high performance computing on graphics processing units: hgpu.org

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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

A Problem-Based Learning Approach to GPU Computing

Robert Geist, Joshua A. Levine, James Westall

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Tags: Computer science, Differential equations, nVidia, nVidia GeForce GTX Titan X, OpenCL, Partial differential equations, PDEs

November 29, 2015 by hgpu

Embedded Ensemble Propagation for Improving Performance, Portability and Scalability of Uncertainty Quantification on Emerging Computational Architectures

E. Phipps, M. D'Elia, H. C. Edwards, M. Hoemmen, J. Hu, S. Rajamanickam

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Tags: Computer science, CUDA, Differential equations, nVidia, OpenMP, Partial differential equations, PDEs, Tesla K20

November 24, 2015 by hgpu

Evaluating 3-D Stencil codes on Intel Xeon Phi: Limitations and Trade-offs

Mario Hernandez, Juan M. Cebrian, Jose M. Cecilia, Jose M. Garcia

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Tags: Algorithms, Computer science, Differential equations, Intel Xeon Phi, OpenMP, Partial differential equations, PDEs, Wave equation

November 11, 2015 by hgpu

Evaluation of the Intel Xeon Phi and NVIDIA K80 as accelerators for two-dimensional panel codes

Lukas Einkemmer

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Tags: Computer science, CUDA, Differential equations, Intel Xeon Phi, nVidia, Partial differential equations, PDEs, Performance, Tesla K80

November 10, 2015 by hgpu

Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

Hector Dearman

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Tags: Benchmarking, Computer science, CUDA, Differential equations, FEM, Finite element method, nVidia, nVidia GRID K520, OpenCL, Partial differential equations, PDEs, Thesis

November 3, 2015 by hgpu

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

S. Cuomo, A. Galletti, G. Giunta, L. Marcellino

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Tags: Algorithms, Computer science, CUDA, Differential equations, GPU cluster, Heterogeneous systems, nVidia, nVidia Quadro K5000, Optical flow, Partial differential equations, PDEs

June 24, 2015 by hgpu

A Parallel Implementation of the Galerkin Method for Solving Partial Differential Equations on a Triangular Mesh

Rachel N. Hess

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Tags: Differential equations, FEM, Finite element method, Mathematics, OpenCL, Partial differential equations, PDEs, PyOpenCL, Thesis, Triangular meshes

June 7, 2015 by hgpu

Fireflies: New software for interactively exploring dynamical systems using GPU computing

Robert Merrison-Hort

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Tags: Computer science, Differential equations, Mathematical Software, Mathematics, nVidia, nVidia GeForce GTX 460, ODEs, OpenCL, Ordinary differential equations, Package, Python

May 7, 2015 by hgpu

High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study

Gloria Ortega Lopez

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Tags: Computer science, CUDA, Differential equations, FEM, Finite difference, Finite element method, GPU cluster, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C2050, Tesla M2070, Tesla M2090, Thesis, Tomography

March 30, 2015 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

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

A Problem-Based Learning Approach to GPU Computing

Embedded Ensemble Propagation for Improving Performance, Portability and Scalability of Uncertainty Quantification on Emerging Computational Architectures

Evaluating 3-D Stencil codes on Intel Xeon Phi: Limitations and Trade-offs

Evaluation of the Intel Xeon Phi and NVIDIA K80 as accelerators for two-dimensional panel codes

Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

A Parallel Implementation of the Galerkin Method for Solving Partial Differential Equations on a Triangular Mesh

Fireflies: New software for interactively exploring dynamical systems using GPU computing

High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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