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Hardware-accelerated numerics

Hardware-accelerated numerics refers to the use of specialized hardware, such as Graphics Processing Units (GPUs) or other accelerators, to enhance the performance of numerical computations. GPUs, designed for graphics rendering, have been used for almost two decades in scientific and engineering applications due to their parallel processing capabilities. Heterogeneous computing involves employing a combination of different types of processors, such as CPUs and GPUs, to efficiently handle diverse computational tasks. In the context of numerical simulations, hardware acceleration aims to significantly speed up complex calculations by leveraging the parallel processing power of specialized hardware components.

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What do we do?

Our scientists have a long tradition for utilizing emerging computer hardware in computational modelling. For instance, starting in the early 2000s, we were among the pioneers in GPU computing, using programmable graphics cards for solving systems of conservation and balance laws (see, e.g., our tutorial paper from 2007). We still maintain a high level of expertise in heterogeneous  computing (combined use of multi- and many-core devices), as exemplified in our GPU Ocean software, and also have a certain proficiency in visual and cloud-based computing.  (You can find some details on our old webpages).

Software

GPU Ocean

GPU Ocean

A GPU-accelerated simulation framework for running large ensembles of simplified ocean models for real-world domains.

Jutul

Jutul

Experimental Julia framework for fully differentiable multiphysics simulators based on implicit finite-volume methods with automatic differentiation.