On GPU's viability as a middleware accelerator

  • Authors:

  • Samer Al-Kiswany;Abdullah Gharaibeh;Elizeu Santos-Neto;Matei Ripeanu

  • Affiliations:

  • Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, Canada V6T 1Z4;Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, Canada V6T 1Z4;Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, Canada V6T 1Z4;Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, Canada V6T 1Z4

  • Venue:
  • Cluster Computing
  • Year:
  • 2009

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Abstract

Today Graphics Processing Units (GPUs) are a largely underexploited resource on existing desktops and a possible cost-effective 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 and we design StoreGPU, a library that accelerates a number of hashing-based middleware primitives popular in distributed storage system implementations. Our evaluation shows that StoreGPU enables up twenty five fold performance gains on synthetic benchmarks as well as on a high-level application: the online similarity detection between large data files.