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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >GPUs as Storage System Accelerators
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GPUs as Storage System Accelerators

机译：GPU作为存储系统加速器

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摘要

Massively multicore processors, such as graphics processing units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.
机译：大量的多核处理器（例如图形处理单元（GPU））以可比的价格提供比传统CPU高出一个数量级的峰值性能。这种计算成本的下降，因为一类系统组件的每单位性能成本的任何数量级的下降，都触发了重新设计系统并探索设计其重新校准成本的新方法的机会。绩效关系。该项目探讨了利用GPU的计算能力来提高分布式存储系统的性能，可靠性或安全性的可行性。在这种情况下，我们提出了一种存储系统原型的设计，该原型使用GPU卸载来加速基于哈希的大量计算密集型原语，并介绍有效利用GPU的处理能力的技术。我们评估该原型在两种配置下的性能：作为可寻址的内容存储系统（有助于在同一文件的后续版本之间进行在线相似性检测），以及作为传统系统（使用哈希来保持数据完整性）。此外，我们评估了卸载GPU对竞争应用程序性能的影响。我们的结果表明，该技术可以带来明显的性能提升，而不会负面影响并发运行的应用程序的性能。

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