high performance computing on graphics processing units: hgpu.org

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Patient-Specific Non-Linear Finite Element Modelling for Predicting Soft Organ Deformation in Real-Time; Application to Non-Rigid Neuroimage Registration

Adam Wittek, Grand Joldes, Mathieu Couton, Simon K. Warfield, Karol Miller

Tags: Biology, Medicine, nVidia, Surgical simulation, Tesla C870

November 20, 2010 by hgpu

A flexible high-performance Lattice Boltzmann GPU code for the simulations of fluid flows in complex geometries

Massimo Bernaschi, Massimiliano Fatica, Simone Melchionna, Sauro Succi, Efthimios Kaxiras

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, MPI, nVidia, nVidia GeForce 8600 M GT, Tesla C870

November 18, 2010 by hgpu

Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines

Antonio Wendell De Oliveira Rodrigues, Frederic Guyomarch, Yvonnick Le Menach, Jean-Luc Dekeyser

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Sparse matrix, Tesla C870

November 9, 2010 by hgpu

Parallelizing the QUDA Library for Multi-GPU Calculations in Lattice Quantum Chromodynamics

Ronald Babich, Michael A. Clark, Balint Joo

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Tags: CUDA, GPU cluster, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C1060, Tesla C2050, Tesla C870

November 9, 2010 by hgpu

Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU

Imran S. Haque, Vijay S. Pande

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Tags: Computer science, CUDA, Hardware Architecture, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9500 GT, Package, Tesla C870

November 9, 2010 by hgpu

High-performance cone beam reconstruction using CUDA compatible GPUs

Yusuke Okitsu, Fumihiko Ino, Kenichi Hagihara

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Tags: CUDA, Image processing, Image reconstruction, nVidia, nVidia GeForce 8800 GTX, Tesla C870

November 8, 2010 by hgpu

Best-effort semantic document search on GPUs

Surendra Byna,Jiayuan Meng,Anand Raghunathan,Srimat Chakradhar,Srihari Cadambi

Tags: Computer science, nVidia, Programming techniques, Semantic indexing, Tesla C870

November 5, 2010 by hgpu

Multilevel summation of electrostatic potentials using graphics processing units

David J. Hardy, John E. Stone, Klaus Schulten

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Tags: Chemistry, CUDA, Electrostatics, Molecular dynamics, Molecular modeling, Multilevel summation, nVidia, nVidia GeForce GTX 280, Physics, Tesla C870, Tesla D870

November 2, 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

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

Sequence alignment with GPU: Performance and design challenges

Gregory M. Striemer, Ali Akoglu

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, Package, Sequence alignment, Smith-Waterman algorithm, Tesla C870

October 27, 2010 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Patient-Specific Non-Linear Finite Element Modelling for Predicting Soft Organ Deformation in Real-Time; Application to Non-Rigid Neuroimage Registration

A flexible high-performance Lattice Boltzmann GPU code for the simulations of fluid flows in complex geometries

Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines

Parallelizing the QUDA Library for Multi-GPU Calculations in Lattice Quantum Chromodynamics

Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU

High-performance cone beam reconstruction using CUDA compatible GPUs

Best-effort semantic document search on GPUs

Multilevel summation of electrostatic potentials using graphics processing units

GPU acceleration of cutoff pair potentials for molecular modeling applications

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

Sequence alignment with GPU: Performance and design challenges

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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