high performance computing on graphics processing units: hgpu.org

hgpu.org » Computer science

A Hybrid Approach to Parallel Connected Component Labeling Using CUDA

Youngsung Soh, Hadi Ashraf, Yongsuk Hae, Intaek Kim

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 550 Ti

December 4, 2013 by hgpu

Comparative Study of High Performance Computing Using Multi-core Parallel Systems

Hyo Jong Lee, Hyeon Kyu Kim

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 550 Ti, nVidia GeForce GTX 580, Performance

December 4, 2013 by hgpu

HSPA+/LTE-A Turbo Decoder on GPU and Multicore CPU

Michael Wu, Guohui Wang, Bei Yin, Christoph Studer, Joseph R. Cavallaro

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Wireless baseband architecture

December 4, 2013 by hgpu

Divergence Analysis

Diogo Sampaio, Rafael Martins De Souza, Sylvain Collange, Fernando Magno Quintao Pereira

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 570, Performance

December 4, 2013 by hgpu

Fingerprint grid enhancement on GPU

Raja Lehtihet, Wael El Oraiby, Mohammed Benmohammed

View

Download (PDF)

Tags: Algorithms, Biometrics, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 560 Ti

December 4, 2013 by hgpu

Multithreaded Transposition of Square Matrices with Common Code for Intel Xeon Processors and Intel Xeon Phi Coprocessors

Andrey Vladimirov

View

Download (PDF)

Tags: Algorithms, Benchmarking, Computer science, Intel Phi, Intel Xeon Phi, Linear Algebra

December 3, 2013 by hgpu

GPU and CPU Cooperative Accelerated Road Detection

Peng Xiong, Cheng Xu, Zheng Tian, Tao Li

View

Download (PDF)

Tags: Computer science, Computer vision, Neural networks, OpenCV

December 3, 2013 by hgpu

SESH framework: A Space Exploration Framework for GPU Application and Hardware Codesign

Joo Hwan Lee, Jiayuan Meng, Hyesoon Kim

View

Download (PDF)

Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GTX 580, Performance

December 3, 2013 by hgpu

Real-time High Resolution Fusion of Depth Maps on GPU

Dmitry Trifonov

View

Download (PDF)

Tags: Algorithms, Computer science, Computer vision, Graphics, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 560 Ti, OpenCL

December 3, 2013 by hgpu

GenBase: A Complex Analytics Genomics Benchmark

Rebecca Taft, Manasi Vartak, Nadathur Rajagopalan Satish, Narayanan Sundaram, Samuel Madden, Michael Stonebraker

View

Download (PDF)

Tags: Benchmarking, Computer science, Intel Phi, Intel Xeon Phi

November 30, 2013 by hgpu

Fractal Based Method on Hardware Acceleration for Natural Environments

Divya Udayan J, HyungSeok Kim, Jun Lee, Jee-In Kim

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 650, Rendering

November 30, 2013 by hgpu

Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel Reconstruction and Marker-less Motion Tracking via GPU Acceleration

Jian Song, Yatao Bian, Junchi Yan, Xu Zhao, Yuncai Liu

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 6870, Computer science, Computer vision, Graphics, OpenCL, OpenCV

November 30, 2013 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A Hybrid Approach to Parallel Connected Component Labeling Using CUDA

Comparative Study of High Performance Computing Using Multi-core Parallel Systems

HSPA+/LTE-A Turbo Decoder on GPU and Multicore CPU

Divergence Analysis

Fingerprint grid enhancement on GPU

Multithreaded Transposition of Square Matrices with Common Code for Intel Xeon Processors and Intel Xeon Phi Coprocessors

GPU and CPU Cooperative Accelerated Road Detection

SESH framework: A Space Exploration Framework for GPU Application and Hardware Codesign

Real-time High Resolution Fusion of Depth Maps on GPU

GenBase: A Complex Analytics Genomics Benchmark

Fractal Based Method on Hardware Acceleration for Natural Environments

Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel Reconstruction and Marker-less Motion Tracking via GPU Acceleration

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Most viewed papers (last 30 days)