A GPU accelerated storage system

Abstract

Massively multicore processors, like, for example, Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation.In this context, we focus on data storage: We explore the feasibility of harnessing the GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing.We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Further, this work sheds light on the use of heterogeneous multicore processors for enhancing low-level system primitives, and introduces techniques to efficiently leverage the processing power of GPUs.