high performance computing on graphics processing units: hgpu.org

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The gputools package enables GPU computing in R

Joshua Buckner, Justin Wilson, Mark Seligman, Brian Athey, Stanley Watson, Fan Meng

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Tags: Computer science, CUBLAS, CUDA, nVidia, Package

October 28, 2010 by hgpu

Accelerating Large Graph Algorithms on the GPU Using CUDA

Pawan Harish, P. Narayanan

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce 8800 GTX

October 28, 2010 by hgpu

Implementing Decision Trees and Forests on a GPU

Toby Sharp

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Tags: Computer science, Computer vision, HLSL, nVidia, nVidia GeForce GTX 280

October 28, 2010 by hgpu

Stochastic DT-MRI Connectivity Mapping on the GPU

Tim McGraw, Mariappan Nadar

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Tags: Computer science, Diffusion tensor, GLSL, Magnetic resonance imaging, nVidia, nVidia GeForce 8800 GTX, OpenGL, Stochastic tractography

October 28, 2010 by hgpu

GPU virtualization on VMware’s hosted I/O architecture

Micah Dowty, Jeremy Sugerman

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Tags: Computer science, Virtualization

October 28, 2010 by hgpu

GPU accelerated image aligned splatting

N. Neophytou, K. Mueller

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Tags: Computer science, Image aligned splatting, nVidia, nVidia Quadro FX 3400, Rendering

October 28, 2010 by hgpu

Fuzzy ART Neural Network Parallel Computing on the GPU

Mario Martinez-Zarzuela, Diaz, Diez, Miriam Rodriguez

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Tags: Computer science, Neural networks, nVidia, nVidia GeForce 7800 GT

October 28, 2010 by hgpu

High performance cellular level agent-based simulation with FLAME for the GPU

Paul Richmond, Dawn Walker, Simon Coakley, Daniela Romano

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Tags: Bioinformatics, Cellular modelling, Computational modelling, Computer science, CUDA, nVidia, Package

October 28, 2010 by hgpu

CrystalGPU: Transparent and Efficient Utilization of GPU Power

Abdullah Gharaibeh, Samer Al-Kiswany, Matei Ripeanu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Package, Performance

October 28, 2010 by hgpu

An integrated GPU power and performance model

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280, Performance

October 28, 2010 by hgpu

GPU as a General Purpose Computing Resource

Qihang Huang, Zhiyi Huang, Paul Werstein, Martin Purvis

Tags: Computer science, CUDA, nVidia, Performance, Programming techniques

October 27, 2010 by hgpu

Parallelization of cellular neural networks on GPU

T. Ho, P. Lam, C. Leung

Tags: Computer science, Neural networks

October 27, 2010 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

The gputools package enables GPU computing in R

Accelerating Large Graph Algorithms on the GPU Using CUDA

Implementing Decision Trees and Forests on a GPU

Stochastic DT-MRI Connectivity Mapping on the GPU

GPU virtualization on VMware’s hosted I/O architecture

GPU accelerated image aligned splatting

Fuzzy ART Neural Network Parallel Computing on the GPU

High performance cellular level agent-based simulation with FLAME for the GPU

CrystalGPU: Transparent and Efficient Utilization of GPU Power

An integrated GPU power and performance model

GPU as a General Purpose Computing Resource

Parallelization of cellular neural networks on GPU

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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