high performance computing on graphics processing units: hgpu.org

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SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Matthieu Ospici, Dimitri Komatitsch, Jean-Francois Mehaut, Thierry Deutsch

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Tags: Benchmarking, Computer science, CUDA, Hybrid computing, nVidia, Tesla T10

November 19, 2011 by hgpu

Efficient GPU Implementation for Particle in Cell Algorithm

Rejith George Joseph, Girish Ravunnikutty, Sanjay Ranka, Eduardo D'Azevedo, Scott Klasky

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Tags: Algorithms, CUDA, Electrodynamics, nVidia, Package, Particle-in-cell methods, Physics, Plasma physics, Tesla T10

November 13, 2011 by hgpu

Automatic transformation and optimization of applications on GPUs and GPU clusters

Wenjing Ma

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Tags: Algorithm optimization, Algorithms, Benchmarking, Code generation, Computer science, CUDA, Data mining, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GX2, Optimization, Tesla T10, Thesis

November 9, 2011 by hgpu

Performance analysis of a hybrid MPI/CUDA implementation of the NASLU benchmark

S.J. Pennycook, S.D. Hammond, S.A. Jarvis, G.R. Mudalige

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, nVidia GeForce 8400 GS, nVidia GeForce 9400 GT, Operating systems, Performance, Tesla C1060, Tesla C2050, Tesla T10

November 8, 2011 by hgpu

Topology Optimization with Unstructured Meshes on Graphics Processing Units (GPUs)

Tomas Zegard Latrach

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, Optimization, Tesla T10, Thesis

November 3, 2011 by hgpu

Techniques to maximize memory bandwidth on the Rigel compute accelerator

William J. Tuohy

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Tags: ATI, ATI Radeon HD 5870, Computer science, Memory, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Tesla T10, Thesis

October 27, 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-Order Discontinuous Galerkin Methods by GPU Metaprogramming

Andreas Klockner, Timothy Warburton, Jan S. Hesthaven

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Tags: CUDA, Differential equations, Mathematics, nVidia, nVidia GeForce GTX 280, Python, Tesla T10

October 5, 2011 by hgpu

Unstructured grid applications on GPU: performance analysis and improvement

Lizandro Solano-Quinde, Zhi Jian Wang, Brett Bode, Arun K. Somani

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Software Engineering, Tesla T10

September 23, 2011 by hgpu

Nikola: embedding compiled GPU functions in Haskell

Geoffrey Mainland, Greg Morrisett

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Programming Languages, Tesla T10

September 14, 2011 by hgpu

Accelerating Haskell array codes with multicore GPUs

Manuel M.T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, Vinod Grover

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Tags: Code generation, Computer science, CUBLAS, CUDA, Data parallelism, nVidia, Performance, Programming Languages, Tesla T10

August 22, 2011 by hgpu

A self-organization based optical flow estimator with GPU implementation (thesis)

Manish Shiralkar

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5600, nVidia Quadro NVS 140 M, Optical flow, Self-organizing map, Tesla T10, Thesis

July 22, 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

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Efficient GPU Implementation for Particle in Cell Algorithm

Automatic transformation and optimization of applications on GPUs and GPU clusters

Performance analysis of a hybrid MPI/CUDA implementation of the NASLU benchmark

Topology Optimization with Unstructured Meshes on Graphics Processing Units (GPUs)

Techniques to maximize memory bandwidth on the Rigel compute accelerator

The Lattice Boltzmann Simulation on Multi-GPU Systems

High-Order Discontinuous Galerkin Methods by GPU Metaprogramming

Unstructured grid applications on GPU: performance analysis and improvement

Nikola: embedding compiled GPU functions in Haskell

Accelerating Haskell array codes with multicore GPUs

A self-organization based optical flow estimator with GPU implementation (thesis)

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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