CUDA | Page 245 | hgpu.org

Programming

hgpu.org » Programming » CUDA

Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

Jinwei Wang, Xirong Ma, Yuanping Zhu, Jizhou Sun

View

Download (PDF)

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

January 26, 2014 by hgpu

GPU acceleration of Newton’s method for large systems of polynomial equations in double double and quad double arithmetic

Jan Verschelde, Xiangcheng Yu

View

Download (PDF)

Tags: Algorithms, CUDA, Extended precision, Linear Algebra, Mathematics, nVidia, Tesla C2050, Tesla K20

January 26, 2014 by hgpu

GPU Monte Carlo scatter calculations for Cone Beam Computed Tomography

Jonas Adler

View

Download (PDF)

Tags: Computed tomography, CT, CUDA, Filtering, Medicine, nVidia, nVidia Quadro 4000, Thesis, Tomography

January 26, 2014 by hgpu

A High-productivity Framework for Multi-GPU computation of Mesh-based applications

Takashi Shimokawabe, Takayuki Aoki, Naoyuki Onodera

View

Download (PDF)

Tags: Computer science, CUDA, Diffusion equation, MPI, nVidia, Tesla K20

January 25, 2014 by hgpu

Accelerating a Bayesian Phylogenetic Inference Application with OpenACC

Joao Pedro de Matos Neves

View

Download (PDF)

Tags: Bayesian, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 580, OpenACC

January 25, 2014 by hgpu

Performance Evaluation of the Intel Many Integrated Core Architecture for 3D Image Reconstruction in Computed Tomography

Johannes Hofmann

View

Download (PDF)

Tags: Algorithms, Computed tomography, CT, CUDA, Image processing, Image reconstruction, Intel Xeon Phi, Medicine, nVidia, nVidia GeForce GTX 680, Thesis, Tomography

January 25, 2014 by hgpu

Improvement of the fused CUDA kernels performance prediction

Peter Novak

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Thesis

January 25, 2014 by hgpu

Finite differences numerical method for two-dimensional superlattice Boltzmann transport equation and case comparison of CPU(C) and GPGPU(CUDA) implementations

Dmitri Priimak

View

Download (PDF)

Tags: Algorithms, Computational Physics, CUDA, Finite difference, Lattice Boltzmann model, Materials Science, nVidia, nVidia GeForce GTX 680, Physics

January 25, 2014 by hgpu

OpenSSL acceleration using Graphics Processing Units

Pedro Miguel Costa Saraiva

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Security, Thesis

January 23, 2014 by hgpu

A comparison between parallelization approaches in molecular dynamics simulations on GPUs

Lorenzo Rovigatti, Petr Sulc, Istvan Z. Reguly, Flavio Romano

View

Download (PDF)

Source codes

Tags: Computational Physics, CUDA, Molecular dynamics, nVidia, Package, Physics, Soft Condensed Matter, Tesla C2070, Tesla K10, Tesla K20

January 23, 2014 by hgpu

Multi-GPU parallel memetic algorithm for capacitated vehicle routing problem

Michal Karpinski, Maciej Pacut

View

Download (PDF)

Tags: Computer science, CUDA, Neural and Evolutionary Computing, nVidia, OpenMPI, Tesla S2050

January 23, 2014 by hgpu

A Lattice Boltzmann Method Simulator for Microfluidics on GPU Cluster

Luiz Otavio S. Ferreira, Lucas Monteiro Volpe

View

Download (PDF)

Tags: CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 580, Visualization

January 19, 2014 by hgpu

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

GPU acceleration of Newton’s method for large systems of polynomial equations in double double and quad double arithmetic

GPU Monte Carlo scatter calculations for Cone Beam Computed Tomography

A High-productivity Framework for Multi-GPU computation of Mesh-based applications

Accelerating a Bayesian Phylogenetic Inference Application with OpenACC

Performance Evaluation of the Intel Many Integrated Core Architecture for 3D Image Reconstruction in Computed Tomography

Improvement of the fused CUDA kernels performance prediction

Finite differences numerical method for two-dimensional superlattice Boltzmann transport equation and case comparison of CPU(C) and GPGPU(CUDA) implementations

OpenSSL acceleration using Graphics Processing Units

A comparison between parallelization approaches in molecular dynamics simulations on GPUs

Multi-GPU parallel memetic algorithm for capacitated vehicle routing problem

A Lattice Boltzmann Method Simulator for Microfluidics on GPU Cluster

Recent source codes

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

Most viewed papers (last 30 days)