Paper Info
Title
GPUs as Storage System Accelerators
Abstract
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.
Year
DOI
Venue
2013
10.1109/TPDS.2012.239
IEEE Transactions on Parallel and Distributed Systems
Keywords
DocType
Volume
order-of-magnitude drop,storage system prototype,storage system,content addressable storage system,magnitude higher peak performance,tangible performance gain,storage system accelerators,computational power,system component,harnessing gpus,traditional system,distributed processing,data integrity,resource management,instruction sets,acceleration,parallel processing,parallel,prototypes,cryptography,memory management
Journal
24
Issue
ISSN
Citations 
8
1045-9219
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Samer Al-Kiswany129422.52
Abdullah Gharaibeh224616.75
Matei Ripeanu32461233.84