Parallel matrix multiplication for various implementations

It has become increasingly common to see that supercomputing applications harness the massive parallelism of graphics cards to speed up computations. In this study, an analysis concerning to the time necessity for four different implementations of parallel matrix multiplication is presented. The execution time of parallel matrix multiplications in Compute Unified Device Architecture (CUDA) can be increased to about 10 times than Matlab implementation, 100 times than Java Thread, 300 times than C++ by using duo core Central Processing Unit (CPU) and 600 times than C++ by using single core CPU respectively by our method, as compared with using the fastest tools of GPU-only case or CPU-only case. The goal of this study is to show how to offload parallel computations to the graphics card, when it is necessary, and to give some idea of how to think about code running in the massively parallel environment.