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SciAI4Industry – Solving PDEs for industry-scale problems with deep learning

Philipp A. Witte, Russell J. Hewett, Kumar Saurabh, AmirHossein Sojoodi, Ranveer Chandra

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Tags: AI, Cloud, Computational Physics, Computer science, Deep learning, Differential equations, Neural networks, nVidia, nVidia A100, nVidia DGX-A100, Partial differential equations, PDEs

November 27, 2022 by hgpu

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang, Sanjay Choudhry

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Tags: cfd, CUDA, Fluid dynamics, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia A100, Partial differential equations, PDEs, Physics, Tesla V100, Video

December 20, 2020 by hgpu

Bempp-cl: A fast Python based just-in-time compiling boundary element library

Timo Betcke, Matthew W. Scroggs

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Tags: Computer science, Differential equations, Integral equations, OpenCL, Package, Partial differential equations, PDEs, Python

October 11, 2020 by hgpu

Unsupervised Deep Learning of Incompressible Fluid Dynamics

Nils Wandel, Michael Weinmann, Reinhard Klein

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Tags: Deep learning, Differential equations, Fluid dynamics, Fluid simulation, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Partial differential equations, PDEs, PyTorch

June 21, 2020 by hgpu

SYCL Code Generation for Multigrid Methods

Stefan Groth, Christian Schmitt, Jürgen Teich, and Frank Hannig

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Tags: Code generation, Computer science, Differential equations, OpenCL, Package, Partial differential equations, PDEs

June 16, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

Efficient Implementation and Optimization of Geometric Multigrid Operations in the LIFT Framework

Martin Lucke

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Tags: Computer science, Differential equations, nVidia, nVidia GeForce GTX 1080, OpenCL, Partial differential equations, PDEs, performance portability, Thesis

January 27, 2019 by hgpu

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

Denis Demidov

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Tags: Computer science, CUDA, Differential equations, Mathematical Software, multigrid, nVidia, OpenCL, OpenMP, Package, Partial differential equations, PDEs, Tesla K80

November 18, 2018 by hgpu

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

Andrew Gloster, Lennon O Naraigh, Khang Ee Pang

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Tags: Computational Physics, CUDA, Differential equations, nVidia, nVidia GeForce GTX Titan X, Package, Partial differential equations, PDEs, Physics

July 21, 2018 by hgpu

Computing dynamics of thin films via large scale GPU-based simulations

Michael-Angelo Y.-H. Lam, Linda J. Cummings, Lou Kondic

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Tags: cfd, CUDA, Differential equations, Fluid dynamics, Liquid crystals, nVidia, nVidia GeForce GTX Titan X, Partial differential equations, PDEs

June 28, 2018 by hgpu

Long-time Simulations with Complex Code Using Multiple Nodes of Intel Xeon Phi Knights Landing

Jonathan S. Graf, Matthias K. Gobbert, Samuel Khuvis

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Tags: Computer science, Differential equations, Intel Xeon Phi, MPI, OpenMP, Partial differential equations, PDEs

February 17, 2018 by hgpu

GPU Accelerated Finite Element Assembly with Runtime Compilation

Tao Cui, Xiaohu Guo, Hui Liu

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Tags: Computer science, CUDA, Differential equations, FEM, Finite element method, Mathematical Software, Numerical Analysis, nVidia, Partial differential equations, PDEs, Symbolic Computation, Tesla K20, Tesla M2090, Tesla V100

February 15, 2018 by hgpu

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FDAS: Fourier Domain Acceleration Search, FPGA-accelerated with OpenCL

Pulsar search acceleration using FPGAs and OpenCL templates

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Reeber: a library for shared- and distributed-memory parallel computation of merge trees

Fast Merge Tree Computation via SYCL

pocl: Portable Computing Language

Efficient OpenCL system integration of non-blocking FPGA accelerators

OneFlow: a deep learning framework designed to be user-friendly, scalable and efficient

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

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high performance computing on graphics processing units: hgpu.org

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Bempp-cl: A fast Python based just-in-time compiling boundary element library

Unsupervised Deep Learning of Incompressible Fluid Dynamics

SYCL Code Generation for Multigrid Methods

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Efficient Implementation and Optimization of Geometric Multigrid Operations in the LIFT Framework

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

Computing dynamics of thin films via large scale GPU-based simulations

Long-time Simulations with Complex Code Using Multiple Nodes of Intel Xeon Phi Knights Landing

GPU Accelerated Finite Element Assembly with Runtime Compilation

Recent source codes

FDAS: Fourier Domain Acceleration Search, FPGA-accelerated with OpenCL

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Reeber: a library for shared- and distributed-memory parallel computation of merge trees

pocl: Portable Computing Language

OneFlow: a deep learning framework designed to be user-friendly, scalable and efficient

Python Suite for Advanced General Ensemble Simulations

GPU load-balancing library for regular and irregular computations

Shine compiler for the RISE language

Cramming the training of a (BERT-type) language model into limited compute

oneDNN: oneAPI Deep Neural Network Library

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