期刊名称：International Journal of Distributed and Parallel Systems
印刷版ISSN：2229-3957
电子版ISSN：0976-9757
出版年度：2012
卷号：3
期号：2
DOI：10.5121/ijdps.2012.3209
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The vision of super computer at every desk can be realized by powerful and highly parallel CPUs or GPUs or APUs. Graphics processors once specialized for the graphics applications only, are now used for the highly computational intensive general purpose applications. Very expensive GFLOPs and TFLOP performance has become very cheap with the GPGPUs. Current work focuses mainly on the highly parallel implementation of Matrix Exponentiation. Matrix Exponentiation is widely used in many areas of scientific community ranging from highly critical flight, CAD simulations to financial, statistical applications. Proposed solution for Matrix Exponentiation uses OpenCL for exploiting the hyper parallelism offered by the many core GPGPUs. It employs many general GPU optimizations and architectural specific optimizations. This experimentation covers the optimizations targeted specific to the Scientific Graphics cards (Tesla–C2050). Heterogeneous Highly Parallel Matrix Exponentiation method has been tested for matrices of different sizes and with different powers. The devised Kernel has shown 1000X speedup and 44 fold speedup with the naive GPU Kernel.
