Implementation of stereo matching using a high level compiler for parallel computing acceleration

Heterogeneous computing systems increase the performance of parallel computing in many domains of general purpose computing with CPU, GPU and other accelerators. With Hardware developments, the software developments like Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL) try to offer a simple and visual framework for parallel computing. But it turns out to be more difficult than programming on CPU platform for optimization of performance. For one kind of parallel computing application, there are different configurations and parameters for various hardware platforms. In this paper, we apply the Hybrid Multi-cores Parallel Programming (HMPP) to automatically generate tunable code for GPU platform and show the results of implementation of Stereo Matching with detailed comparison with C code version and manual CUDA version. The experimental results show that default and optimized HMPP have approximately the same performance and the better quality of disparity map compared with CUDA implementation.