Issue No. 08 - Aug. (2013 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.239
Samer Al-Kiswany , The University of British Columbia, Vancouver
Abdullah Gharaibeh , The University of British Columbia, Vancouver
Matei Ripeanu , The University of British Columbia, Vancouver
Massively multicore processors, such as 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. This project explores the feasibility of harnessing 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, and introduce techniques to efficiently leverage the processing power of GPUs. 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.
Graphics processing unit, Memory management, Instruction sets, Acceleration, Parallel processing, Resource management, Prototypes, graphics processing units (GPUs), Graphics processing unit, Memory management, Instruction sets, Acceleration, Parallel processing, Resource management, Prototypes, content addressable storage, Storage system design, massively parallel processors
M. Ripeanu, A. Gharaibeh and S. Al-Kiswany, "GPUs as Storage System Accelerators," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 1556-1566, 2013.