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The vision of super computer at every desk can be realized by powerful andhighly parallel CPUs or GPUs or APUs. Graphics processors once specialized forthe graphics applications only, are now used for the highly computationalintensive general purpose applications. Very expensive GFLOPs and TFLOPperformance has become very cheap with the GPGPUs. Current work focuses mainlyon the highly parallel implementation of Matrix Exponentiation. MatrixExponentiation is widely used in many areas of scientific community rangingfrom highly critical flight, CAD simulations to financial, statisticalapplications. Proposed solution for Matrix Exponentiation uses OpenCL forexploiting the hyper parallelism offered by the many core GPGPUs. It employsmany general GPU optimizations and architectural specific optimizations. Thisexperimentation covers the optimizations targeted specific to the ScientificGraphics cards (Tesla-C2050). Heterogeneous Highly Parallel MatrixExponentiation method has been tested for matrices of different sizes and withdifferent powers. The devised Kernel has shown 1000X speedup and 44 foldspeedup with the naive GPU Kernel.

Heterogeneous Highly Parallel Implementation of Matrix Exponentiation Using GPU