high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Accelerating All-Atom Normal Mode Analysis with Graphics Processing Unit

Li Liu, Xiaofeng Liu, Jiayu Gong, Hualiang Jiang, Honglin Li

Tags: Biology, Chemistry, CUDA, Macromolecule, Molecular dynamics, nVidia, Pharmaceuticals

June 2, 2011 by hgpu

MapCG: writing parallel program portable between CPU and GPU

Chuntao Hong, Dehao Chen, Wenguang Chen, Weimin Zheng, Haibo Lin

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Tags: Computer science, CUDA, High-level Languages, MapReduce, nVidia, nVidia GeForce GTX 280, Programming techniques

June 2, 2011 by hgpu

Acceleration of LOD-FDTD Method Using Fundamental Scheme on Graphics Processor Units

Eng Leong Tan

Tags: Electrodynamics, FDTD, Finite-difference time-domain, Microwave engineering

June 2, 2011 by hgpu

Swarm’s flight: Accelerating the particles using C-CUDA

Lucas de P. Veronese, Renato A. Krohling

Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, Optimization

June 2, 2011 by hgpu

Spherical harmonic transform on heterogeneous architectures using hybrid programming

Mikolaj Szydlarski, Pierre Esterie, Joel Falcou, Laura Grigori, Radek Stompor

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Tags: Computer science, CUDA, GPU cluster, Hybrid computing, MPI, nVidia, OpenMP, Tesla S1070

June 2, 2011 by hgpu

GPS forward model computing study on CPU/GPU co-processing parallel system using CUDA

Fukang Yin, Fengshun Lu, Xiaoqun Cao, Junqiang Song

Tags: Computational modelling, Computer science, CUDA, Earth and Space Sciences, GPU cluster, nVidia, nVidia GeForce GTX 480, Weather prediction

June 1, 2011 by hgpu

GPUMLib: A new Library to combine Machine Learning algorithms with Graphics Processing Units

N. Lopes, B. Ribeiro, R. Quintas

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Tags: Computer science, CUDA, Machine learning, nVidia, Package

June 1, 2011 by hgpu

A GPU/CUDA implementation of the collection-diffusion model to compute SER of large area and complex circuits

J.L. Autran, S. Uznanski, S. Martinie, P. Roche, G. Gasiot, D. Munteanu

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Tags: Computer science, CUDA, Fault simulation, Hardware Architecture, Memory, nVidia, Physics, Tesla C1060

June 1, 2011 by hgpu

CUDA Accelerated LTL Model Checking

Jiri Barnat, Lubos Brim, Milan Ceska, Tomas Lamr

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 280

June 1, 2011 by hgpu

Implementations of Parallel Computation of Euclidean Distance Map in Multicore Processors and GPUs

Duhu Man, Kenji Uda, Hironobu Ueyama, Yasuaki Ito, Koji Nakano

Tags: Algorithms, Image processing, nVidia, Tesla C1060

June 1, 2011 by hgpu

The Parallel Processing Based on CUDA for Convolution Filter FDK Reconstruction of CT

Benyi Shi, Sihai Chen, Feifei Huang, Cheng Wang, Kun Bi

Tags: Computed tomography, CT, CUDA, Image reconstruction, Medicine, nVidia, Tomography

June 1, 2011 by hgpu

Parallel particle filter algorithm in face tracking

Ke-Yan Liu, Liang Tang, Shan-Qing Li, Lei Wang, Wei Liu

Tags: Algorithms, Computer science, Particle filtering, Video tracking

June 1, 2011 by hgpu

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

BoltzGen:Toward Universal Binder Design

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

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

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high performance computing on graphics processing units: hgpu.org

Applications

Accelerating All-Atom Normal Mode Analysis with Graphics Processing Unit

MapCG: writing parallel program portable between CPU and GPU

Acceleration of LOD-FDTD Method Using Fundamental Scheme on Graphics Processor Units

Swarm’s flight: Accelerating the particles using C-CUDA

Spherical harmonic transform on heterogeneous architectures using hybrid programming

GPS forward model computing study on CPU/GPU co-processing parallel system using CUDA

GPUMLib: A new Library to combine Machine Learning algorithms with Graphics Processing Units

A GPU/CUDA implementation of the collection-diffusion model to compute SER of large area and complex circuits

CUDA Accelerated LTL Model Checking

Implementations of Parallel Computation of Euclidean Distance Map in Multicore Processors and GPUs

The Parallel Processing Based on CUDA for Convolution Filter FDK Reconstruction of CT

Parallel particle filter algorithm in face tracking

Recent source codes

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

BoltzGen:Toward Universal Binder Design

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

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

Most viewed papers (last 30 days)