Type: Journal
• Year: 2010
• Where: IEEEHPCS
• Authors: Ping Yao, Hong An, Mu Xu, Gu Liu, Xiaoqiang Li, Yaobin Wang, Wenting Han
• Keywords: Applied Computing, Computational Power, CPU’s Computational Power, IEEEHPCS 2010, Tremendous Memory Bandwidth