Treffer: DENSE MATRIX-VECTOR MULTIPLICATION ON THE CUDA ARCHITECTURE.

Recently GPUs have acquired the ability to perform fast general purpose computation by running thousands of threads concurrently. This paper presents a new algorithm for dense matrix-vector multiplication on the NVIDIA CUDA architecture. The experiments are conducted on a PC with GeForce 8800GTX and 2.0 GHz Intel Xeon E5335 CPU. The results show that the proposed algorithm runs a maximum of 11.19 times faster than NVIDIA's BLAS library CUBLAS 1.1 on the GPU and 35.15 times faster than the Intel Math Kernel Library 9.1 on a single core x86 with SSE3 SIMD instructions. The performance of Jacobi's iterative method for solving linear equations, which includes the data transfer time between CPU and GPU, shows that the proposed algorithm is practical for real applications. [ABSTRACT FROM AUTHOR]

Copyright of Parallel Processing Letters is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)