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On GPU’s viability as a middleware accelerator

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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.

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Authors and Affiliations

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

    Samer Al-Kiswany, Abdullah Gharaibeh, Elizeu Santos-Neto & Matei Ripeanu

Authors
  1. Samer Al-Kiswany
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  2. Abdullah Gharaibeh
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  3. Elizeu Santos-Neto
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  4. Matei Ripeanu
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Correspondence to Samer Al-Kiswany.

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Al-Kiswany, S., Gharaibeh, A., Santos-Neto, E. et al. On GPU’s viability as a middleware accelerator. Cluster Comput 12, 123–140 (2009). https://doi.org/10.1007/s10586-009-0076-0

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  • DOI: https://doi.org/10.1007/s10586-009-0076-0

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