high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Visual system design for excavator simulator with deformable terrain

Tao Ni, Dingxuan Zhao, Shui Ni

Tags: Algorithms, Computer science, Rendering, Visualization

June 19, 2011 by hgpu

Fast Query for Exemplar-Based Image Completion

Tsz-Ho Kwok, Hoi Sheung, Charlie C.L. Wang

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Tags: Algorithms, CUDA, Image processing, nVidia, nVidia GeForce GTX 295

June 19, 2011 by hgpu

CST: Constructive Solid Trimming for Rendering BReps and CSG

John Hable, Jarek Rossignac

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Tags: Algorithms, CAD, Cg, Computer science, nVidia, nVidia GeForce 6800 GT, OpenGL, Rendering

June 19, 2011 by hgpu

Binary Segmentation of Video Sequences in Real Time

F.J. Hernandez-Lopez, M. Rivera

Tags: Algorithms, CUDA, Image processing, nVidia, Real-time graphics

June 18, 2011 by hgpu

Processing of synthetic Aperture Radar data with GPGPU

C. Clemente, M. di Bisceglie, M. Di Santo, N. Ranaldo, M. Spinelli

Tags: CUDA, FFT, nVidia, Signal processing, Tesla C1060

June 18, 2011 by hgpu

Coming Soon: Research in a Cloud

Pam Frost Gorder

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Tags: Chemistry, Cloud, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Package

June 18, 2011 by hgpu

CUDA-Based Jacobi’s Iterative Method

Zhihui Zhang, Qinghai Miao, Ying Wang

Tags: Computer science, CUDA, Linear Algebra, nVidia

June 18, 2011 by hgpu

Parallelization and characterization of GARCH option pricing on GPUs

Ren-Shuo Liu, Yun-Cheng Tsai, Chia-Lin Yang

Tags: Computational finance, CUDA, Finance, nVidia

June 17, 2011 by hgpu

A System for Capturing, Rendering and Multiplexing Images on Multi-view Autostereoscopic Display

Hock Soon Tan, Jiazhi Xia, Ying He, Y.Q. Guan

Tags: Algorithms, Computer science, Rendering

June 17, 2011 by hgpu

The Possibility of Fast Large-Scale Numerical Simulation Implemented with Graphics Processing Units

Chi-Jer Yu, Chii-Tung Liu

Tags: Computer science, CUDA, Finite volume method, Fluid dynamics, Numerical simulation, nVidia, nVidia GeForce GTX 280

June 17, 2011 by hgpu

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Byunghyun Jang, Dana Schaa, Perhaad Mistry, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, Brook, Computer science, CUDA, Data parallelism, Memory model, nVidia, nVidia GeForce GTX 285, Review

June 17, 2011 by hgpu

Adapting MoM with RWG Basis Functions to GPU Technology Using CUDA

Tomasz Topa, A. Noga, A. Karwowski

Tags: CUDA, Electrodynamics, nVidia

June 16, 2011 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

Programming

Visual system design for excavator simulator with deformable terrain

Fast Query for Exemplar-Based Image Completion

CST: Constructive Solid Trimming for Rendering BReps and CSG

Binary Segmentation of Video Sequences in Real Time

Processing of synthetic Aperture Radar data with GPGPU

CUDA-Based Jacobi’s Iterative Method

Parallelization and characterization of GARCH option pricing on GPUs

A System for Capturing, Rendering and Multiplexing Images on Multi-view Autostereoscopic Display

The Possibility of Fast Large-Scale Numerical Simulation Implemented with Graphics Processing Units

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Adapting MoM with RWG Basis Functions to GPU Technology Using CUDA

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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