Harnessing the power of idle GPUs for acceleration of biological sequence alignment
Harnessing the power of idle GPUs for acceleration of biological sequence alignment
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DOI：
10.1142/s0129626409000390
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
Fumihiko Ino;Yuki Kotani;Yuma Munekawa;K. Hagihara
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文献类型：
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作者：
Fumihiko Ino;Yuki Kotani;Yuma Munekawa;K. Hagihara
本文提出了一种并行系统，能够加速生物序列比对的图形处理单元（GPU）的网格。本文中的GPU网格是一个桌面网格系统，它利用办公室和家庭中的空闲GPU和CPU。我们的并行实现采用了一个主工人范式，以加速刘的OpenGL为基础的算法，在一个单一的GPU上运行。我们将此实现集成到一个基于屏幕保护程序的网格系统中，该系统可以检测对齐代码可以运行的空闲资源。我们还展示了一些实验结果，比较我们的实现与三种不同的实现运行在一个单一的GPU，一个单一的CPU，或多个CPU。因此，我们发现在我们的实验室环境中，单个非专用GPU可以为我们提供与两个专用CPU几乎相同的吞吐量，其中配备GPU的机器通常用于开发GPU应用程序。
This paper presents a parallel system capable of accelerating biological sequence alignment on the graphics processing unit (GPU) grid. The GPU grid in this paper is a desktop grid system that utilizes idle GPUs and CPUs in the office and home. Our parallel implementation employs a master-worker paradigm to accelerate Liu's OpenGL-based algorithm that runs on a single GPU. We integrate this implementation into a screensaver-based grid system that detects idle resources on which the alignment code can run. We also show some experimental results comparing our implementation with three different implementations running on a single GPU, a single CPU, or multiple CPUs. As a result, we find that a single non-dedicated GPU can provide us almost the same throughput as two dedicated CPUs in our laboratory environment, where GPU-equipped machines are ordinarily used to develop GPU applications.