High performance GPU-based parity computing scheduler in storage applications

This 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(log(2)nw) 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 (C) 2016 John Wiley & Sons, Ltd.