High performance computing (HPC) encompasses advanced computation over parallel processing, enabling faster execution of highly compute intensive tasks such as climate research, molecular modeling, physical simulations, cryptanalysis, geophysical modeling, automotive and aerospace design, financial modeling, data mining and more. High performance simulations require the most efficient compute p...

The use of Graphics Processing Units (GPUs) for high-performance computing has gained growing momentum in recent years. Unfortunately, GPU-programming platforms like CUDA are complex, user unfriendly, and increase the complexity of developing high-performance parallel applications. In addition, runtime systems that execute those applications often fail to fully utilize the parallelism of modern...

Graphic processing unit (GPU), which contains hundreds of processing cores, is becoming a popular device for high performance computation in multi-core era. With strictly computation regularity characteristic, specific algorithms are key challenges for performance speed-up. In this paper, we propose a parallel CUDA-Mikami routing algorithm on NVIDIA’s GPU. A 32-bit routing grid encoding is prop...

Graphics processing units (GPUs) are powerful computational devices tailored towards the needs of the 3-D gaming industry for high-performance, real-time graphics engines. Nvidia Corporation provides a programming language called CUDA for general-purpose GPU programming. Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces; if t...

This study presents a new method on 3D visualization in reservoir modeling system by using the computation power of modern programmable Graphics hardware (GPU). The proposed scheme is devised to achieve parallel processing of massive reservoir logging data. By taking advantage of the GPU's parallel processing capability, moreover, the performance of our scheme is discussed in comparison with th...

Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to s...

In this paper, for the reduction of the computation time of a deformable approach to pattern recognition, prototype-parallel displacement computation on GPUs (PPDC-GPU) is proposed. The displacement computation used in this study has the virtue of simplicity and consists of locally parallel processing, therefore it is suitable for the implementation on graphical processing units (GPUs). In the ...

We present an algorithm for solving the Longest Common Subsequence problem using graphics hardware acceleration. We identify a parallel memory access pattern which enables us to run efficiently on multiple layers of parallel hardware by matching each layer to the best sub-algorithm, which is determined using a mix of theoretical and experimental data including knowledge of the specific hardware...

We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues faced when merging fine-g...

Noise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. A common problem in imaging systems by using CMOS or CCD sensors is appearance of  the salt and pepper noise. This paper presents Cellular Automata (CA) framework for noise removal of distorted image by the salt an...