ViennaCL-Linear Algebra Library for Multi- and Many-Core Architectures

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Authors Karl Rupp, Philippe Tillet, Florian Rudolf, Josef Weinbub, Andreas Morhammer, Tibor Grasser, Ansgar J√ľngel, Siegfried Selberherr
Journal/Conference Name SIAM J. Scientific Computing
Paper Category
Paper Abstract CUDA, OpenCL, and OpenMP are popular programming models for the multicore architectures of CPUs and many-core architectures of GPUs or Xeon Phis. At the same time, computational scientists face the question of which programming model to use to obtain their scientific results. We present the linear algebra library ViennaCL, which is built on top of all three programming models, thus enabling computational scientists to interface to a single library, yet obtain high performance for all three hardware types. Since the respective compute back end can be selected at runtime, one can seamlessly switch between different hardware types without the need for error-prone and time-consuming recompilation steps. We present new benchmark results for sparse linear algebra operations in ViennaCL, complementing results for the dense linear algebra operations in ViennaCL reported in earlier work. Comparisons with vendor libraries show that ViennaCL provides better overall performance for sparse matrix-vector and sparse matrix-matrix products. Additional benchmark results for pipelined iterative solvers with kernel fusion and preconditioners identify the respective sweet spots for CPUs, Xeon Phis, and GPUs.
Date of publication 2016
Code Programming Language R

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