high performance computing on graphics processing units: hgpu.org

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Complete PISO and SIMPLE solvers on Graphics Processing Units

Tadeusz Tomczak, Katarzyna Zadarnowska, Zbigniew Koza, Maciej Matyka, Aukasz Miroslaw

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Tags: Algorithms, Computer science, CUDA, Navier-Stokes equations, NSEs, nVidia, Tesla C2070

July 9, 2012 by hgpu

GPU-Optimized Molecular Dynamics Simulations

Tyson J. Lipscomb

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Tags: Biology, Biomolecules, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Pseudo-random number generators, Review, RNA, Tesla C2070, Thesis

July 8, 2012 by hgpu

Parallel Processing using FPGAs and GPUs

Abhijit Joshi

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, Tesla C2070, Thesis

July 3, 2012 by hgpu

On the Use of GPUs in Realizing Cost-Effective Distributed RAID

Aleksandr Khasymski, M. Mustafa Rafique, Ali R. Butt, Sudharshan S. Vazhkudai, Dimitrios S. Nikolopoulos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9500 GT, Storage system, Tesla C2070

July 3, 2012 by hgpu

FMM-based vortex method for simulation of isotropic turbulence on GPUs, compared with a spectral method

Rio Yokota, L. A. Barba

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Source codes

Tags: Computational Physics, Computer science, CUDA, Fast multipole method, FFT, Fluid dynamics, N-body simulation, Numerical Analysis, Numerical simulation, nVidia, Package, Spectral methods, Tesla C2070, Tesla M2050

June 21, 2012 by hgpu

Efficient simulations of long wave propagation and runup using a LBM approach on GPGPU hardware

Christian F. Janssen, Stephan T. Grilli, Manfred Krafczyk

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Tags: Algorithms, CUDA, Fluid dynamics, Lattice Boltzmann model, Numerical simulation, nVidia, Tesla C2070, Wave equation

June 19, 2012 by hgpu

Implementing density functional theory (DFT) methods on many-core GPGPU accelerators

Bishwajit Mohan Gosswami

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Tags: Chemistry, CUDA, Density-functional theory, nVidia, Tesla C2070, Thesis

June 19, 2012 by hgpu

Experiences with High-Level Programming Directives for Porting Applications to GPUs

Oscar Hernandez, Wei Ding, Barbara Chapman, Ramanan Sankaran, and Richard Graham

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Tags: Computer science, CUDA, nVidia, OpenCL, Tesla C2070

June 13, 2012 by hgpu

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

Federico Raimondo, Juan Kamienkowski, Mariano Sigman, Diego Fernandez Slezak

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Tags: Algorithms, Biology, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neuroscience, nVidia, nVidia GeForce GTX 560, Package, Tesla C2070

June 11, 2012 by hgpu

Solving the Ghost-Gluon System of Yang-Mills Theory on GPUs

Markus Hopfer, Reinhard Alkofer, Gundolf Haase

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Tags: Computational Physics, CUDA, High Energy Physics - Phenomenology, High Energy Physics - Theory, Integral equations, nVidia, nVidia GeForce GTX 560 Ti, OpenMPI, Physics, QCD, Tesla C2070

June 11, 2012 by hgpu

CUDA Kernel Design for GPU-Based Beam Dynamics Simulations

K. Amyx, J. Balasalle, I. Pogorelov, M. Borland, R. Soliday, Y.Wang

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Tags: Computer science, CUDA, nVidia, Tesla C2070

June 10, 2012 by hgpu

Autotuning Stencil-Based Computations on GPUs

Azamat Mametjanov, Daniel Lowell, Ching-Chen May, Boyana Norris

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Tags: Computer science, CUDA, Differential equations, nVidia, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C1060, Tesla C2070

June 9, 2012 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Complete PISO and SIMPLE solvers on Graphics Processing Units

GPU-Optimized Molecular Dynamics Simulations

Parallel Processing using FPGAs and GPUs

On the Use of GPUs in Realizing Cost-Effective Distributed RAID

FMM-based vortex method for simulation of isotropic turbulence on GPUs, compared with a spectral method

Efficient simulations of long wave propagation and runup using a LBM approach on GPGPU hardware

Implementing density functional theory (DFT) methods on many-core GPGPU accelerators

Experiences with High-Level Programming Directives for Porting Applications to GPUs

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

Solving the Ghost-Gluon System of Yang-Mills Theory on GPUs

CUDA Kernel Design for GPU-Based Beam Dynamics Simulations

Autotuning Stencil-Based Computations on GPUs

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Most viewed papers (last 30 days)