high performance computing on graphics processing units: hgpu.org

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An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Computer science, CUDA, Memory level parallelism, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600, Warp level parallelism

October 29, 2010 by hgpu

GPU-based Video Feature Tracking and Matching

Sudipta N. Sinha, Jan-michael Frahm, Marc Pollefeys, Yakup Genc

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Tags: Computer science, Image processing, nVidia, nVidia GeForce 7900 GTX, OpenGL, Package, SIFT

October 29, 2010 by hgpu

GPU acceleration of cutoff pair potentials for molecular modeling applications

Christopher I. Rodrigues, David J. Hardy, John E. Stone, Klaus Schulten, Wen Mei

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Tags: Algorithms, Biology, Chemistry, CUDA, Medicine, Molecular dynamics, nVidia, Tesla C870

October 28, 2010 by hgpu

AUTO-GC: Automatic translation of data mining applications to GPU clusters

Wenjing Ma, Gagan Agrawal

Tags: Code generation, Computer science, CUDA, GPU cluster, nVidia

October 28, 2010 by hgpu

GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration

G. C. Sharp, N. Kandasamy, H. Singh, M. Folkert

Tags: Brook, Computed radiography, Computer science, Image processing, Medicine, nVidia, nVidia GeForce 8800 GTX

October 28, 2010 by hgpu

CUDA cuts: Fast graph cuts on the GPU

Vibhav Vineet, P. J. Narayanan

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX

October 28, 2010 by hgpu

GPU packet classification using OpenCL: a consideration of viable classification methods

Alastair Nottingham, Barry Irwin

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Tags: Algorithms, Computer science, nVidia, OpenCL

October 28, 2010 by hgpu

An adaptive performance modeling tool for GPU architectures

Sara S. Baghsorkhi, Matthieu Delahaye, Sanjay J. Patel, William D. Gropp, Wen mei

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Tags: Analytical model, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Performance

October 28, 2010 by hgpu

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

Lukasz Ligowski, Witold Rudnicki

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Sequence alignment, Smith-Waterman algorithm

October 28, 2010 by hgpu

Visualization and GPU-accelerated simulation of medical ultrasound from CT images

Oliver Kutter, Ramtin Shams, Nassir Navab

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Tags: ATI, ATI Radeon 1950 PRO, Computer science, GLSL, Medicine, nVidia, nVidia Quadro FX 3600 M, nVidia Quadro FX 5600, OpenGL, Ultrasound, Visualization

October 28, 2010 by hgpu

Jump flooding in GPU with applications to Voronoi diagram and distance transform

Guodong Rong, Tiow S. Tan

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Tags: Computer science, Digital geometry, Interactive application, nVidia, nVidia GeForce 6800 GT, OpenGL, Visualization, Voronoi diagram

October 28, 2010 by hgpu

String Matching on a Multicore GPU Using CUDA

Charalampos S. Kouzinopoulos, Konstantinos G. Margaritis

Tags: Algorithms, Computer science, CUDA, nVidia, String matching

October 28, 2010 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

An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

GPU-based Video Feature Tracking and Matching

GPU acceleration of cutoff pair potentials for molecular modeling applications

AUTO-GC: Automatic translation of data mining applications to GPU clusters

GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration

CUDA cuts: Fast graph cuts on the GPU

GPU packet classification using OpenCL: a consideration of viable classification methods

An adaptive performance modeling tool for GPU architectures

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

Visualization and GPU-accelerated simulation of medical ultrasound from CT images

Jump flooding in GPU with applications to Voronoi diagram and distance transform

String Matching on a Multicore GPU Using 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)