Performance data of multiple-precision scalar and vector BLAS operations on CPU and GPU

Many optimized linear algebra packages support the singleand double-precision floating-point data types. However, there are a number of important applications that require a higher level of precision, up to hundreds or even thousands of digits. This article presents performance data of four dense basic linear algebra subprograms - ASUM, DOT, SCAL, and AXPY - implemented using existing extended-/multipleprecision software for conventional central processing units and CUDA compatible graphics processing units. The following open source packages are considered: MPFR, MPDECIMAL, ARPREC, MPACK, XBLAS, GARPREC, CAMPARY, CUMP, and MPRES-BLAS. The execution time of CPU and GPU implementations is measured at a fixed problem size and various levels of numeric precision. The data in this article are related to the research article entitled "Design and implementation of multiple-precision BLAS Level 1 functions for graphics processing units"[1]. (C) 2020 The Author(s). Published by Elsevier Inc.