Evaluation of the HPC challenge benchmarks in virtualized environments

  • Authors:

  • Piotr Luszczek;Eric Meek;Shirley Moore;Dan Terpstra;Vincent M. Weaver;Jack Dongarra

  • Affiliations:

  • Innovative Computing Laboratory, University of Tennessee, Knoxville;Innovative Computing Laboratory, University of Tennessee, Knoxville;Innovative Computing Laboratory, University of Tennessee, Knoxville;Innovative Computing Laboratory, University of Tennessee, Knoxville;Innovative Computing Laboratory, University of Tennessee, Knoxville;Innovative Computing Laboratory, University of Tennessee, Knoxville

  • Venue:
  • Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing - Volume 2
  • Year:
  • 2011

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Abstract

This paper evaluates the performance of the HPC Challenge benchmarks in several virtual environments, including VMware, KVM and VirtualBox. The HPC Challenge benchmarks consist of a suite of tests that examine the performance of HPC architectures using kernels with memory access patterns more challenging than those of the High Performance LINPACK (HPL) benchmark used in the TOP500 list. The tests include four local (matrix-matrix multiply, STREAM, RandomAccess and FFT) and four global (High Performance Linpack --- HPL, parallel matrix transpose --- PTRANS, RandomAccess and FFT) kernel benchmarks. The purpose of our experiments is to evaluate the overheads of the different virtual environments and investigate how different aspects of the system are affected by virtualization. We ran the benchmarks on an 8-core system with Core i7 processors using Open MPI. We did runs on the bare hardware and in each of the virtual environments for a range of problem sizes. As expected, the HPL results had some overhead in all the virtual environments, with the overhead becoming less significant with larger problem sizes. The RandomAccess results show drastically different behavior and we attempt to explain it with pertinent experiments. We show the cause of variability of performance results as well as major causes of measurement error.