high performance computing on graphics processing units: hgpu.org

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An experimental study on performance portability of OpenCL kernels

Sean Rul, Hans Vandierendonck, Joris D'Haene and Koen De Bosschere

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Tags: ATI, ATI FirePro V8700, Cell processor, Computer science, nVidia, OpenCL, Performance, Playstation, Tesla C1060

February 16, 2011 by hgpu

The fast multipole method on parallel clusters, multicore processors, and graphics processing units

Eric Darve, Cris Cecka, Toru Takahashi

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Tags: Algorithms, CUDA, Fast multipole method, Fluid dynamics, nVidia, Tesla C1060, Tesla S1070

February 11, 2011 by hgpu

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Ruijiang Li, John H. Lewis, Xun Jia, Xuejun Gu, Michael Folkerts, Chunhua Men, William Y. Song, Steve B. Jiang

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Tags: CUDA, Image reconstruction, Medical Physics, Medicine, nVidia, Physics, Radiotherapy, Tesla C1060

February 9, 2011 by hgpu

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

Benjamin G. Levine, John E. Stone, Axel Kohlmeyer

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Tags: Chemistry, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Tesla C1060, Tesla C2050, Tesla S1070

February 9, 2011 by hgpu

Axel: a heterogeneous cluster with FPGAs and GPUs

Kuen Hung Tsoi, Wayne Luk

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Tags: Computer science, CUDA, FPGA, GPU cluster, Heterogeneous systems, nVidia, OpenMPI, Tesla C1060

February 7, 2011 by hgpu

Evaluating multi-core platforms for HPC data-intensive kernels

Alexander S. van Amesfoort, Ana Lucia Varbanescu, Henk J. Sips, Rob V. van Nieuwpoort

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Tags: Algorithms, Cell processor, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Programming techniques, Tesla C1060

February 6, 2011 by hgpu

NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs

Andreas Fidjeland, Etienne B. Roesch, Murray P. Shanahan, Wayne Luk

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Source codes

Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, Package, Tesla C1060

February 5, 2011 by hgpu

Model-driven autotuning of sparse matrix-vector multiply on GPUs

Jee W. Choi, Amik Singh, Richard W. Vuduc

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla C1060, Tesla C870, Tesla T10P

February 5, 2011 by hgpu

QUDA programming for staggered quarks

Steven Gottlieb, Guochun Shi, Aaron Torok, Volodymyr Kindratenko

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Tags: CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C1060, Tesla C2050, Tesla S1070

February 4, 2011 by hgpu

On testing GPU memory for hard and soft errors

Guochun Shi, Jeremy Enos, Michael Showerman, Volodymyr Kindratenko

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Tags: Computer science, CUDA, Hardware Architecture, Memory, nVidia, Package, Tesla C1060, Tesla S1070

February 3, 2011 by hgpu

3D Information Extraction Based on GPU

Ying Zhang

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce 8400 M GS, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia Quadro FX 3700, Tesla C1060

February 3, 2011 by hgpu

Data access optimized applications on the GPU using NVIDIA CUDA

Dheevatsa Mudiger

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Tags: Computer science, CUDA, Data Structures and Algorithms, Fluid dynamics, nVidia, Tesla C1060, Tesla C870, Thesis

February 3, 2011 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

An experimental study on performance portability of OpenCL kernels

The fast multipole method on parallel clusters, multicore processors, and graphics processing units

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

Axel: a heterogeneous cluster with FPGAs and GPUs

Evaluating multi-core platforms for HPC data-intensive kernels

NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs

Model-driven autotuning of sparse matrix-vector multiply on GPUs

QUDA programming for staggered quarks

On testing GPU memory for hard and soft errors

3D Information Extraction Based on GPU

Data access optimized applications on the GPU using NVIDIA CUDA

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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