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The ability to process bigger chunks of data opens the door to a new level of computing power for tasks like scientific simulations and cinematic-style graphics.
GPUs have proved to be a powerful accelerator for intensive scientific simulations.
As a result, scientific simulations continue to investigate more and more complex systems.
Proxy applications are developed to simplify studying parallel performance of scientific simulations and to test potential solutions for performance problems.
Many large scale scientific simulations involve the time evolution of systems modelled as Partial Differential Equations (PDEs).
Recently, the graphical processing units (GPU), which is a device originally developed for rendering the graphical image, started to be used for scientific simulations.
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(2) From the various scientific model simulations used for predicting physical phenomena.
"Of course, qCraft isn't a perfect scientific simulation, but it's a fun way for players to experience a few parts of quantum mechanics outside of thought experiments or dense textbook examples".
Amazon lists the following examples: "web indexing, data mining, log file analysis, machine learning, financial analysis, scientific simulation, and bioinformatics research".
GPU offers a number of unique benefits to scientific simulation and visualization.
Stencil computations consume a major part of runtime in many scientific simulation codes.
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