A talented CPU-to-GPU memory mapping technique

In order to fast effective analysis of large systems, high performance computing (HPC) is essential. NVIDIA Compute Unified Device Architecture (CUDA)-assisted central processing unit (CPU) and graphics processing unit (GPU) computing platform has proven its potential to be used for HPC supports. In CPU/GPU computing, original data and instructions are copied from CPU-main-memory to GPU-global-memory. Inside GPU, it would be beneficial to keep the data into shared memory (shared only by the threads of that block) than in the global memory (shared by all threads). However, GPU shared memory is much smaller than GPU global memory (for Fermi Tesla C2075, total shared memory per block is 48 KB and total global memory is 5.6 GB). In this paper, we introduce a CPU-main-memory to GPU-global-memory mapping technique to improve the GPU/overall system performance by increasing the effectiveness of GPU shared memory. Experimental results, from solving Laplace´s equation for 512×512 matrix using Fermi and Kepler cards, show that proposed CPU-to-GPU memory mapping technique help decrease the overall execution time by more than 75%.