Computing the sparse matrix vector product using block-based kernels without zero padding on processors with AVX-512 instructions
نویسندگان
چکیده
منابع مشابه
Computing the Sparse Matrix Vector Product using Block-Based Kernels Without Zero Padding on Processors with AVX-512 Instructions
The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications from various fields. The High Performance Computing (HPC) community has therefore continuously invested a lot of effort to provide an efficient SpMV kernel on modern CPU architectures. It has been shown that block-based kernels are helpful to achieve high performance, but also that they are diffic...
متن کاملThe sparse matrix vector product on GPUs
The sparse matrix vector product (SpMV) is a paramount operation in engineering and scientific computing and, hence, has been a subject of intense research for long. The irregular computations involved in SpMV make its optimization challenging. Therefore, enormous effort has been devoted to devise data formats to store the sparse matrix with the ultimate aim of maximizing the performance. The G...
متن کاملA Fast Sparse Block Circulant Matrix Vector Product
In the context of computed tomography (CT), iterative image reconstruction techniques are gaining attention because high-quality images are becoming computationally feasible. They involve the solution of large systems of equations, whose cost is dominated by the sparse matrix vector product (SpMV). Our work considers the case of the sparse matrices being block circulant, which arises when takin...
متن کاملFast Sorting Algorithms using AVX-512 on Intel Knights Landing
The modern CPU’s design, which is composed of hierarchical memory and SIMD/vectorization capability, governs the potential for algorithms to be transformed into efficient implementations. The release of the AVX-512 changed things radically, and motivated us to search for an efficient sorting algorithm that can take advantage of it. In this paper, we describe the best strategy we have found, whi...
متن کاملPerformance Improvement of Sparse Matrix Vector Product on Vector Machines
Many applications based on finite element and finite difference methods include the solution of large sparse linear systems using preconditioned iterative methods. Matrix vector multiplication is one of the key operations that has a significant impact on the performance of any iterative solver. In this paper, recent developments in sparse storage formats on vector machines are reviewed. Then, s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PeerJ Computer Science
سال: 2018
ISSN: 2376-5992
DOI: 10.7717/peerj-cs.151