Titlebook: Neural Information Processing; 27th International C Haiqin Yang,Kitsuchart Pasupa,Irwin King Conference proceedings 2020 Springer Nature Sw

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Muhammad Nabeel Asim,Muhammad Ali Ibrahim,Muhammad Imran Malik,Andreas Dengel,Sheraz Ahmedto select an appropriate combination of algorithms for the hardware and problem at hand. Since a manual configuration of a multigrid solver is tedious and does not scale for a large number of different hardware platforms, we have been developing a code generator that automatically generates a multig

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Philip Mehrgardt,Matloob Khushi,Anusha Withana,Simon Poonportable as well as easily composable is getting more and more challenging. Additionally, code that has been aggressively optimized for certain execution platforms is usually not easily portable to others without either losing a great share of performance or investing many hours by re-applying optim

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ations) which general-purpose parallel file systems (PFS) were not optimized for. Burst-buffer file systems aim to solve that challenge by spanning an ad hoc file system across node-local flash storage at compute nodes to relief the PFS from such access patterns. However, existing burst-buffer file

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Nida Itrat Abbasi,Sony Saint-Auret,Junji Hamano,Anumita Chaudhury,Anastasios Bezerianos,Nitish V. Thions. Unfortunately, these simulations suffer from the curse of dimensionality due to the five-plus-one-dimensional nature of the equations. Hence, we propose a sparse grid approach based on the sparse grid combination technique which splits the simulation grid into multiple smaller grids of varying

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Zhen Zhang,Junhai Xu,Luqi Cheng,Cheng Chen,Lingzhong Fansist in generating solutions with minimal user involvement. Parallel computation is becoming indispensable in solving the large-scale problems that arise in science and engineering applications. Yet the use of parallel computation is limited by the high cost of developing the needed software. To ove