A Note on Auto-tuning GEMM for GPUs

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A Note on Auto-tuning GEMM for GPUs
The development of high performance dense linear algebra (DLA) critically depends on highly optimized BLAS, and especially on the matrix multiplication routine (GEMM). This is especially true for Graphics Processing Units (GPUs), as evidenced by recently published results on DLA for GPUs that rely on highly optimized GEMM [13, 11]. However, the current best GEMM performance, e.g. of up to 375 GFlop/s in single precision and of up to 75 GFlop/s in double precision arithmetic on NVIDIA’s GTX 280, is difficult to achieve. The development involves extensive GPU knowledge and even backward engineering to understand some undocumented insides about the architecture that have been of key importance in the development [12]. In this paper, we describe some GPU GEMM auto-tuning optimization techniques that allow us to keep up with changing hardware by rapidly reusing, rather than reinventing, the existing ideas. Auto-tuning, as we show in this paper, is a very practical solution where in additi...
Yinan Li, Jack Dongarra, Stanimire Tomov
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICCS
Authors Yinan Li, Jack Dongarra, Stanimire Tomov
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