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Titlebook: High Performance Computing; 9th Latin American C Philippe Navaux,Carlos J. Barrios H.,Ginés Guerrer Conference proceedings 2022 The Editor(

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發(fā)表于 2025-3-23 13:43:15 | 只看該作者
12#
發(fā)表于 2025-3-23 17:33:18 | 只看該作者
Franco Seveso,Raúl Marichal,Ernesto Dufrechou,Pablo Ezzattihich these methods function. Furthermore, this chapter overviews the most recent improvements and trends in apoptosis methods, and introduces . book content. The information provided is useful to novice scientists, as well as the more advanced scientist.
13#
發(fā)表于 2025-3-23 21:03:31 | 只看該作者
Conference proceedings 2022 Performance Computing Conference, is an international academic meeting aimed at providing a forum to foster the growth and strength of the High Performance Computing (HPC) community in Latin America and the Caribbean through the exchange and dissemination of new ideas, techniques, and research in HPC and its application areas.
14#
發(fā)表于 2025-3-24 00:54:01 | 只看該作者
15#
發(fā)表于 2025-3-24 03:18:33 | 只看該作者
,A Comparative Evaluation of?Parallel Programming Python Tools for?Particle-in-Cell on?Symmetric Mulizes. The results obtained consistently indicate that PyMP has the best performance, Multiprocessing showed a similar behavior but with longer execution times, and Torcpy did not properly scale when increasing the number of workers. Finally, a just-in-time (JIT) alternative was studied by using Numb
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發(fā)表于 2025-3-24 07:12:05 | 只看該作者
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發(fā)表于 2025-3-24 12:40:42 | 只看該作者
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發(fā)表于 2025-3-24 16:23:22 | 只看該作者
Time-Power-Energy Balance of , Kernels in Modern , type of operation. Our study focuses on the evaluation of . to address dense NLA operations. Specifically, in this work we explore and evaluate the available options for two of the most representative kernels of ., i.e. . and .. The experimental evaluation is carried out in an Alveo U50 accelerator
19#
發(fā)表于 2025-3-24 19:43:31 | 只看該作者
Improving Boundary Layer Predictions Using Parametric Physics-Aware Neural Networks,ation as predictor variables. Even though using these coefficients when training a PINN model is a common strategy for inverse problems, to the best of our knowledge we are the first to consider these coefficients for parametric direct problems. This finding may be instrumental in multiscale problem
20#
發(fā)表于 2025-3-25 00:00:16 | 只看該作者
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