Titlebook: Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI; 17th Smoky Mountains Jeffrey Nichols,Becky

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盤旋

Large-Scale Neural Solvers for Partial Differential EquationsHowever, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing. Scanning the parameters of the underlying model significantly increases the runtime as the simulations have to be cold-started for each parame

不幸的人

Integrating Deep Learning in Domain Sciences at Exascalerformance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogene

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敵手

adequate-intake

adequate-intake

Fulfilling the Promises of Lossy Compression for Scientific Applicationssion has been identified as one solution and has been tested for many use-cases: reducing streaming intensity (instruments), reducing storage and memory footprints, accelerating computation and accelerating data access and transfer. Ultimately, users’ trust in lossy compression relies on the preserv

Texture

DataStates: Towards Lightweight Data Models for Deep Learningarge number of alternative training and/or inference paths. However, with increasing model complexity and new training approaches that mix data, model, pipeline and layer-wise parallelism, this pattern is challenging to address in a scalable and efficient manner. To this end, this position paper adv

難解

deciduous