Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)
نویسندگان
چکیده
منابع مشابه
Accurate cross-architecture performance modeling for sparse matrix-vector multiplication (SpMV) on GPUs
This paper presents an integrated analytical and profile-based cross-architecture performance modeling tool to specifically provide inter-architecture performance prediction for Sparse Matrix-Vector Multiplication (SpMV) on NVIDIA GPU architectures. To design and construct the tool, we investigate the interarchitecture relative performance for multiple SpMV kernels. For a sparse matrix, based o...
متن کاملOptimizing Sparse Matrix-Vector Multiplication on GPUs
We are witnessing the emergence of Graphics Processor units (GPUs) as powerful massively parallel systems. Furthermore, the introduction of new APIs for general-purpose computations on GPUs, namely CUDA from NVIDIA, Stream SDK from AMD, and OpenCL, makes GPUs an attractive choice for high-performance numerical and scientific computing. Sparse Matrix-Vector multiplication (SpMV) is one of the mo...
متن کاملInvestigating the Effects of Hardware Parameters on Power Consumptions in SPMV Algorithms on Graphics Processing Units (GPUs)
Although Sparse matrix-vector multiplication (SPMVs) algorithms are simple, they include important parts of Linear Algebra algorithms in Mathematics and Physics areas. As these algorithms can be run in parallel, Graphics Processing Units (GPUs) has been considered as one of the best candidates to run these algorithms. In the recent years, power consumption has been considered as one of the metr...
متن کاملAccelerating Sparse Matrix Vector Multiplication on Many-Core GPUs
Many-core GPUs provide high computing ability and substantial bandwidth; however, optimizing irregular applications like SpMV on GPUs becomes a difficult but meaningful task. In this paper, we propose a novel method to improve the performance of SpMV on GPUs. A new storage format called HYB-R is proposed to exploit GPU architecture more efficiently. The COO portion of the matrix is partitioned ...
متن کاملImplementing Blocked Sparse Matrix-Vector Multiplication on NVIDIA GPUs
We discuss implementing blocked sparse matrix-vector multiplication for NVIDIA GPUs. We outline an algorithm and various optimizations, and identify potential future improvements and challenging tasks. In comparison with previously published implementation, our implementation is faster on matrices having many high fill-ratio blocks but slower on matrices with low number of non-zero elements per...
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: Electronics
سال: 2020
ISSN: 2079-9292
DOI: 10.3390/electronics9101675