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Effectiveness of GPGPU for Solving the Magnetohydrodynamics Equations Using the CIP-MOCCT Method

Ryosuke Ueda, Yutaka Matsumoto, Masafumi Itagaki, Shun-ichi Oikawa

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Tags: CUDA, Magnetohydrodynamics, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Physics

December 5, 2011 by hgpu

GMM based Fisher vector calculation on GPGPU

Erik Bodzsar, Balint Daroczy, Istvan Petras, Andras A. Benczur

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Source codes

Tags: Clustering, CUDA, Databases, Image processing, nVidia, nVidia GeForce GTX 285, Package, Performance, SIFT

December 5, 2011 by hgpu

Parallel Quadtree Coding of Large-Scale Raster Geospatial Data on GPGPUs

Jianting Zhang, Simin You, Le Gruenwald

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Tags: CUDA, Data parallelism, Earth and Space Sciences, Geoscience, nVidia, Sensing, Tesla C2050

December 5, 2011 by hgpu

Real-Time Handling of GPU Interrupts in LITMUS RT

Glenn A. Elliott, Chih-Hao Sun, James H. Anderson

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, Operating systems

December 5, 2011 by hgpu

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Alecio Pedro Delazari Binotto

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, OpenCL, Task scheduling, Thesis

December 5, 2011 by hgpu

GPU-Euler: Sequence Assembly Using GPGPU

Syed F. Mahmood, Huzefa Rangwala

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Tags: Biology, CUDA, Genetics, nVidia, nVidia Quadro FX 5800

December 5, 2011 by hgpu

A New Tool for Classification of Satellite Images Available from Google Maps: Efficient Implementation in Graphics Processing Units

Sergio Bernabe, Antonio Plaza

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Tags: Algorithms, Clustering, Consensus, CUDA, Databases, Image processing, nVidia, nVidia GeForce 9400 M, Tesla C1060

December 5, 2011 by hgpu

GROPHECY: GPU performance projection from CPU code skeletons

Jiayuan Meng, Vitali A. Morozov, Kalyan Kumaran, Venkatram Vishwanath, Thomas D. Uram

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Tags: Analytical model, Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, Performance, Tesla C1060

December 5, 2011 by hgpu

GPU-Based Parallel Multi-objective Particle Swarm Optimization

You Zhou, Ying Tan

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, Optimization

December 4, 2011 by hgpu

Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs

Fumihiko Ino, Yuma Munekawa, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, Distributed computing, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Search, Smith-Waterman algorithm

December 4, 2011 by hgpu

kNN Query Processing in Metric Spaces Using GPUs

Ricardo J. Barrientos, Jose I. Gomez, Christian Tenllado, Manuel Prieto Matias, Mauricio Marin

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Tags: Algorithms, Biology, Computational biology, CUDA, Databases, Information Retrieval, nVidia, nVidia GeForce GTX 280

December 4, 2011 by hgpu

Computing Strongly Connected Components in Parallel on CUDA

Jiri Barnat, Petr Bauch, Lubos Brim, Milan Ceska

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 480

December 4, 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

Effectiveness of GPGPU for Solving the Magnetohydrodynamics Equations Using the CIP-MOCCT Method

GMM based Fisher vector calculation on GPGPU

Parallel Quadtree Coding of Large-Scale Raster Geospatial Data on GPGPUs

Real-Time Handling of GPU Interrupts in LITMUS RT

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

GPU-Euler: Sequence Assembly Using GPGPU

A New Tool for Classification of Satellite Images Available from Google Maps: Efficient Implementation in Graphics Processing Units

GROPHECY: GPU performance projection from CPU code skeletons

GPU-Based Parallel Multi-objective Particle Swarm Optimization

Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs

kNN Query Processing in Metric Spaces Using GPUs

Computing Strongly Connected Components in Parallel on 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)