High performance GPU‐based parity computing scheduler in storage applications

Abstract

SummaryThis paper proposes a high‐performance graphics processing unit (GPU)‐based parity computing scheduler, which we call GPU‐redundant array of inexpensive disks (RAID), to reduce the encoding and decoding time for storage applications. The proposed GPU‐RAID differs from existing RAID in that it performs additional pairwise‐parallel XOR operations between data code words in each data stripe by applying divide‐and‐conquer approach using extra reserved space and it also increases parallelism by processing multiple strips in parallel using multiple GPU threads. And so the proposed GPU‐RAID pipelines data blocks into solid‐state disks and parity blocks into hard disk drives at the target server. The proposed algorithm decreases the span complexity of the parity computation schedule to O(log2nw) where n is the number of disks and w is the number of code words in a block, and it can be applied to various types of erasure codes. Experimental results show that the proposed storage application (SA1) improves average encoding performance by 63%, and 41%, and average decoding performance by 58%, and 38%, compared with traditional storage applications GPUStore (SA3) and Gibraltar RAID(SA2), respectively. Copyright © 2016 John Wiley & Sons, Ltd.