high performance computing on graphics processing units: hgpu.org

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Towards GPGPU Assisted Computing in Virtualized Environments

Thilo Schmitt, Alexander Weggerle, Christian Himpel, Peter Schulthess

Tags: ATI, ATI Stream, Computer science, CUDA, nVidia, OpenCL, Review, Virtualization

September 8, 2011 by hgpu

Implementing Independent Component Analysis in General-Purpose GPU Architectures

Jacquelyne Forgette, Renata Wachowiak-Smolikova, Mark Wachowiak

Tags: Algorithms, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 260, OpenCL, Signal processing

September 8, 2011 by hgpu

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Vishakha Gupta, Karsten Schwan, Niraj Tolia, Vanish Talwar, Parthasarathy Ranganathan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX, Operating systems, Task scheduling, Virtualization

September 7, 2011 by hgpu

Automatic CPU-GPU communication management and optimization

Thomas B. Jablin, Prakash Prabhu, James A. Jablin, Nick P. Johnson, Stephen R. Beard, David I. August

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Programming techniques

September 7, 2011 by hgpu

High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs

John E. Stone, Jan Saam, David J. Hardy, Kirby L. Vandivort, Wen-mei W. Hwu, Klaus Schulten

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Tags: Algorithms, Chemistry, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Performance, Programming techniques, Rendering, Visualization

September 7, 2011 by hgpu

CUDA-level performance with python-level productivity for Gaussian mixture model applications

H. Cook, E. Gonina, S. Kamil, G. Friedland, D. Patterson, A. Fox

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, OpenCL, Python

September 7, 2011 by hgpu

GPU-based asynchronous particle swarm optimization

Luca Mussi, Youssef S.G. Nashed, Stefano Cagnoni

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, Optimization, Performance

September 7, 2011 by hgpu

FPGA vs. multi-core CPUs vs. GPUs: hands-on experience with a sorting application

Cristian Grozea, Zorana Bankovic, Pavel Laskov

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia Quadro FX 5600, OpenMP, Sorting

September 7, 2011 by hgpu

Parallel packet classification using GPU co-processors

Alastair Nottingham, Barry Irwin

Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, Security

September 7, 2011 by hgpu

Parallel implementation of conjugate gradient method on graphics processors

Marcin Wozniak, Tomasz Olas, Roman Wyrzykowski

Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Sparse matrix, Tesla C1060

September 7, 2011 by hgpu

CUDA-based GPU Implementation of Hierarchical Belief Propagation for Fast Stereo Matching

Jae-Il Jung, Yo-Sung Ho

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 470

September 6, 2011 by hgpu

A CUDA-based parallel implementation of K-nearest neighbor algorithm

Shenshen Liang, Ying Liu, Cheng Wang, Liheng Jian

Tags: Algorithms, Computer science, CUDA, Nearest neighbour, nVidia, Optimization

September 5, 2011 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

Towards GPGPU Assisted Computing in Virtualized Environments

Implementing Independent Component Analysis in General-Purpose GPU Architectures

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Automatic CPU-GPU communication management and optimization

High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs

CUDA-level performance with python-level productivity for Gaussian mixture model applications

GPU-based asynchronous particle swarm optimization

FPGA vs. multi-core CPUs vs. GPUs: hands-on experience with a sorting application

Parallel packet classification using GPU co-processors

Parallel implementation of conjugate gradient method on graphics processors

CUDA-based GPU Implementation of Hierarchical Belief Propagation for Fast Stereo Matching

A CUDA-based parallel implementation of K-nearest neighbor algorithm

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)