StoreGPU: exploiting graphics processing units to accelerate distributed storage systems
StoreGPU: exploiting graphics processing units to accelerate distributed storage systems
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DOI：
10.1145/1383422.1383443
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发表时间：
2008-06
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影响因子：
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通讯作者：
S. Al-Kiswany;Abdullah Gharaibeh;E. Santos-Neto;George L. Yuan;M. Ripeanu
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作者：
S. Al-Kiswany;Abdullah Gharaibeh;E. Santos-Neto;George L. Yuan;M. Ripeanu
如今，图形处理单元 (GPU) 是现有桌面上很大程度上未充分利用的资源，也是对高性能系统的一种可能经济有效的增强。迄今为止，大多数利用 GPU 的应用程序都是专门的科学应用程序。很少有人关注如何利用这些高度并行的设备来支持操作系统或中间件级别的更通用的功能。本研究的假设是，使用 GPU 支持可以显着加速提高分布式系统可靠性或性能的通用中间件级技术（例如内容寻址、纠删码或数据相似性检测）。我们迈出了验证这一假设的第一步，重点关注分布式存储系统。作为概念验证，我们设计了 StoreGPU，这是一个库，可以加速分布式存储系统实现中流行的许多基于哈希的原语。我们的评估表明，StoreGPU 在综合基准测试以及高级应用程序（大型数据文件之间的在线相似性检测）上实现了高达八倍的性能提升。
Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible cost-effective enhancement to high-performance systems. To date, most applications that exploit GPUs are specialized scientific applications. Little attention has been paid to harnessing these highly-parallel devices to support more generic functionality at the operating system or middleware level. This study starts from the hypothesis that generic middleware level techniques that improve distributed system reliability or performance (such as content addressing, erasure coding, or data similarity detection) can be significantly accelerated using GPU support. We take a first step towards validating this hypothesis, focusing on distributed storage systems. As a proof of concept, we design StoreGPU, a library that accelerates a number of hashing based primitives popular in distributed storage system implementations. Our evaluation shows that StoreGPU enables up to eight-fold performance gains on synthetic benchmarks as well as on a high-level application: the online similarity detection between large data files.