high performance computing on graphics processing units: hgpu.org

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Ultrasound goes GPU: real-time simulation using CUDA

Tobias Reichl, Josh Passenger, Oscar Acosta, Olivier Salvado

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Tags: CUDA, Image-guided therapy, Medicine, nVidia, nVidia GeForce 8800 GTX, Surgical simulation, Ultrasound, Visualization

October 29, 2010 by hgpu

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

Dimitri Komatitsch, Gordon Erlebacher, Dominik Goddeke, David Michea

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Tags: Computational Physics, CUDA, Finite element method, Geoscience, GPU cluster, MPI, Numerical simulation, nVidia, Physics, Seismic modeling, Spectral elements, Tesla S1060, Tesla S1070

October 29, 2010 by hgpu

Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

Nguyen-Khang Pham, Annie Morin, Patrick Gros

Tags: CUBLAS, CUDA, Factorial correspondence analysis, Image processing, nVidia, SIFT

October 29, 2010 by hgpu

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

David Dynerman, Erick Butzlaff, Julie C. Mitchell

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Tags: Bioinformatics, Biology, Biophysics, Chemistry, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Solvent effects

October 29, 2010 by hgpu

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 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

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

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

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

Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU

Dar J. Chang, Ahmed H. Desoky, Ming Ouyang, Eric C. Rouchka

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Tags: Computer science, CUDA, Hierarchical clustering, nVidia, Similarity and dissimilarity metrics, Tesla C870

October 28, 2010 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Ultrasound goes GPU: real-time simulation using CUDA

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

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

GPU acceleration of cutoff pair potentials for molecular modeling applications

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

CUDA cuts: Fast graph cuts on the GPU

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

String Matching on a Multicore GPU Using CUDA

Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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