A review on Approximate Computing applied to meteorological forecast models using software-based techniques

Sudo, Marcelo Augusto; Fazenda, Álvaro Luiz

doi:10.5753/eradsp.2020.16884

Cited by 1 publication

(1 citation statement)

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“…This allows users to utilize less expensive and more energy-efficient hardware to perform the reduced precision computation using the arithmetic logic unit. A Systematic Review analyzed by the authors [Sudo and Fazenda 2020] pointed out that "precision scaling" or "mixed precision" is mentioned by almost half of the papers selected when filtered by software techniques from AC.…”

Section: Introductionmentioning

confidence: 99%

Mixed precision applied on common mathematical procedures over GPU

Sudo

Fazenda²,

Souto³

2022

Anais Do XXIII Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2022)

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Approximate Computing is a paradigm used by researchers as alternative to the diminishing of the evolution of hardware performance in the ongoing race for computational throughput in HPC. Precision reduction and mixed precision are the most studied among the existing techniques. In addition, some NVIDIA GPUs have Tensor Core architecture to speed up some classes of algorithms, such as matrix multiplication. This study aims to apply Approximate Computing techniques, like mixed precision, in matrix multiplication and stencil algorithms using OpenACC directives and cuTensor library to analyze performance gains versus accuracy losses. Results showed that it was possible to obtain a speedup of 16.60× with an optimized matrix multiplication algorithm present in the matmul intrinsic function using 16-bit floating-point data with Tensor Core, compared to a naive version using 64-bit floating-point. For this same case, accuracy loss went from 10−26 up to 10−1, approximately. For the stencil algorithm, it was possible to obtain a gain of 1.60× by only reducing variables precision from 64-bit to 16-bit floating-point, with accuracy loss from 0 to 10−9, for 300 iterations.

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Section: Introductionmentioning

confidence: 99%