StoreGPU: Exploiting Graphics Processing Units to Accelerate Distributed Storage Systems

Today 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, 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 ten fold performance gains on synthetic benchmarks and up to nine-fold gains for a high-level application, online similarity detection between large data files.

People

Faculty:

• Matei Ripeanu

    Students:

• Samer Al-Kiswany

• Abdullah Gharaibeh

• Elizeu Santos-Neto

• George Yuan

Publications