Paradigmatic shifts for exascale supercomputing

Neal E Davis, Robert W. Robey, Charles R. Ferenbaugh, David Nicholaeff, Dennis P. Trujillo

Research output: Contribution to journalArticle

Abstract

As the next generation of supercomputers reaches the exascale, the dominant design parameter governing performance will shift from hardware to software. Intelligent usage of memory access, vectorization, and intranode threading will become critical to the performance of scientific applications and numerical calculations on exascale supercomputers. Although challenges remain in effectively programming the heterogeneous devices likely to be utilized in future supercomputers, new languages and tools are providing a pathway for application developers to tackle this new frontier. These languages include open programming standards such as OpenCL and OpenACC, as well as widely-adopted languages such as CUDA; also of importance are high-quality libraries such as CUDPP and Thrust. This article surveys a purposely diverse set of proof-of-concept applications developed at Los Alamos National Laboratory. We find that the capability level of the accelerator computing hardware and languages has moved beyond the regular grid finite difference calculations and molecular dynamics codes. More advanced applications requiring dynamic memory allocation, such as cell-based adaptive mesh refinement, can now be addressedand with more effort even unstructured mesh codes can be moved to the GPU.

Original languageEnglish (US)
Pages (from-to)1023-1044
Number of pages22
JournalJournal of Supercomputing
Volume62
Issue number2
DOIs
StatePublished - Nov 1 2012

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Keywords

  • Cell-based adaptive mesh refinement
  • Exascale computing
  • GPGPU
  • Heterogeneous architecture
  • Molecular dynamics

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

Cite this

Davis, N. E., Robey, R. W., Ferenbaugh, C. R., Nicholaeff, D., & Trujillo, D. P. (2012). Paradigmatic shifts for exascale supercomputing. Journal of Supercomputing, 62(2), 1023-1044. https://doi.org/10.1007/s11227-012-0789-3