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Very good scalability on massive computations is demonstrated using strong scaling study up to 1024 cores.
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Cloud computing integrates several scientific models and distributes resources over the network, thus, resolving the issue of massive computations being done on only one site.
Nowadays massive computation is required just to reduce and analyze experimental data, and simulations and computational explorations are employed in fields as diverse as climate modeling and research mathematics.
This massive computation task is generally considered to be a paradigm example of experimental mathematics.
The applicability of such simulations is often limited by the time required for calculation, but the use of massive parallelism to accelerate these computations is typically limited by inter-processor communication requirements.
However, tachometer-based OA methods require the installation of an external sensor to obtain rotating speed, whereas tacholess OA methods are usually complicated and require massive computation cost.
(2) Embarrassingly parallelism: Under embarrassingly parallel (massive parallelism) scenarios, the computation is naturally independent among subtasks, but the execution time of each subtask varies.
All computations were performed in Matlab.
Computations were performed at the Stanford Research Computing Center.
The computations are based on 2017 data.
All computations were implemented in MATLAB.
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