Sequence Homology Search Using Fine Grained Cycle Sharing of Idle GPUs

In this paper, we propose a Fine Grained Cycle Sharing (FGCS) system capable of exploiting idle Graphics Processing Units (GPUs) for accelerating sequence homology search in local area network environments. Our system exploits short idle periods on GPUs by running small parts of guest programs such that each part can be completed within hundreds of milliseconds. To detect such short idle periods from the pool of registered resources, our system continuously monitors keyboard and mouse activities via event handlers rather than waiting for a screensaver, as is typically deployed in existing systems. Our system also divides guest tasks into small parts according to a performance model that estimates execution times of the parts. This task division strategy minimizes any disruption to the owners of the GPU resources. Experimental results show that our FGCS system running on two nondedicated GPUs achieves 111-116 percent of the throughput achieved by a single dedicated GPU. Furthermore, our system provides over two times the throughput of a screensaver-based system. We also show that the idle periods detected by our system constitute half of the system uptime. We believe that the GPUs hidden and often unused in office environments provide a powerful solution to sequence homology search.