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Graphics processor units (GPUs), such as the AMD FireStream series, offer a tremendous computing power that is frequently an order of magnitude larger than even the most modern multi-core CPUs, making them an attractive platform for high performance computing due to their relative cheapness compared with conventional PC clusters. General-purpose computing on GPUs (GPGPU) is becoming popular in HPC because of its high peak performance. This paper investigates current technology that enables a GPU to accelerate HPC applications. As a representative kernel program in HPC, the speed of matrix multiplication plays an important role in the whole performance of the application. We introduce a new parallel algorithm to accelerate this operation. Comparing the speed of the computation through the CPU and the GPU, the result demonstrates that calculations are preformed considerably faster through the GPU than through the CPU.