To reduce the high computation overhead induced by erasure coding, an effective way is to convert multiplications in finite fields intoXORs. However, the existing coding libraries adopt standard binaryXORand ignore the register efficiency, which inevitably induces too many extraLOADs/STOREsbetween registers and cache/memory, contributing to the main coding latency. From the view of register efficiency, we redesign the diagram of executingXORsand propose a new coding procedure, Coding with Adaptation to Registers (CAR), which keeps the temporal parities in registers until their constructions are completed. We further propose an enhanced coding procedure, CAR+, which further reduces the number ofLOADsby leveraging multiple registers. By integrating multiple optimizations into CAR and CAR+, we implement an erasure coding library, which increases the encoding throughput by up to 203.1% compared with the state-of-the-art erasure coding libraries.

Erasure coding is a common redundancy scheme for tolerating failures in storage systems. Compared with replication, erasure coding saves a large amount of storage space, but incurs heavy computation overhead and, is more time consuming. In this article, we accelerate the coding speed with three techniques. First, we propose an algorithm to search coding bitmatrices with fewer 1’s from Vandermonde and Cauchy matrices, and further optimize the coding bitmatrices by greedily reducing the number of 1’s in the bitmatrices. So we can find near-optimal coding bitmatrices with the number of 1’s only up to 1% more than the lower bound. Next, we redesign the process of building pointers and reuse the pointers to access data for coding, which obtains a better tradeoff between spatial locality and computation efficiency. Finally, we smartly decompose the coding procedure of wide stripes into multiple subprocedures, to improve spatial locality and reduce the number of XORs. Based on the proposed techniques, we implement an erasure coding library, Cerasure. Extensive experiments show that Cerasure significantly improves the coding throughput. Compared with the state-of-the-art erasure coding libraries, Zerasure and SLPEC, Cerasure increases the encoding throughput by up to 200.2%.

Erasure coding is a common redundancy scheme for tolerating failures in storage systems. Compared with replication, erasure coding saves a large amount of storage space, but incurs heavy computation overhead and thus is more time-consuming. To this end, we design an algorithm to find a better parity coding matrix to reduce the number of XORs in coding based on Vandermonde matrices instead of Cauchy matrices. In addition, we optimize the coding process, to accelerate the computation speed of XOR and obtain a better tradeoff between spatial locality and computation efficiency. For wide stripes which becomes increasingly interesting, we propose to decompose the coding procedure into multiple subprocedures for better utilization of spatial locality. We integrate these methods into coding procedure and implement an erasure coding library, Cerasure. Extensive experiments show that Cerasure significantly improves the coding speed. Compared with the state-of-the-art erasure coding libraries, Zerasure and SLPEC, Cerasure increases the encoding throughput by up to 109.47%.