high performance computing on graphics processing units: hgpu.org

Parallel computing can offer an enormous advantage regarding the performance for very large applications in almost any field: scientific computing, computer vision, databases, data mining, and economics. GPUs are high performance many-core processors that can obtain very high FLOP rates. Since the first idea of using GPU for general purpose computing, things have evolved and now there are several approaches to GPU programming: CUDA from NVIDIA and Stream from AMD. CUDA is now a popular programming model for general purpose computations on GPU for C/C++ programmers. A great number of applications were ported to CUDA programming model and they obtain speedups of orders of magnitude comparing to optimized CPU implementations. In this paper we present a Java linear algebra library (Java-CUDA) that uses CUBLAS for performing numerical computation on GPU. . Our tests show that the performance of the Java-CUDA library is two orders of magnitude better than the pure Java implementation and 10-20 times better that of the optimized JAVA and CBLAS run on CPU and approximately equal with the C program that call CUBLAS.