The obtained results of the proposed optimized algorithms are achieved a performance improvement of 71%, 59%, and 56% for C = A.B, C = A.B T, and C = A T.B separately compared with results that are achieved by implementing the latest Intel Math Kernel Library 2017 SGEMV subroutines. Additionally, a comparative study between single-core and multicore platforms has been examined. This work has a comparative study of using most popular compilers: Intel C++ compiler 17.0 over Microsoft Visual Studio C++ compiler 2015. This program displays the error until the number of columns of first matrix is equal to the number of rows of second matrix. Making parallel implementation guidelines of said algorithms, where the target architecture’s characteristics need to be taken into consideration when said algorithms are applied are presented. To multiply two matrices, the number of columns of first matrix should be equal to the number of rows to second matrix. This paper is different from other papers by concentrating on several main technique and the results therein. Our optimization is designed by using AVX instruction sets, OpenMP parallelization, and memory access optimization to overcome bandwidth limitations. Our goal is to accelerate and optimize square single-precision matrix multiplication from 2080 to 4512, i.e. AVX is supporting variety of applications such as image processing. Said prescript processes a chunk of data both individually and altogether. Each process puts their own multiplication into c, and returns. Each process receives a full copy of the empty array 'c'. This paper is focused on Intel Advanced Vector Extension (AVX) which has been borne of the modern developments in AMD processors and Intel itself. The multiplication does not need to take offset into account.
0 kommentar(er)
