Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
