Optimizing Memory-Bound SYMV Kernel on GPU Hardware Accelerators

  • Ahmad Abdelfattah
  • Jack Dongarra
  • David Keyes
  • Hatem Ltaief
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7851)

Abstract

Hardware accelerators are becoming ubiquitous high performance scientific computing. They are capable of delivering an unprecedented level of concurrent execution contexts. High-level programming language extensions (e.g., CUDA), profiling tools (e.g., PAPI-CUDA, CUDA Profiler) are paramount to improve productivity, while effectively exploiting the underlying hardware. We present an optimized numerical kernel for computing the symmetric matrix-vector product on nVidia Fermi GPUs. Due to its inherent memory-bound nature, this kernel is very critical in the tridiagonalization of a symmetric dense matrix, which is a preprocessing step to calculate the eigenpairs. Using a novel design to address the irregular memory accesses by hiding latency and increasing bandwidth, our preliminary asymptotic results show 3.5x and 2.5x fold speedups over the similar CUBLAS 4.0 kernel, and 7-8% and 30% fold improvement over the Matrix Algebra on GPU and Multicore Architectures (MAGMA) library in single and double precision arithmetics, respectively.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ahmad Abdelfattah
    • 1
  • Jack Dongarra
    • 2
  • David Keyes
    • 1
  • Hatem Ltaief
    • 3
  1. 1.Division of Mathematical and Computer Sciences and EngineeringKAUSTThuwalSaudi Arabia
  2. 2.Innovative Computing LaboratoryUniversity of TennesseeKnoxvilleUSA
  3. 3.Supercomputing LaboratoryKAUSTThuwalSaudi Arabia

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