high performance computing on graphics processing units: hgpu.org

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Enabling Computational Dynamics in Distributed Computing Environments Using a Heterogeneous Computing Template

Dan Negrut, Toby Heyn, Andrew Seidl, Dan Melanz, David Gorsich, David Lamb

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Tags: Collision detection, Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, Tesla C1060, Visualization

October 20, 2011 by hgpu

A high performance computing framework for physics-based modeling and simulation of military ground vehicles

Dan Negrut, David Lamb, David Gorsich

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Tags: Algorithms, Collision detection, CUDA, Distributed computing, Heterogeneous systems, nVidia, Physics, Tesla C1060

October 20, 2011 by hgpu

PEPPHER: Efficient and Productive Usage of Hybrid Computing Systems

Siegfried Benkner, Sabri Pllana, Jesper Larsson Traff, Philippas Tsigas, Uwe Dolinsky, Cedric Augonnet, Beverly Bachmayer, Christoph Kessler, David Moloney, Vitaly Osipov

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Tags: Code generation, Computer science, Heterogeneous systems, Hybrid computing, nVidia, OpenCL, Tesla C1060

October 20, 2011 by hgpu

Optimization strategies for parallel CPU and GPU implementations of a meshfree particle method

Jose M. Dominguez, Alejandro J.C. Crespo, Moncho Gomez-Gesteira

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Tags: Computational Engineering, Computer science, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 480, OpenMP, Optimization, Performance, Tesla C1060

October 18, 2011 by hgpu

Magneto-hydrodynamics simulation in astrophysics

Bijia Pang

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Tags: Algorithms, Astrophysics, CUDA, Fortran, Heterogeneous systems, Magnetohydrodynamics, nVidia, OpenCL, Physics, Tesla C1060, Tesla C2050, Thesis

October 17, 2011 by hgpu

The Lattice Boltzmann Simulation on Multi-GPU Systems

Thor Kristian Valderhaug

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Tags: ATI, ATI Radeon HD 5870, Fluid dynamics, Lattice Boltzmann model, MPI, nVidia, OpenCL, Tesla C1060, Tesla C2070, Tesla S2070, Tesla T10, Thesis

October 17, 2011 by hgpu

High performance finite difference PDE solvers on GPUs

Daniel Egloff

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Tags: CUDA, Finance, Finite difference, nVidia, Partial differential equations, PDEs, Risk Management, Tesla C1060

October 16, 2011 by hgpu

Skeleton Programming for Heterogeneous GPU-based Systems

Usman Dastgeer

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C1060, Thesis

October 16, 2011 by hgpu

Scaling-up spatially-explicit ecological models using graphics processors

Johan van de Koppel, Rohit Gupta, Cornelis Vuik

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Tags: CUDA, Earth and Space Sciences, Ecology, nVidia, Tesla C1060

October 16, 2011 by hgpu

Effects of compression on data intensive algorithms

Ahmed Adnan Aqrawi

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Tags: Algorithms, Compression, Computer science, CUDA, Filtering, Image processing, nVidia, Seismology, Tesla C1060, Tesla C2050, Thesis

October 15, 2011 by hgpu

Efficient Mapping of Streaming Applications for Image Processing on Graphics Cards

Richard Membarth, Hritam Dutta, Frank Hannig, Jurgen Teich

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Source codes

Tags: Algorithms, CUDA, Image processing, nVidia, Package, Tesla C1060

October 15, 2011 by hgpu

Input Sensitivity of GPU Program Optimizations

Yixun Liu, Eddy Z. Zhang, Poornima Bhamidipati, Xipeng Shen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, OpenCL, Optimization, Performance, Tesla C1060

October 13, 2011 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

Enabling Computational Dynamics in Distributed Computing Environments Using a Heterogeneous Computing Template

A high performance computing framework for physics-based modeling and simulation of military ground vehicles

PEPPHER: Efficient and Productive Usage of Hybrid Computing Systems

Optimization strategies for parallel CPU and GPU implementations of a meshfree particle method

Magneto-hydrodynamics simulation in astrophysics

The Lattice Boltzmann Simulation on Multi-GPU Systems

High performance finite difference PDE solvers on GPUs

Skeleton Programming for Heterogeneous GPU-based Systems

Scaling-up spatially-explicit ecological models using graphics processors

Effects of compression on data intensive algorithms

Efficient Mapping of Streaming Applications for Image Processing on Graphics Cards

Input Sensitivity of GPU Program Optimizations

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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