high performance computing on graphics processing units: hgpu.org

hgpu.org » Algorithms

fastHOG – a real-time GPU implementation of HOG

Victor A. Prisacariu, Ian Reid

View

Download (PDF)

Source codes

Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 285, Package

March 22, 2011 by hgpu

An evaluation of GPU acceleration for sparse reconstruction

Thomas R. Braun

Tags: Algorithms, Filtering, Image processing, Image recognition

March 21, 2011 by hgpu

High-quality Real-time Stereo using Adaptive Cost Aggregation and Dynamic Programming

Liang Wang, Miao Liao, Minglun Gong, Ruigang Yang, David Nister

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon X1800, Computer science, Computer vision

March 21, 2011 by hgpu

Stereovision On GPU

Ruigang Yang, Liang Wang, Greg Welch, Marc Pollefeys

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon X1800, Computer science, Computer vision

March 21, 2011 by hgpu

Real-time Stochastic Rasterization on Conventional GPU Architectures

M McGuire, E Enderton, P Shirley, D Luebke

View

Download (PDF)

Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce GTX 280, OpenGL

March 20, 2011 by hgpu

GPU-based Dynamic Tubular Grids for Sparse Volume Rendering

David Mayerich, John Keyser

View

Download (PDF)

Tags: Algorithms, Computer science, Image processing, Rendering

March 20, 2011 by hgpu

GPU Vision: Accelerating Computer Vision algorithms with Graphics Processing Units

Tamas K. Lengyel, James Gedarovich, Antonio Cusano, Thomas J. Peters

View

Download (PDF)

Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce 9800 GT

March 20, 2011 by hgpu

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

David Barrie Thomas, Lee Howes, Wayne Luk

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Pseudo-random number generators

March 20, 2011 by hgpu

Mersenne Twister Random Number Generation on FPGA, CPU and GPU

Xiang Tian, Khaled Benkrid

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia GeForce 8800 GTX, Pseudo-random number generators

March 20, 2011 by hgpu

Mean Shift Parallel Tracking on GPU

Peihua Li, Lijuan Xiao

Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Video tracking

March 19, 2011 by hgpu

Efficient and Quality Contouring Algorithms on the GPU

L. Schmitz, L. F. Scheidegger, D. K. Osmari, C. A. Dietrich, J. L. D. Comba

Tags: Algorithms, Computer science, Image generation

March 19, 2011 by hgpu

2D and 3D level-set algorithms on GPU

G. J. Tornai, G. Cserey

Tags: Algorithms, Computer science, Computer vision

March 18, 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

fastHOG – a real-time GPU implementation of HOG

An evaluation of GPU acceleration for sparse reconstruction

High-quality Real-time Stereo using Adaptive Cost Aggregation and Dynamic Programming

Stereovision On GPU

Real-time Stochastic Rasterization on Conventional GPU Architectures

GPU-based Dynamic Tubular Grids for Sparse Volume Rendering

GPU Vision: Accelerating Computer Vision algorithms with Graphics Processing Units

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

Mersenne Twister Random Number Generation on FPGA, CPU and GPU

Mean Shift Parallel Tracking on GPU

Efficient and Quality Contouring Algorithms on the GPU

2D and 3D level-set algorithms on GPU

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

Most viewed papers (last 30 days)