high performance computing on graphics processing units: hgpu.org

hgpu.org » Algorithms

Finite element assembly strategies on multi-and many-core architectures

G. R. Markall, A. Slemmer, D. A .Ham, P. H. J. Kelly, C. D. Cantwell, S. J. Sherwin

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Tags: Algorithms, ATI, ATI Radeon HD 5870, CUDA, FEM, Finite element method, Fluid dynamics, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Spectral elements

October 4, 2011 by hgpu

Optimization of the Gaussian Mixture Model Evaluation on GPU

Jan Vanek, Jan Trmal, Josef V. Psutka, Josef Psutka

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 260, OpenCL, Optimization, Speech recognition

October 4, 2011 by hgpu

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

W. Zuo, A. McNeil, M. Wetter, E.S. Lee

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Tags: Algorithms, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 460, OpenCL, Raytracing

October 4, 2011 by hgpu

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Fawnizu Azmadi Hussin, Anas Mohd Nazlee, Noohul Basheer Zain Ali

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Tags: Algorithms, Computer science, CUDA, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 260, Performance, Programming techniques, Security

October 3, 2011 by hgpu

Parallel Game Tree Search Using GPU

L'ubomir Lackovic

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Games, Graph theory, nVidia, nVidia GeForce GTX 460

October 3, 2011 by hgpu

Implementation of the optimization algorithms on GPGPU architecture and multi-cores

Li Liang

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, OpenCL, Optimization

October 3, 2011 by hgpu

Parallel SAT-Solving with OpenCL

Sander Beckers, Gorik De Samblanx, Floris De Smedt, Toon Goedeme, Lars Struyf, Joost Vennekens

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Tags: Algorithms, Computer science, nVidia, OpenCL, SAT

October 3, 2011 by hgpu

Heterogeneous Computing with OpenCL

Benedict Gaster, David R. Kaeli, Lee Howes, Perhaad Mistry, Dana Schaa

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Tags: Algorithms, Book, Computer science, Heterogeneous systems, OpenCL, Optimization, Tutorial

October 3, 2011 by hgpu

Towards robust automatic detection of vulnerable road users: monocular pedestrian tracking from a moving vehicle

Kristof Van Beeck, Floris De Smedt, Sander Beckers, Lars Struyf, Joost Vennekens, Gorik De Samblanx, Toon Goedeme, Tinne Tuytelaars

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Tags: Algorithms, Computer science, Computer vision, nVidia, nVidia GeForce GTX 295, OpenCL

October 3, 2011 by hgpu

A New Class of Parallel Scheduling Algorithms

Wojciech Bozejko

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Tags: Algorithms, Book, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Programming techniques, Task scheduling, Tesla C1060, Tesla C2050

October 2, 2011 by hgpu

Development of a Chemically Reacting Flow Solver on the Graphic Processing Units

Hai Le

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Tags: Algorithms, CUDA, Finite volume method, Fluid dynamics, nVidia, Optimization, Tesla C2050

October 2, 2011 by hgpu

An Execution Model and Runtime For Heterogeneous Many-Core Systems

Gregory Diamos

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Source codes

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Package, PTX, Tesla C1060, Tesla S870, Thesis

October 2, 2011 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Finite element assembly strategies on multi-and many-core architectures

Optimization of the Gaussian Mixture Model Evaluation on GPU

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Parallel Game Tree Search Using GPU

Implementation of the optimization algorithms on GPGPU architecture and multi-cores

Parallel SAT-Solving with OpenCL

Heterogeneous Computing with OpenCL

Towards robust automatic detection of vulnerable road users: monocular pedestrian tracking from a moving vehicle

A New Class of Parallel Scheduling Algorithms

Development of a Chemically Reacting Flow Solver on the Graphic Processing Units

An Execution Model and Runtime For Heterogeneous Many-Core Systems

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

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

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

Most viewed papers (last 30 days)