high performance computing on graphics processing units: hgpu.org

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Computing without processors

Satnam Singh

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, DirectX, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 470

September 19, 2011 by hgpu

Real-time ray casting of algebraic B-spline surfaces

Feifei Wei, Jieqing Feng

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Interactive application, nVidia, nVidia GeForce GTX 570, Rasterization, Real-time graphics, Visualization

September 19, 2011 by hgpu

The TheLMA project: Multi-GPU implementation of the lattice Boltzmann method

Christian Obrecht, Frederic Kuznik, Bernard Tourancheau, Jean-Jacques Roux

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Tags: CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, nVidia, Presentation, Tesla C1060

September 19, 2011 by hgpu

Improving SIMD efficiency for parallel Monte Carlo light transport on the GPU

Dietger van Antwerpen

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Raytracing

September 19, 2011 by hgpu

Simpler and faster HLBVH with work queues

Kirill Garanzha, Jacopo Pantaleoni, David McAllister

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Raytracing, Real-time graphics

September 19, 2011 by hgpu

A small-world network model for distributed storage of semantic metadata

A. Leist, K. A. Hawick

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Tags: Artificial intelligence, Computer science, CUDA, Networks, nVidia

September 19, 2011 by hgpu

Using many-core hardware to correlate radio astronomy signals

Rob V. van Nieuwpoort, John W. Romein

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Tags: Algorithms, Astrophysics, ATI, ATI CAL, ATI Radeon HD 4870, ATI Stream, Cell processor, CUDA, nVidia, Performance, Physics, Signal processing, Tesla C1060

September 19, 2011 by hgpu

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

Mark Joselli, Marcelo Zamith, Esteban Clua, Anselmo Montenegro, Regina Leal-Toledo, Aura Conci, Paulo Pagliosa, Luis Valente, Bruno Feijo

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Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce 8200 M, nVidia GeForce 8400 GS, nVidia GeForce 8800 GTS, Performance, Task scheduling

September 19, 2011 by hgpu

cuIBM — A GPU-accelerated Immersed Boundary Method

Simon K Layton, Anush Krishnan, Lorena A. Barba

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Tags: CUDA, Fluid dynamics, Linear Algebra, nVidia, Optimization, Tesla C2050

September 19, 2011 by hgpu

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Florian Worsdorfer, Florian Stock, Eduardo Bayro-Corrochano, Dietmar Hildenbrand

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Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Optimization, Package, Performance

September 16, 2011 by hgpu

Implicit and dynamic trees for high performance rendering

Nathan Andrysco, Xavier Tricoche

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Tags: 3D Graphics and Realism, Computer science, CUDA, KD-tree, nVidia, nVidia GeForce GTX 480, Raytracing, Rendering

September 16, 2011 by hgpu

Fast Monte Carlo Simulation for Patient-specific CT/CBCT Imaging Dose Calculation

Xun Jia, Hao Yan, Xuejun Gu, Steve B. Jiang

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Tags: Computed tomography, CT, CUDA, Heterogeneous systems, Medical Physics, Medicine, Monte Carlo simulation, nVidia, Physics, Tesla C2050, Tomography

September 16, 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

Computing without processors

Real-time ray casting of algebraic B-spline surfaces

The TheLMA project: Multi-GPU implementation of the lattice Boltzmann method

Improving SIMD efficiency for parallel Monte Carlo light transport on the GPU

Simpler and faster HLBVH with work queues

A small-world network model for distributed storage of semantic metadata

Using many-core hardware to correlate radio astronomy signals

An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU

cuIBM — A GPU-accelerated Immersed Boundary Method

Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra

Implicit and dynamic trees for high performance rendering

Fast Monte Carlo Simulation for Patient-specific CT/CBCT Imaging Dose Calculation

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)