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Titlebook: High Performance Computing; ISC High Performance Heike Jagode,Hartwig Anzt,Hatem Ltaief Conference proceedings 2020 Springer Nature Switzer

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樓主: 和善
31#
發(fā)表于 2025-3-26 21:05:09 | 只看該作者
32#
發(fā)表于 2025-3-27 02:40:06 | 只看該作者
Static Analysis to Enhance Programmability and Performance in OmpSs-2uting (HPC) applications. Recent studies show that cutting-edge Real-Time applications, such as those for unmanned vehicles, can successfully exploit these models. In this scenario, OpenMP is a de facto standard for HPC, and is being studied for Real-Time systems due to its time-predictability and d
33#
發(fā)表于 2025-3-27 07:17:30 | 只看該作者
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發(fā)表于 2025-3-27 09:47:59 | 只看該作者
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發(fā)表于 2025-3-27 16:06:19 | 只看該作者
Complete Deep Computer-Vision Methodology for Investigating Hydrodynamic Instabilitiesation of said instabilities is concerned with highly non-linear dynamics. Currently, three main methods are used for understanding of such phenomena – namely analytical and statistical models, experiments, and simulations – and all of them are primarily investigated and correlated using human expert
36#
發(fā)表于 2025-3-27 19:27:42 | 只看該作者
37#
發(fā)表于 2025-3-28 01:59:17 | 只看該作者
Unsupervised Learning of Particle Image Velocimetryperimental fluid dynamics and the remote sensing of environmental flows. Recently, the development of deep learning based methods has inspired new approaches to tackle the PIV problem. These supervised learning based methods are driven by large volumes of data with ground truth training information.
38#
發(fā)表于 2025-3-28 04:13:56 | 只看該作者
39#
發(fā)表于 2025-3-28 08:44:37 | 只看該作者
Parameter Identification of RANS Turbulence Model Using Physics-Embedded Neural Network Therefore even a modest improvement of the turbulence model can significantly reduce the overall cost of a three-dimensional, time-dependent simulation. In this paper we demonstrate a novel method to find the optimal parameters in the Reynolds-averaged Navier–Stokes (RANS) turbulence model using hi
40#
發(fā)表于 2025-3-28 13:32:07 | 只看該作者
Investigating the Overhead of the REST Protocol When Using Cloud Services for HPC Storage offer to move complete HPC workloads into the Cloud, this is limited by the massive demand of computing power alongside storage resources typically required by I/O intensive HPC applications. It is widely believed that HPC hardware and software protocols like MPI yield superior performance and lowe
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