high performance computing on graphics processing units: hgpu.org

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GPGPU and Multi-Core Architectures for Computing Clustering Coefficients of Irregular Graphs

A. Leist, K.A. Hawick, D.P. Playne

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Tags: Algorithms, Cell processor, Clustering, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480

January 24, 2012 by hgpu

Efficient GPU implementation of a two waves WAF method for the two-dimensional one layer Shallow Water system on structured meshes

Marc de la Asuncion, Manuel J. Castro, E. D. Fernandez-Nieto, Jose M. Mantas, Sergio Ortega Acosta, Jose Manuel Gonzalez-Vida

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580

January 24, 2012 by hgpu

Dynamically Managed Data for CPU-GPU Architectures

Thomas B. Jablin, James A. Jablin, Prakash Prabhu, Feng Liu, David I. August

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Tags: Computer science, CUDA, Data Structures and Algorithms, Memory model, nVidia, nVidia GeForce GTX 480, Performance

January 19, 2012 by hgpu

Combining approximate inference methods for efficient learning on large computer clusters

Zhenwen Dai, Jacquelyn A. Shelton, Jorg Bornschein, Abdul Saboor Sheikh, Jorg Lucke

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Tags: Algorithms, Computational Complexity, Computer science, GPU cluster, Machine learning, MPI, nVidia, nVidia GeForce GTX 480, OpenCL, Python

January 19, 2012 by hgpu

Parallel Optimization of Queries in XML Dataset Using GPU

Xujie Si, Airu Yin, Xiaocheng Huang, Xiaojie Yuan, Xiaoguang Liu, Gang Wang

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, Optimization

January 18, 2012 by hgpu

A visibility-based approach for occupancy grid computation in disparity space

Mathias Perrollaz, John-David Yoder, Amaury Negre, Anne Spalanzani, Christian Laugier

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Tags: Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX 480, Stereo vision

January 15, 2012 by hgpu

Graphics Processor Clusters for High Speed Backpropagation

Daniel P. Campbell, Daniel A. Cook

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Tags: CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, Optimization, Signal processing, Tesla C2050

January 14, 2012 by hgpu

Parallel Implementations of Hopfield Neural Networks On GPU

Li Liang

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 480, Optimization

January 13, 2012 by hgpu

Efficient Parallel CKY Parsing on GPUs

Youngmin Yi, Chao-Yue Lai, Slav Petrov, Kurt Keutzer

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Text mining

January 13, 2012 by hgpu

A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors

Jiwon Seo, Yu-Hsuan Chen, David S. De Lorenzo, Sherman Lo, Per Enge, Dennis Akos, Jiyun Lee

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Tags: CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Signal processing

January 13, 2012 by hgpu

An FPGA-based processing pipeline for high definition stereo video

Pierre Greisen, Simon Heinzle, Markus Gross, Andreas Peter Burg

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Tags: Algorithms, CUDA, FPGA, Image processing, nVidia, nVidia GeForce GTX 480, Stereo vision

January 13, 2012 by hgpu

GKLEE: Concolic Verification and Test Generation for GPUs

Guodong Li, Peng Li, Geof Sawaya, Ganesh Gopalakrishnan, Indradeep Ghosh, Sreeranga P. Rajan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Programming techniques

January 12, 2012 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

GPGPU and Multi-Core Architectures for Computing Clustering Coefficients of Irregular Graphs

Efficient GPU implementation of a two waves WAF method for the two-dimensional one layer Shallow Water system on structured meshes

Dynamically Managed Data for CPU-GPU Architectures

Combining approximate inference methods for efficient learning on large computer clusters

Parallel Optimization of Queries in XML Dataset Using GPU

A visibility-based approach for occupancy grid computation in disparity space

Graphics Processor Clusters for High Speed Backpropagation

Parallel Implementations of Hopfield Neural Networks On GPU

Efficient Parallel CKY Parsing on GPUs

A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors

An FPGA-based processing pipeline for high definition stereo video

GKLEE: Concolic Verification and Test Generation for GPUs

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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