high performance computing on graphics processing units: hgpu.org

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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

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

Lukas Polok

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Tags: Algorithms, Computer science, Differential equations, Factorization, FEM, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 680, OpenCL, Partial differential equations, PDEs, Sparse matrix, Tesla K40, Thesis

December 19, 2017 by hgpu

GPU accelerated computation of Polarized Subsurface BRDF for Flat Particulate Layers

Charly Collin, Sumanta Pattanaik

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Tags: Computer science, Differential equations, Graphics, nVidia, nVidia GeForce GTX 580, OpenCL, Rendering

July 22, 2017 by hgpu

Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models

Saptarshi Das, Xi Chen, Michael P. Hobson

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Tags: CUDA, Differential equations, Earth and Space Sciences, Geophysics, Heterogeneous systems, nVidia, Partial differential equations, PDEs, Seismology, Spectral methods, Tesla C2075, Tesla K20, Visualization, Wave equation

May 18, 2017 by hgpu

Accelerating solutions of PDEs with GPU-based swept time-space decomposition

Daniel J Magee, Kyle E Niemeyer

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Tags: Algorithms, cfd, Computational Physics, CUDA, Differential equations, Fluid dynamics, nVidia, Package, Partial differential equations, PDEs, Physics, Tesla K40

May 11, 2017 by hgpu

Speeding up a few orders of magnitude the Jacobi method: high order Chebyshev-Jacobi over GPUs

J.E. Adsuara, I. Cordero-Carrion, P. Cerda-Duran, M.A. Aloy

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Tags: CUDA, Differential equations, Finite difference, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, Partial differential equations, PDEs, Tesla K40

May 2, 2017 by hgpu

OpenCL-Based FPGA Accelerator for 3D FDTD with Periodic and Absorbing Boundary Conditions

Hasitha Muthumala Waidyasooriya, Tsukasa Endo, Masanori Hariyama, Yasuo Ohtera

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Tags: Computer science, Differential equations, FDTD, Finite difference, Finite-difference time-domain, FPGA, OpenCL, Partial differential equations, PDEs

April 26, 2017 by hgpu

A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms

William R. Saunders, James Grant, Eike H. Muller

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Tags: Algorithms, Biology, Chemistry, Code generation, Computer science, CUDA, Differential equations, DSL, Molecular dynamics, nVidia, Package, Partial differential equations, PDEs, Physics, Python, Tesla K20

April 15, 2017 by hgpu

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Christopher P. Stone, Kyle E. Niemeyer

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Tags: Computational Physics, Computer science, Differential equations, Intel Xeon Phi, nVidia, ODEs, OpenCL, Ordinary differential equations, Physics, Tesla K20

August 28, 2016 by hgpu

Finite Element Integration with Quadrature on the GPU

Matthew G. Knepley, Karl Rupp, Andy R. Terrel

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Tags: AMD FirePro W9100, ATI, Computer science, Differential equations, Mathematical Software, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 750 Ti, OpenCL, Partial differential equations, PDEs, Tesla K20

July 16, 2016 by hgpu

pyJac: analytical Jacobian generator for chemical kinetics

Kyle E. Niemeyer, Nicholas J. Curtis, Chih-Jen Sung

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Tags: Algorithms, Computational Physics, CUDA, Differential equations, Finite difference, nVidia, Package, Physics, Python, Tesla C2075

May 17, 2016 by hgpu

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qcgpu-rust: A High Performance, Hardware accelerated, Quantum computer simulator in Rust

Simulating Quantum Computers Using OpenCL

hStreams-benchmark: The benchmark of Intel hStreams

Auto-tuning Streamed Applications on Intel Xeon Phi

Parabix

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

HLS4ML

Fast inference of deep neural networks in FPGAs for particle physics

cytonRL: reinforcement learning, deep Q-network, double DQN, dueling DQN, prioritized experience replay

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

DLL: Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

DLL: A Blazing Fast Deep Neural Network Library

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The 3rd IEEE International Conference on Image, Vision and Computing (ICIVC), 2018

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high performance computing on graphics processing units: hgpu.org

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

GPU Accelerated Finite Element Assembly with Runtime Compilation

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

GPU accelerated computation of Polarized Subsurface BRDF for Flat Particulate Layers

Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models

Accelerating solutions of PDEs with GPU-based swept time-space decomposition

Speeding up a few orders of magnitude the Jacobi method: high order Chebyshev-Jacobi over GPUs

OpenCL-Based FPGA Accelerator for 3D FDTD with Periodic and Absorbing Boundary Conditions

A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Finite Element Integration with Quadrature on the GPU

pyJac: analytical Jacobian generator for chemical kinetics

Recent source codes

glow: Compiler for Neural Network hardware accelerators

GENESYS: GPU system calls from on ROCm

SR-NMT: Implementation in pytorch of SR-NMT

dlbench: Benchmarking State-of-the-Art Deep Learning Software Tools

qcgpu-rust: A High Performance, Hardware accelerated, Quantum computer simulator in Rust

hStreams-benchmark: The benchmark of Intel hStreams

Parabix

HLS4ML

cytonRL: reinforcement learning, deep Q-network, double DQN, dueling DQN, prioritized experience replay

DLL: Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

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International Conference on Biomedicine & Pharmacotherapy, 2018

8th International Workshop on Computer Science and Engineering (WCSE’18), 2018

International Conference on Robotics, Artificial Intelligence, Automation and Mechatronics, 2018

The 3rd IEEE International Conference on Image, Vision and Computing (ICIVC), 2018

The 2th International Conference on Robotics and Automation Sciences (ICRAS), 2018