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GPU VSIPL: Core and Beyond

GPU VSIPL: Core and Beyond. Andrew Kerr 1 , Dan Campbell 2 , and Mark Richards 1 1 Georgia Institute of Technology 2 Georgia Tech Research Institute. Goal. An application development environment for embedded high performance computing that achieves

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GPU VSIPL: Core and Beyond

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  1. GPU VSIPL:Core and Beyond Andrew Kerr1, Dan Campbell2, and Mark Richards1 1Georgia Institute of Technology2Georgia Tech Research Institute

  2. Goal • An application development environment for embedded high performance computing that achieves • Portability: same code usable with different processors, processor generations, and vendors • Productivity: disciplined programming model, leverage highly optimized libraries for signal processing+linear algebra • Performance: employ highly advanced processors,~1 TFLOPS Approach • Adopt the VSIPL API for open standard portability and productivity • Develop a state-of-the-art GPU-VSIPL library to leverage CUDA-enabled GPU performance

  3. GPU-VSIPL Functional Coverage VSIPL API • What’s covered from VSIPL Core • Data Types • real, complex, integer, boolean, index • View Types • Matrix, vector • Element-wise Operators • arithmetic, trigonometric, transcendental, scatter/gather, logical, and comparison • Signal Processing • FFT (in-place, out-of-place, batched) • Fast FIR filter, window creation,1D correlation • Random number generation, histogram • Linear Algebra • generalized matrix product • QR decomposition, least-squares solver • What’s Not (yet) • Linear Algebra • LU, Toeplitz, least-squares solvers • What’s Added Beyond VSIPL Core • Scalar and matrix versions of element-wise vector operators • Matrix utility functions Core Profile GPU VSIPL Core Lite Profile

  4. Performance Examples:Signal Processing 1D FFT, In-Place 1D Correlation

  5. Performance Examples:Linear Algebra Matrix-Vector Product QR Decomposition

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