high performance computing on graphics processing units: hgpu.org

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Data Structures and Transformations for Physically Based Simulation on a GPU

Perhaad Mistry, Dana Schaa, Byunghyun Jang, David Kaeli, Albert Dvornik, Dwight Meglan

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 285, Physics-based simulation

February 22, 2011 by hgpu

Software-Based ECC for GPUs

Naoya Maruyama, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Error recovery, FFT, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, Tesla S1070

February 21, 2011 by hgpu

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Byunghyun Jang, Dana Schaa, Perhaad Mistry, and David Kaeli

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Tags: Computer science, Matrix multiplication, Memory model, nVidia, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 285, OpenCL, Optimization

February 16, 2011 by hgpu

Understanding the efficiency of ray traversal on GPUs

Timo Aila, Samuli Laine

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Tags: 3D Graphics and Realism, Computer science, CUDA, Image generation, nVidia, nVidia GeForce GTX 285, Raytracing

February 12, 2011 by hgpu

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

Benjamin G. Levine, John E. Stone, Axel Kohlmeyer

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Tags: Chemistry, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Tesla C1060, Tesla C2050, Tesla S1070

February 9, 2011 by hgpu

A New Data Layout For Set Intersection on GPUs

Rasmus Resen Amossen, Rasmus Pagh

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Tags: Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 285, OpenCL, PyOpenCL

February 7, 2011 by hgpu

QUDA programming for staggered quarks

Steven Gottlieb, Guochun Shi, Aaron Torok, Volodymyr Kindratenko

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Source codes

Tags: CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C1060, Tesla C2050, Tesla S1070

February 4, 2011 by hgpu

3I: A tool for visualizing and processing in parallel 2D & 3D images

Juan P.D'Amato, Maria V.Cifuentes, Pablo Lotito

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Tags: 3D Graphics and Realism, CUDA, Image processing, nVidia, nVidia GeForce GTX 285

February 3, 2011 by hgpu

3D Registration Based on Normalized Mutual Information: Performance of CPU vs. GPU Implementation

Florian Jung, Stefan Wesarg

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Tags: CUDA, Image processing, Image registration, Medicine, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 285

February 3, 2011 by hgpu

Simulation and visualization of the Saint-Venant system using GPUs

Andre Brodtkorb, Trond Hagen, Knut-Andreas Lie, Jostein Natvig

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Rendering, Visualization

January 31, 2011 by hgpu

Geometry Construction from Caustic Images

Manuel Finckh, Holger Dammertz, and Hendrik P. A. Lensch

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Rendering

January 27, 2011 by hgpu

Parallel genetic algorithm on the CUDA architecture

Petr Pospichal, Jiri Jaros and Josef Schwarz

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 285

January 22, 2011 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

Data Structures and Transformations for Physically Based Simulation on a GPU

Software-Based ECC for GPUs

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Understanding the efficiency of ray traversal on GPUs

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

A New Data Layout For Set Intersection on GPUs

QUDA programming for staggered quarks

3I: A tool for visualizing and processing in parallel 2D & 3D images

3D Registration Based on Normalized Mutual Information: Performance of CPU vs. GPU Implementation

Simulation and visualization of the Saint-Venant system using GPUs

Geometry Construction from Caustic Images

Parallel genetic algorithm on the CUDA architecture

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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