high performance computing on graphics processing units: hgpu.org

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GPU Programming in a High Level Language: Compiling X10 to CUDA

Dave Cunningham, Rajesh Bordawekar, Vijay Saraswat

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, nVidia Quadro FX 3700 M, OpenCL, Tesla C1060, Tesla S1070

December 12, 2011 by hgpu

Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research

Martin Peniak, Anthony Morse, Christopher Larcombe, Salomon Ramirez-Contla, Angelo Cangelosi

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 470, Package, Tesla C1060

December 11, 2011 by hgpu

Accelerating Swarm Intelligence Algorithms with GPU-Computing

Robin M. Weiss

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Tags: Algorithms, Cluster analysis, Clustering, Computer science, CUDA, Data mining, nVidia, Tesla C1060

December 11, 2011 by hgpu

Particle Simulation on a GPU with PyCUDA

Min Ragan-Kelley

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Tags: CUDA, nVidia, nVidia GeForce GTX 260, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Python, Tesla C1060

December 10, 2011 by hgpu

GPU implementations of scheduling heuristics for heterogeneous computing environments

Sergio Nesmachnow, Mauro Canabe

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Task scheduling, Tesla C1060

December 9, 2011 by hgpu

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Kristian Stavaker

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8600, OpenCL, Tesla C1060, Tesla M2050, Thesis

December 9, 2011 by hgpu

Three-Dimensional Modeling of Long-Wave Runup: Simulation of Tsunami Inundation with GPU-SPHysics

Robert Weiss, Andrew James Munoz, Robert A. Dalrymple, Alexis Herault, Giuseppe Bilotta

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Tags: Algorithms, CUDA, Earth and Space Sciences, nVidia, Package, Tesla C1060

December 8, 2011 by hgpu

Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Stefan Lang, Panos Drouvelis, Enkelejda Tafaj, Peter Bastian, Bert Sakmann

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Tags: Algorithms, Biology, CUDA, Electron microscopy, Image processing, Microscopy, Neuroscience, nVidia, Tesla C1060

December 8, 2011 by hgpu

Evolving Neural Networks on GPUs

Johannes Hofmann

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, Finance, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX 480, Tesla C1060

December 7, 2011 by hgpu

Performance engineering for the Lattice Boltzmann method on GPGPUs: Architectural requirements and performance results

Johannes Habich, Christian Feichtinger, Harald Kostler, Georg Hager, Gerhard Wellein

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Tags: Algorithms, ATI, ATI Radeon HD 6970, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Performance, Tesla C1060, Tesla C2070

December 6, 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

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

GPU Programming in a High Level Language: Compiling X10 to CUDA

Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research

Accelerating Swarm Intelligence Algorithms with GPU-Computing

Particle Simulation on a GPU with PyCUDA

GPU implementations of scheduling heuristics for heterogeneous computing environments

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Three-Dimensional Modeling of Long-Wave Runup: Simulation of Tsunami Inundation with GPU-SPHysics

Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Evolving Neural Networks on GPUs

Performance engineering for the Lattice Boltzmann method on GPGPUs: Architectural requirements and performance results

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

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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