high performance computing on graphics processing units: hgpu.org

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Parallel Iteration to the Radiative Transport in Inhomogeneous Media with Bootstrapping

Laszlo Szirmay-Kalos, Gabor Liktor, Tamas Umenhoffer, Balazs Toth, Shree Kumar, Glenn Lupton

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Tags: CUDA, GPU cluster, Image processing, Image reconstruction, Monte Carlo simulation, nVidia, nVidia GeForce 8800 GTX

May 31, 2011 by hgpu

Distributed time, conservative parallel logic simulation on GPUs

Bo Wang, Yuhao Zhu, Yangdong Deng

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Tags: Computer science, CUDA, Electronic design automation, Hardware Architecture, nVidia, nVidia GeForce GTX 280, Performance

May 31, 2011 by hgpu

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

K. Dohi, K. Benkridt, C. Ling, T. Hamada, Y. Shibata

Tags: Bioinformatics, Biology, CUDA, nVidia, Sequence alignment, Smith-Waterman algorithm

May 31, 2011 by hgpu

Connected component identification and cluster update on GPU

Martin Weigel

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, Ising model, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Physics, Statistical Mechanics, Tesla M2070

May 31, 2011 by hgpu

A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality

Christoph Muller, Steffen Frey, Magnus Strengert, Carsten Dachsbacher, Thomas Ertl

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia Quadro FX 5600, Performance

May 30, 2011 by hgpu

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Graphics Processing Unit Computing

Alhadi Bustamam, Kevin Burrage, Nicholas A. Hamilton

Tags: Bioinformatics, Biology, CUDA, MPI, nVidia, Sparse matrix

May 30, 2011 by hgpu

High performance comparison-based sorting algorithm on many-core GPUs

Xiaochun Ye, Dongrui Fan, Wei Lin, Nan Yuan, Paolo Ienne

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX, Performance, Sorting

May 30, 2011 by hgpu

Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

Shixun Zhang, Shinichi Yamagiwa, Masahiko Okumura, Seiji Yunoki

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Tags: Computational Physics, Condensed matter, CUDA, nVidia, Performance, Physics, Tesla C2050

May 30, 2011 by hgpu

GPU-based parallel-beam and cone-beam forward- and backprojection using CUDA

Michael Knaup, Sven Steckmann, Marc Kachelriess

Tags: CUDA, Image processing, Image reconstruction, Medicine, nVidia, Tomography

May 29, 2011 by hgpu

Performance comparison of GPU and FPGA architectures for the SVM training problem

Markos Papadonikolakis, Christos-Savvas Bouganis, George Constantinides

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Tags: Computer science, CUDA, FPGA, Machine learning, nVidia, nVidia GeForce 8500 GT, Performance

May 29, 2011 by hgpu

GPU-Based Ray Tracing of Splats

Rhushabh Goradia, Sriram Kashyap, Parag Chaudhuri, Sharat Chandran

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 275, Raytracing, Rendering

May 29, 2011 by hgpu

Fast GPU-Based CT Reconstruction using the Common Unified Device Architecture (CUDA)

Holger Scherl, Benjamin Keck, Markus Kowarschik, Joachim Hornegger

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Tags: Computed tomography, CT, CUDA, Image processing, Image reconstruction, Medicine, nVidia, nVidia GeForce 8800 GTX, Poster

May 28, 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

Parallel Iteration to the Radiative Transport in Inhomogeneous Media with Bootstrapping

Distributed time, conservative parallel logic simulation on GPUs

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

Connected component identification and cluster update on GPU

A Compute Unified System Architecture for Graphics Clusters Incorporating Data Locality

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Graphics Processing Unit Computing

High performance comparison-based sorting algorithm on many-core GPUs

Performance Acceleration of Kernel Polynomial Method Applying Graphics Processing Units

GPU-based parallel-beam and cone-beam forward- and backprojection using CUDA

Performance comparison of GPU and FPGA architectures for the SVM training problem

GPU-Based Ray Tracing of Splats

Fast GPU-Based CT Reconstruction using the Common Unified Device Architecture (CUDA)

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)