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Wyszukujesz frazę "parallel algorithm" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
GPU-based parallel algorithm of interaction induced light scatering simulation in fluids
Autorzy:
Dawid, Aleksander
Powiązania:
https://bibliotekanauki.pl/articles/1954464.pdf
Data publikacji:
2019
Wydawca:
Politechnika Gdańska
Tematy:
GPGPU
CUDA
interaction induced phenomena
many body correlation function
parallel algorithm
Opis:
We parallelized the sequential algorithm of the four-body correlation function if eachcombination of two pairs(i, j)and(k, l) was averaged over the time in a separate calculation thread. The generator of pairs used as the input for this algorithm was also parallelized and connected with the 4-body correlation function calculations. We used our algorithm to accelerate extremely intensive calculations of the 4-body polarizability anisotropy correlation functions,which were very important to estimate the interaction induced light scattering spectrum. The resulting C code was used to test our algorithm on Graphics Processing Units (GPUs) with the Compute Unified Device Architecture (CUDA) technology from NVIDIA®Corporation. Asa result, we achieved 12 times the acceleration of the 4-body correlation function calculations in comparison to the Central Processing Unit (CPU) core. The peak performance of the GPU calculations was registered at the level of 19 times faster than the CPU core. We also found thatacceleration depended on the memory consumption. In the single precision mode, the relative error between the CPU and GPU calculations was found to be within 0.1%
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2019, 23, 1; 5-17
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
GPU-based tuning of quantum-inspired genetic algorithm for a combinatorial optimization problem
Autorzy:
Nowotniak, R.
Kucharski, J.
Powiązania:
https://bibliotekanauki.pl/articles/201268.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
quantum-inspired genetic algorithm
evolutionary computing
meta-optimization
parallel algorithms
GPGPU
Opis:
This paper concerns efficient parameters tuning (meta-optimization) of a state-of-the-art metaheuristic, Quantum-Inspired Genetic Algorithm (QIGA), in a GPU-based massively parallel computing environment (NVidia CUDATMtechnology). A novel approach to parallel implementation of the algorithm has been presented. In a block of threads, each thread transforms a separate quantum individual or different quantum gene; In each block, a separate experiment with different population is conducted. The computations have been distributed to eight GPU devices, and over 400× speedup has been gained in comparison to Intel Core i7 2.93GHz CPU. This approach allows efficient meta-optimization of the algorithm parameters. Two criteria for the meta-optimization of the rotation angles in quantum genes state space have been considered. Performance comparison has been performed on combinatorial optimization (knapsack problem), and it has been presented that the tuned algorithm is superior to Simple Genetic Algorithm and to original QIGA algorithm.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 2; 323-330
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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