high performance computing on graphics processing units: hgpu.org

hgpu.org » Optimization

Performance Considerations When Using a Dedicated Ray Traversal Engine

Tomas Davidovic, Lukas Marsalek, Philipp Slusallek

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Tags: Algorithms, Computer science, nVidia, Optimization, Raytracing

December 31, 2011 by hgpu

Efficient Triangle and Quadrilateral Clipping within Shaders

M. McGuire

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Tags: Algorithms, Computer science, GLSL, nVidia, nVidia GeForce GTX 580, OpenGL, Optimization, Package, Rasterization

December 31, 2011 by hgpu

Parallel Quadtree Coding of Large-Scale Raster Geospatial Data on Multicore CPUs and GPGPUs

Jianting Zhang, Simin You, Le Gruenwald

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Tags: CUDA, Data parallelism, Earth and Space Sciences, Geoscience, nVidia, Optimization, Sensing, Tesla C2050

December 29, 2011 by hgpu

Multilevel Tile Load Map on Massive Terrain Visualization

Junlan Nie, Dongliang Guo, Yanfen Wang, Lingfu Kong, Yong Tang

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Tags: Algorithms, Computer science, GLSL, Image processing, nVidia, nVidia GeForce GT 240 M, OpenGL, Optimization, Rendering, Visualization

December 28, 2011 by hgpu

GPU-accelerated Chemical Similarity Assessment for Large Scale Databases

Marco Maggioni, Marco Domenico Santambrogio, Jie Liang

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Tags: Algorithms, Chemistry, CUDA, Databases, nVidia, nVidia GeForce 9400 M, nVidia GeForce GTX 460, Optimization

December 27, 2011 by hgpu

Hybrid CPU and GPGPU Volunteer Computing Framework over the Extensible Messaging and Presence Protocol for Prallel Branch and Bound Optimization of Truss Structures

Mikolaj Mackowiak, Krzysztof Kurowski, Aleksandr Igumenov

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Tags: Algorithms, Computer science, CUDA, Distributed computing, Hybrid computing, MPI, nVidia, Optimization, Package

December 27, 2011 by hgpu

Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Qi Li

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, Optimization, Sorting, Tesla C1060, Tesla C2050, Tesla C2070, Thesis

December 26, 2011 by hgpu

Image processing algorithm optimization with CUDA for Pure Data

Rudi Giot, Abilio Rodrigues e Sousa

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Tags: Algorithm optimization, Algorithms, CUDA, Image processing, nVidia, Optimization

December 24, 2011 by hgpu

GPU Pathfinding Optimization

Adonis Silva, Fernando Rocha, Artur Santos, Geber Ramalho, Veronica Teichrieb

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Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce GTS 360 M, Optimization, Path problems

December 23, 2011 by hgpu

Porting and optimizing MAGFLOW on CUDA

Giuseppe Bilotta, Eugenio Rustico, Alexis Herault, Annamaria Vicari, Giovanni Russo, Ciro Del Negro, Giovanni Gallo

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Tags: CUDA, Earth and Space Sciences, Geoscience, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization

December 21, 2011 by hgpu

Implementation of a Parallel Tree Method on a GPU

Naohito Nakasato

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Tags: Astrophysics, ATI, ATI IL, ATI Radeon HD 5870, Computational Complexity, Computer science, Galaxy Astrophysics, Instrumentation and Methods for Astrophysics, KD-tree, OpenCL, Optimization, Performance

December 21, 2011 by hgpu

Parallel Contour-Buildup Algorithm for the Molecular Surface

Michael Krone, Sebastian Grottel, Thomas Ertl

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Tags: Algorithms, Chemistry, CUDA, Molecular dynamics, Molecular surface, nVidia, nVidia GeForce GTX 580, Optimization, Pharmaceuticals, Visualization

December 20, 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

Performance Considerations When Using a Dedicated Ray Traversal Engine

Efficient Triangle and Quadrilateral Clipping within Shaders

Parallel Quadtree Coding of Large-Scale Raster Geospatial Data on Multicore CPUs and GPGPUs

Multilevel Tile Load Map on Massive Terrain Visualization

GPU-accelerated Chemical Similarity Assessment for Large Scale Databases

Hybrid CPU and GPGPU Volunteer Computing Framework over the Extensible Messaging and Presence Protocol for Prallel Branch and Bound Optimization of Truss Structures

Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Image processing algorithm optimization with CUDA for Pure Data

GPU Pathfinding Optimization

Porting and optimizing MAGFLOW on CUDA

Implementation of a Parallel Tree Method on a GPU

Parallel Contour-Buildup Algorithm for the Molecular Surface

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)