Scientific Computations on Modern Parallel Vector Systems

  • Authors:

  • Leonid Oliker;Andrew Canning;Jonathan Carter;John Shalf;Stephane Ethier

  • Affiliations:

  • Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Lawrence Berkeley National Laboratory;Princeton University

  • Venue:
  • Proceedings of the 2004 ACM/IEEE conference on Supercomputing
  • Year:
  • 2004

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Abstract

Computational scientists have seen a frustrating trend of stagnating application performance despite dramatic increases in the claimed peak capability of high performance computing systems. This trend has been widely attributed to the use of superscalar-based commodity components whoýs architectural designs offer a balance between memory performance, network capability, and execution rate that is poorly matched to the requirements of large-scale numerical computations. Recently, two innovative parallel-vector architectures have become operational: the Japanese Earth Simulator (ES) and the Cray X1. In order to quantify what these modern vector capabilities entail for the scientists that rely on modeling and simulation, it is critical to evaluate this architectural paradigm in the context of demanding computational algorithms. Our evaluation study examines four diverse scientific applications with the potential to run at ultrascale, from the areas of plasma physics, material science, astrophysics, and magnetic fusion. We compare performance between the vector-based ES and X1, with leading superscalar-based platforms: the IBM Power3/4 and the SGI Altix. Our research team was the first international group to conduct a performance evaluation study at the Earth Simulator Center; remote ES access in not available. Results demonstrate that the vector systems achieve excellent performance on our application suite - the highest of any architecture tested to date. However, vectorization of a particle-in-cell code highlights the potential difficulty of expressing irregularly structured algorithms as data-parallel programs.