利用gpu实现高性价比的分布式RAID

Aleksandr Khasymski, M. M. Rafique, A. Butt, Sudharshan S. Vazhkudai, Dimitrios S. Nikolopoulos
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引用次数: 17

摘要

用户和应用程序数据的指数级增长需要新的方法来提供容错和防止数据丢失的保护。高性能计算(High Performance Computing, HPC)存储系统处于处理海量数据的前沿,通常在后端采用硬件RAID。但是,这种解决方案成本高,不能保证端到端的数据完整性,并且可能成为数据重建过程中的瓶颈。在本文中,我们设计了一种创新的解决方案,以实现并行文件系统(PFS)的灵活、容错和高性能RAID-6解决方案。我们的系统利用低成本,战略性地放置gpu -在客户端和服务器端-来加速奇偶计算。与基于硬件的方法相比,我们在每个文件的基础上提供对RAID阵列的大小、长度和位置的完全控制,端到端数据完整性检查,以及RAID阵列重构的并行化。我们已经将我们的系统与广泛使用的Lustre PFS一起部署,并表明我们的方法是可行的,并且会产生可接受的开销。
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On the Use of GPUs in Realizing Cost-Effective Distributed RAID
The exponential growth in user and application data entails new means for providing fault tolerance and protection against data loss. High Performance Computing (HPC) storage systems, which are at the forefront of handling the data deluge, typically employ hardware RAID at the backend. However, such solutions are costly, do not ensure end-to-end data integrity, and can become a bottleneck during data reconstruction. In this paper, we design an innovative solution to achieve a flexible, fault-tolerant, and high-performance RAID-6 solution for a parallel file system (PFS). Our system utilizes low-cost, strategically placed GPUs - both on the client and server sides - to accelerate parity computation. In contrast to hardware-based approaches, we provide full control over the size, length and location of a RAID array on a per file basis, end-to-end data integrity checking, and parallelization of RAID array reconstruction. We have deployed our system in conjunction with the widely-used Lustre PFS, and show that our approach is feasible and imposes acceptable overhead.
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