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StoreGPU: Exploiting Graphics Processing Units to Accelerate Distributed Storage Systems
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BibTeX
@MISC{Al-kiswany_storegpu:exploiting,
author = {Samer Al-kiswany and Abdullah Gharaibeh and Elizeu Santos-neto and George Yuan and Matei Ripeanu},
title = {StoreGPU: Exploiting Graphics Processing Units to Accelerate Distributed Storage Systems},
year = {}
}
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Abstract
Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible costeffective enhancement to high-performance systems. To date, most applications that exploit GPUs are specialized scientific applications. Little attention has been paid to harnessing these highly-parallel devices to support more generic functionality at the operating system or middleware level. This study starts from the hypothesis that generic middleware-level techniques that improve distributed system reliability or performance (such as content addressing, erasure coding, or data similarity detection) can be significantly accelerated using GPU support. We take a first step towards validating this hypothesis, focusing on distributed storage systems. As a proof of concept, we design StoreGPU, a library that accelerates a number of hashing based primitives popular in distributed storage system implementations. Our evaluation shows that StoreGPU enables up to eight-fold performance gains on synthetic benchmarks as well as on a highlevel application: the online similarity detection between large data files.
Keyphrases
exploiting graphic accelerate distributed storage system highlevel application data similarity detection eight-fold performance gain highly-parallel device erasure coding distributed storage system online similarity detection underexploited resource generic middleware-level technique synthetic benchmark large data file middleware level possible costeffective enhancement system reliability little attention today graphic high-performance system scientific application generic functionality storage system implementation operating system gpu support first step content addressing
