17236

CLBlast: A Tuned OpenCL BLAS Library

Cedric Nugteren
TomTom, Amsterdam, The Netherlands
arXiv:1705.05249 [cs.MS], (12 May 2017)

@article{nugteren2017clblast,

   title={CLBlast: A Tuned OpenCL BLAS Library},

   author={Nugteren, Cedric},

   year={2017},

   month={may},

   archivePrefix={"arXiv"},

   primaryClass={cs.MS}

}

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This work demonstrates how to accelerate dense linear algebra computations using CLBlast, an open-source OpenCL BLAS library providing optimized routines for a wide variety of devices. It is targeted at machine learning and HPC applications and thus provides a fast matrix-multiplication routine (GEMM) to accelerate the core of many applications (e.g. deep learning, iterative solvers, astrophysics, computational fluid dynamics, quantum chemistry). CLBlast has four main advantages over other BLAS libraries: 1) it is optimized for and tested on a large variety of OpenCL devices including less commonly used devices such as embedded and low-power GPUs, 2) it can be explicitly tuned for specific problem-sizes on specific hardware platforms, 3) it can perform operations in half-precision floating-point FP16 saving precious bandwidth, time and energy, 4) and it can combine multiple operations in a single batched routine, accelerating smaller problems significantly. This paper describes the library and demonstrates the advantages of CLBlast experimentally for different use-cases on a wide variety of OpenCL hardware.
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