high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

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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Source codes

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

Fast and scalable list ranking on the GPU

M. Suhail Rehman, Kishore Kothapalli, P. J. Narayanan

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Tags: Algorithms, Computer science, CUDA, List ranking, nVidia, nVidia GeForce GTX 280

October 27, 2010 by hgpu

GPU-Based FFT Computation for Multi-Gigabit WirelessHD Baseband Processing

Nicholas Hinitt, Taskin Kocak

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Tags: Computer science, CUDA, FFT, nVidia

October 27, 2010 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Applications

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

GPU-Based FFT Computation for Multi-Gigabit WirelessHD Baseband Processing

Recent source codes

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

INT v.s. FP: A framework to compare low-bit integer and float-point formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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