22887

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Cade Brown, Ahmad Abdelfattah, Stanimire Tomov, Jack Dongarra
Innovative Computing Laboratory, University of Tennessee, Knoxville, USA
Innovative Computing Laboratory Technical Report ICL-UT-20-12, 2020

@article{brown2020design,

   title={Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs},

   author={Brown, Cade and Abdelfattah, Ahmad and Tomov, Stanimire and Dongarra, Jack},

   year={2020}

}

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Dense linear algebra (DLA) has historically been in the vanguard of software that must be adapted first to hardware changes. This is because DLA is both critical to the accuracy and performance of so many different types of applications, and because they have proved to be outstanding vehicles for finding and implementing solutions to the problems that novel architectures pose. Therefore, in this paper we investigate the portability of the MAGMA DLA library to the latest AMD GPUs. We use auto tools to convert the CUDA code in MAGMA to the HeterogeneousComputing Interface for Portability (HIP) language. MAGMA provides LAPACK for GPUs and benchmarks for fundamental DLA routines ranging from BLAS to dense factorizations, linear systems and eigen-problem solvers. We port these routines to HIP and quantify currently achievable performance through the MAGMA benchmarks for the main workload algorithms on MI25 and MI50 AMD GPUs. Comparison with performance roofline models and theoretical expectations are used to identify current limitations and directions for future improvements.
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