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The paper presents design, implementation and tuning of a hybrid parallel OpenMP+CUDA code for computation of similarity between pairs of a large number of multidimensional vectors. The problem has a wide range of applications, and consequently its optimization is of high importance, especially on currently widespread hybrid CPU+GPU systems targeted in the paper. The following are presented and tested...
The computational power and the physical memory size of a single GPU device are often insufficient for large-scale problems. Using CUDA, the user must explicitly partition such problems into several tasks repeating the data transfer and kernel execution. To use multiple GPUs, explicit device switching is also needed. Furthermore, low-level hand optimizations such as load balancing and determining...
Although General Purpose computation on GPU (GPGPU) is widely used for high-performance computing, standard programming frameworks such as CUDA and Open CL are still difficult to use. They require low-level specifications and hand-optimization is a large burden. Therefore we are developing an easier framework named MESI-CUDA. Based on a virtual shared memory model, MESI-CUDA hides low level memory...
Recently there was an active development of parallel programming methods concerning implementation of general-purpose algorithms on graphical processing units (GPUs). Using this specialized hardware allows increasing performance significantly, but requires low-level programming and understanding details of underlying hardware and software platform. Therefore there is a need for automating development...
Recent years have seen a trend in using graphic processing units (GPU) as accelerators for general-purpose computing. The inexpensive, single-chip, massively parallel architecture of GPU has evidentially brought factors of speedup to many numerical applications. However, the development of a high-quality GPU application is challenging, due to the large optimization space and complex unpredictable...
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