三精度BLAS子程序在gpu上的实现与评价

Daichi Mukunoki, D. Takahashi
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引用次数: 12

摘要

在特斯拉C2050上实现并评估了三精度基本线性代数子程序(BLAS) AXPY、GEMV和GEMM。本文给出了一种双单(D+S)型三精度浮点值格式及其运算。它们基于类似于双双(DD)型四倍精度运算的技术。在GPU上,D+ s类型的运算在理论和实践上都比dd类型的运算成本更高。因此,三精度的GEMM比四精度的GEMM慢,因为它是一个计算绑定的操作。然而,三精度AXPY和GEMV在GPU上是内存受限的操作,因此它们的三精度子程序的执行时间接近四精度子程序的3/4。因此,我们得出结论,在不需要四倍精度,但双精度不够的情况下,三重精度值格式对内存绑定操作是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation and Evaluation of Triple Precision BLAS Subroutines on GPUs
We implemented and evaluated the triple precision Basic Linear Algebra Subprograms (BLAS) subroutines, AXPY, GEMV and GEMM on a Tesla C2050. In this paper, we present a Double Single (D+S) type triple precision floating-point value format and operations. They are based on techniques similar to Double-Double (DD) type quadruple precision operations. On the GPU, the D+S-type operations are more costly than the DD-type operations in theory and in practice. Therefore, the triple precision GEMM, which is a compute-bound operation, is slower than the quadruple precision GEMM. However, the triple precision AXPY and GEMV are memory-bound operations on the GPU, thus their execution time of these triple precision subroutines is close to 3/4 of the quadruple precision subroutines. Therefore, we conclude that the triple precision value format is useful for memory-bound operations, in cases where the quadruple precision is not required, but double precision is not sufficient.
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