Sequence Homology Search Using Fine Grained Cycle Sharing of Idle GPUs
Sequence Homology Search Using Fine Grained Cycle Sharing of Idle GPUs
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DOI：
10.1109/tpds.2011.239
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发表时间：
2012-04
影响因子：
5.3
通讯作者：
Fumihiko Ino;Yuma Munekawa;K. Hagihara
中科院分区：
文献类型：
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作者：
Fumihiko Ino;Yuma Munekawa;K. Hagihara
本文提出了一种细粒度循环共享(FGCS)系统，该系统能够利用空闲的图形处理单元(GPU)来加速局域网环境中的序列同源性搜索。我们的系统通过运行客户程序的一小部分来利用GPU上的短空闲时间，这样每一部分都可以在数百毫秒内完成。为了从注册资源池中检测到如此短的空闲期，我们的系统通过事件处理程序持续监控键盘和鼠标活动，而不是像在现有系统中通常部署的那样等待屏幕保护程序。我们的系统还根据性能模型将客户任务划分为小部分，该模型估计部分的执行时间。这种任务划分策略最大限度地减少了对GPU资源所有者的任何干扰。实验结果表明，在两个非专用GPU上运行的FGCS系统的吞吐量是单个专用GPU的111%-116%。此外，我们的系统提供的吞吐量是基于屏幕保护程序的系统的两倍多。我们还表明，我们的系统检测到的空闲时间占系统正常运行时间的一半。我们相信，隐藏在办公环境中且经常不使用的GPU为序列同源性搜索提供了一个强大的解决方案。
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.