AbstractIn this paper we will present a detailed study on tuning double-precision matrix-matrix multiplication (DGEMM) on the Intel Xeon E5-2680 CPU. We selected an optimal algorithm from the instruction set perspective as well software tools optimized for Intel Advance Vector Extensions (AVX). Our optimizations included the use of vector memory operations, and AVX instructions. Our proposed algorithm achieves a performance improvement of 33% compared to the latest results achieved using the Intel Math Kernel Library DGEMM subroutine
Graphics hardware's performance is advancing much faster than the performance of conventional microp...
Efficient implementation of deep neural networks (DNNs) on CPU-based systems is critical owing to th...
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in are...
AbstractIn this paper we will present a detailed study on tuning double-precision matrix-matrix mult...
AbstractThis paper presents results of our study on double-precision general matrix-matrix multiplic...
In heterogeneous systems that include CPUs and GPUs, the data transfers between these components pla...
The performance of a parallel matrix-matrix-multiplication routine with the same functionality as DG...
his paper presents the design and implementation of a highly efficient Double-precision General Matr...
This paper examines how to write code to gain high performance on modern computers as well as the im...
학위논문 (석사)-- 서울대학교 대학원 : 자연과학대학 수리과학부, 2018. 2. 신동우.This paper presents the design and implementation...
In order to utilize the tremendous computing power of grpahics hardware and to automatically adapt t...
Double-precision general matrix multiplication (DGEMM) is an essential kernel for measuring the pote...
Abstract. Moore’s Law suggests that the number of processing cores on a single chip increases expone...
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in are...
The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra l...
Graphics hardware's performance is advancing much faster than the performance of conventional microp...
Efficient implementation of deep neural networks (DNNs) on CPU-based systems is critical owing to th...
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in are...
AbstractIn this paper we will present a detailed study on tuning double-precision matrix-matrix mult...
AbstractThis paper presents results of our study on double-precision general matrix-matrix multiplic...
In heterogeneous systems that include CPUs and GPUs, the data transfers between these components pla...
The performance of a parallel matrix-matrix-multiplication routine with the same functionality as DG...
his paper presents the design and implementation of a highly efficient Double-precision General Matr...
This paper examines how to write code to gain high performance on modern computers as well as the im...
학위논문 (석사)-- 서울대학교 대학원 : 자연과학대학 수리과학부, 2018. 2. 신동우.This paper presents the design and implementation...
In order to utilize the tremendous computing power of grpahics hardware and to automatically adapt t...
Double-precision general matrix multiplication (DGEMM) is an essential kernel for measuring the pote...
Abstract. Moore’s Law suggests that the number of processing cores on a single chip increases expone...
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in are...
The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra l...
Graphics hardware's performance is advancing much faster than the performance of conventional microp...
Efficient implementation of deep neural networks (DNNs) on CPU-based systems is critical owing to th...
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in are...