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Modern Graphics Processing Units (GPUs) with massive number of threads and many-core architecture support both graphics and general purpose computing. NVIDIA's compute unified device architecture (CUDA) takes advantage of parallel computing and utilizes the tremendous power of GPUs. The present study demonstrates a high performance computing (HPC) framework for a Monte-Carlo simulation to determine...
GPUs have emerged as general-purpose accelerators in high-performance computing (HPC) and scientific applications. However, the reliability characteristics of GPU applications have not been investigated in depth. While error propagation has been extensively investigated for non-GPU applications, GPU applications have a very different programming model which can have a significant effect on error propagation...
The use of GPU clusters for scientific applications in areas such as physics, chemistry and bioinformatics is becoming more widespread. These clusters frequently have different types of processing devices, such as CPUs and GPUs, which can themselves be heterogeneous. To use these devices in an efficient manner, it is crucial to find the right amount of work for each processor that balances the computational...
IT is a kind of science which develops fast. Nowadays the GPGPU (General Purpose Graphic Processing Unit) based technology is gaining on the importance. Few years ago, GPU was designed only to process the graphic data. The GPGPU enables computation on all kinds of data. This innovation brings computing performance huge boost. The GPGPU have been developed since year 2003. The technology was not available...
In this paper, several methods of optimizing parallel implementation of 2D FDTD algorithm are presented. Some practical problems occurring in real simulations are taken into consideration. Moreover, the presented methods are supported with appropriate tests and practical examples.
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