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https://www.readbyqxmd.com/read/28991781/a-heterogeneous-computing-accelerated-sce-ua-global-optimization-method-using-openmp-opencl-cuda-and-openacc
#1
Guangyuan Kan, Xiaoyan He, Liuqian Ding, Jiren Li, Ke Liang, Yang Hong
The shuffled complex evolution optimization developed at the University of Arizona (SCE-UA) has been successfully applied in various kinds of scientific and engineering optimization applications, such as hydrological model parameter calibration, for many years. The algorithm possesses good global optimality, convergence stability and robustness. However, benchmark and real-world applications reveal the poor computational efficiency of the SCE-UA. This research aims at the parallelization and acceleration of the SCE-UA method based on powerful heterogeneous computing technology...
October 2017: Water Science and Technology: a Journal of the International Association on Water Pollution Research
https://www.readbyqxmd.com/read/27925252/the-gpu-enabled-divide-expand-consolidate-ri-mp2-method-dec-ri-mp2
#2
Dmytro Bykov, Thomas Kjaergaard
We report porting of the Divide-Expand-Consolidate Resolution of the Identity second-order Møller-Plesset perturbation (DEC-RI-MP2) method to the graphic processing units (GPUs) using OpenACC compiler directives. It is shown that the OpenACC compiler directives implementation efficiently accelerates the rate-determining step of the DEC-RI-MP2 method with minor implementation effort. Moreover, the GPU acceleration results in a better load balance and thus in an overall scaling improvement of the DEC algorithm...
February 5, 2017: Journal of Computational Chemistry
https://www.readbyqxmd.com/read/26904094/comparison-of-acceleration-techniques-for-selected-low-level-bioinformatics-operations
#3
Daniel Langenkämper, Tobias Jakobi, Dustin Feld, Lukas Jelonek, Alexander Goesmann, Tim W Nattkemper
Within the recent years clock rates of modern processors stagnated while the demand for computing power continued to grow. This applied particularly for the fields of life sciences and bioinformatics, where new technologies keep on creating rapidly growing piles of raw data with increasing speed. The number of cores per processor increased in an attempt to compensate for slight increments of clock rates. This technological shift demands changes in software development, especially in the field of high performance computing where parallelization techniques are gaining in importance due to the pressing issue of large sized datasets generated by e...
2016: Frontiers in Genetics
https://www.readbyqxmd.com/read/24497950/fast-acceleration-of-2d-wave-propagation-simulations-using-modern-computational-accelerators
#4
Wei Wang, Lifan Xu, John Cavazos, Howie H Huang, Matthew Kay
Recent developments in modern computational accelerators like Graphics Processing Units (GPUs) and coprocessors provide great opportunities for making scientific applications run faster than ever before. However, efficient parallelization of scientific code using new programming tools like CUDA requires a high level of expertise that is not available to many scientists. This, plus the fact that parallelized code is usually not portable to different architectures, creates major challenges for exploiting the full capabilities of modern computational accelerators...
2014: PloS One
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