Fast Erasure Coding for Data Storage

Abstract

Various techniques have been proposed in the literature to improve erasure code computation efficiency, including optimizing bitmatrix design and computation schedule, common XOR (exclusive-OR) operation reduction, caching management techniques, and vectorization techniques. These techniques were largely proposed individually, and, in this work, we seek to use them jointly. To accomplish this task, these techniques need to be thoroughly evaluated individually and their relation better understood. Building on extensive testing, we develop methods to systematically optimize the computation chain together with the underlying bitmatrix. This led to a simple design approach of optimizing the bitmatrix by minimizing a weighted computation cost function, and also a straightforward coding procedure—follow a computation schedule produced from the optimized bitmatrix to apply XOR-level vectorization. This procedure provides better performances than most existing techniques (e.g., those used in ISA-L and Jerasure libraries), and sometimes can even compete against well-known but less general codes such as EVENODD, RDP, and STAR codes. One particularly important observation is that vectorizing the XOR operations is a better choice than directly vectorizing finite field operations, not only because of the flexibility in choosing finite field size and the better encoding throughput, but also its minimal migration efforts onto newer CPUs.