high performance computing on graphics processing units: hgpu.org

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Distributed Massive Model Rendering

Revanth N R, P. J. Narayanan

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia Quadro FX 3700 M, Rendering, Tesla C2050, Tesla C2070, Tesla S1070

January 8, 2013 by hgpu

Graphic-Processing-Units Based Adaptive Parameter Estimation of a Visual Psychophysical Model

Hairong Gu

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Tesla C2050, Thesis

January 4, 2013 by hgpu

Automatic Code Generation for Stencil Computations on GPU Architectures

Justin Andrew Holewinski

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, OpenCL, Tesla C2050, Tesla K10, Thesis

January 4, 2013 by hgpu

Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance

Qing Hou, Min Li, Yulu Zhou, Jiechao Cui, Zhenguo Cui, Jun Wang

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Tags: Computational Physics, CUDA, GPU cluster, Materials Science, Molecular dynamics, nVidia, Physics, Tesla C2050

January 2, 2013 by hgpu

GPU-Based Tracking Algorithms for the ATLAS High-Level Trigger

D. Emeliyanov, J. Howard

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Tags: CUDA, Data acquisition, Instrumentation and measurement, Nuclear physics, nVidia, Particle physics and field theory, Physics, Tesla C2050

December 29, 2012 by hgpu

Algorithmic Skeleton Framework for the Orchestration of GPU Computations

Ricardo Jorge dos Santos Marques

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, Optimization, Programming techniques, Tesla C2050

December 25, 2012 by hgpu

Acceleration of multivariate analysis techniques in TMVA using GPUs

A. Hoecker, H. McKendrick, J. Therhaag, A. Washbrook

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Tags: Biological Physics, CUDA, Neural networks, nVidia, Physics, Tesla C1060, Tesla C2050

December 16, 2012 by hgpu

Communication-Avoiding Optimization of Geometric Multigrid on GPUs

Amik Singh

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Tesla C2050

December 16, 2012 by hgpu

Towards Domain-specific Computing for Stencil Codes in HPC

Richard Membarth, Frank Hannig, Jurgen Teich, Harald Kostler

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Tags: Algorithms, Code generation, Compression, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro FX 5800, OpenCL, Package, Tesla C2050

December 12, 2012 by hgpu

CUDACLAW: a Data Parallel Solution Framework for Hyperbolic PDEs

Hrag Gorune Krikor Ohannessian

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Tags: Acoustics, CUDA, Differential equations, Finite volume method, Hyperbolic PDEs, Mathematics, nVidia, nVidia GeForce GTX 460, Partial differential equations, PDEs, Tesla C2050, Wave equation

December 12, 2012 by hgpu

Vectorized Higher Order Finite Difference Kernels

Gerhard Zumbusch

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Tags: Algorithms, ATI, ATI Radeon HD 6990, ATI Radeon HD 7970, Computer science, Finite difference, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, OpenCL, Performance, Tesla C2050, Tesla K20

December 10, 2012 by hgpu

Lightweight Modular Staging and Embedded Compilers: Abstraction Without Regret for High-Level High-Performance Programming

Tiark Rompf

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Software Engineering, Tesla C2050, Thesis

December 8, 2012 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Distributed Massive Model Rendering

Graphic-Processing-Units Based Adaptive Parameter Estimation of a Visual Psychophysical Model

Automatic Code Generation for Stencil Computations on GPU Architectures

Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance

GPU-Based Tracking Algorithms for the ATLAS High-Level Trigger

Algorithmic Skeleton Framework for the Orchestration of GPU Computations

Acceleration of multivariate analysis techniques in TMVA using GPUs

Communication-Avoiding Optimization of Geometric Multigrid on GPUs

Towards Domain-specific Computing for Stencil Codes in HPC

CUDACLAW: a Data Parallel Solution Framework for Hyperbolic PDEs

Vectorized Higher Order Finite Difference Kernels

Lightweight Modular Staging and Embedded Compilers: Abstraction Without Regret for High-Level High-Performance Programming

Recent source codes

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

ROCm's implementation of Gromacs

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

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