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Accelerating the MilkyWay@Home Volunteer Computing Project with GPUs

  • Travis Desell
  • Anthony Waters
  • Malik Magdon-Ismail
  • Boleslaw K. Szymanski
  • Carlos A. Varela
  • Matthew Newby
  • Heidi Newberg
  • Andreas Przystawik
  • David Anderson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6067)

Abstract

General-Purpose computing on Graphics Processing Units (GPGPU) is an emerging field of research which allows software developers to utilize the significant amount of computing resources GPUs provide for a wider range of applications. While traditional high performance computing environments such as clusters, grids and supercomputers require significant architectural modifications to incorporate GPUs, volunteer computing grids already have these resources available as most personal computers have GPUs available for recreational use. Additionally, volunteer computing grids are gradually upgraded by the volunteers as they upgrade their hardware, whereas clusters, grids and supercomputers are typically upgraded only when replaced by newer hardware. As such, MilkyWay@Home’s volunteer computing system is an excellent testbed for measuring the potential of large scale distributed GPGPU computing across a large number of heterogeneous GPUs. This work discusses the implementation and optimization of the MilkyWay@Home client application for both Nvidia and ATI GPUs. A 17 times speedup was achieved for double-precision calculations on a Nvidia GeForce GTX 285 card, and a 109 times speedup for double-precision calculations on an ATI HD5870 card, compared to the CPU version running on one core of a 3.0 GHz AMD Phenom(tm)II X4 940. Using single-precision calculations was also evaluated which further increased performance 6.2 times for ATI card, and 7.8 times on the Nvidia card but with some loss of accuracy. Modifications to the BOINC infrastructure which enable GPU discovery and utilization are also discussed. The resulting software enabled MilkyWay@Home to use GPU applications for a significant increase in computing power, at the time of this publication approximately 216 teraflops, which would place the combined power of these GPUs between the 11th and 12th fastest supercomputers in the world.

Keywords

Graphic Processing Unit Single Instruction Multiple Data Volunteer Computing World Community Grid GPGPU Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Travis Desell
    • 1
  • Anthony Waters
    • 1
  • Malik Magdon-Ismail
    • 1
  • Boleslaw K. Szymanski
    • 1
  • Carlos A. Varela
    • 1
  • Matthew Newby
    • 2
  • Heidi Newberg
    • 2
  • Andreas Przystawik
    • 3
  • David Anderson
    • 4
  1. 1.Department of Computer ScienceRensselaer Polytechnic InstituteTroy NYUSA
  2. 2.Department of Physics, Applied Physics and AstronomyRensselaer Polytechnic InstituteTroy NYUSA
  3. 3.Institut für PhysikUniversität RostockRostockGermany
  4. 4.U.C. Berkeley Space Sciences LaboratoryUniversity of California, BerkeleyBerkeleyUSA

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