The Impact of Vectorization on Erasure Code Computing in Cloud Storages - A Performance and Power Consumption Study

Erasure code storage systems are becoming popular choices for cloud storage systems due to cost-effective storage space saving schemes and higher fault-resilience capabilities. Both erasure code encoding and decoding procedures are involving heavy array, matrix, and table-lookup compute intensive operations. Multi-core, many-core, and streaming SIMD extension are implemented in modern CPU designs. In this paper, we study the power consumption and energy efficiency of erasure code computing using traditional Intel x86 platform and Intel Streaming SIMD extension platform. We use a breakdown power consumption analysis approach and conduct power studies of erasure code encoding process on various storage devices. We present the impact of various storage devices on erasure code based storage systems in terms of processing time, power utilization, and energy cost. Finally we conclude our studies and demonstrate the Intel x86's Streaming SIMD extensions computing is a cost-effective and favorable choice for future power efficient HPC cloud storage systems.