A Generic FPGA Accelerator for Minimum Storage Regenerating Codes

Mian Qin, Joo Hwan Lee, Rekha Pitchumani, Y. Ki, N. Reddy, Paul V. Gratz
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Abstract

Erasure coding is widely used in storage systems to achieve fault tolerance while minimizing the storage overhead. Recently, Minimum Storage Regenerating (MSR) codes are emerging to minimize repair bandwidth while maintaining the storage efficiency. Traditionally, erasure coding is implemented in the storage software stacks, which hinders normal operations and blocks resources that could be serving other user needs due to poor cache performance and costs high CPU and memory utilizations. In this paper, we propose a generic FPGA accelerator for MSR codes encoding/decoding which maximizes the computation parallelism and minimizes the data movement between off-chip DRAM and the on-chip SRAM buffers. To demonstrate the efficiency of our proposed accelerator, we implemented the encoding/decoding algorithms for a specific MSR code called Zigzag code on Xilinx VCU1525 acceleration card. Our evaluation shows our proposed accelerator can achieve ~2.4-3.1x better throughput and ~4.2-5.7x better power efficiency compared to the state-of-art multi-core CPU implementation and ~2.8-3.3x better throughput and ~4.2-5.3x better power efficiency compared to a modern GPU accelerator.
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一种用于最小存储再生码的通用FPGA加速器
Erasure编码被广泛应用于存储系统中,以实现容错,同时最小化存储开销。为了在保证存储效率的同时最小化修复带宽,最近出现了最小存储再生码(MSR)。传统上,擦除编码是在存储软件堆栈中实现的,这阻碍了正常的操作,并且由于缓存性能差和高CPU和内存利用率,阻塞了可以服务于其他用户需求的资源。在本文中,我们提出了一个通用的FPGA加速器用于MSR编码/解码,它最大限度地提高了计算并行性,并最大限度地减少了片外DRAM和片上SRAM缓冲区之间的数据移动。为了证明我们提出的加速器的效率,我们在Xilinx VCU1525加速卡上实现了一种称为Zigzag代码的特定MSR代码的编码/解码算法。我们的评估表明,与最先进的多核CPU实现相比,我们提出的加速器可以实现~2.4-3.1倍的吞吐量和~4.2-5.7倍的功率效率,与现代GPU加速器相比,可以实现~2.8-3.3倍的吞吐量和~4.2-5.3倍的功率效率。
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