high performance computing on graphics processing units: hgpu.org

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Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures

Marc A. Suchard, Quanli Wang, Cliburn Chan, Jacob Frelinger, Andrew Cron, Mike West

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Tags: Bayesian, CUDA, Mathematics, nVidia, nVidia GeForce 9600 M GT, nVidia GeForce GTX 285, Package, Statistics, Tesla C1060

March 4, 2011 by hgpu

Architecture-Aware Optimization Targeting Multithreaded Stream Computing

Byunghyun Jang, Synho Do, Homer Pien, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, Brook, Computer science, Optimization, Programming techniques

March 4, 2011 by hgpu

Redesigning combustion modeling algorithms for the Graphics Processing Unit (GPU): Chemical kinetic rate evaluation and ordinary differential equation integration

Yu Shi, William H. Green, Hsi-Wu Wong, Oluwayemisi O. Oluwole

Tags: Chemistry, Computational modelling, CUDA, nVidia, nVidia GeForce GTX 260, Tesla C2050

March 4, 2011 by hgpu

Multi-GPU Performance of Incompressible Flow Computation by Lattice Boltzmann Method on GPU Cluster

Xian Wang, Takayuki Aoki

Tags: CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, MPI, nVidia

March 4, 2011 by hgpu

Real-Time Multiprocessor Systems with GPUs

Glenn A. Elliott and James H. Anderson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Performance, Task scheduling

March 3, 2011 by hgpu

Smooth Mixed-Resolution GPU Volume Rendering

Johanna Beyer, Markus Hadwiger, Torsten Moller, Laura Fritz

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 280, OpenGL, Rendering, Visualization

March 3, 2011 by hgpu

The sparse matrix vector product on GPUs

F. Vazquez, E. M. Garzon , J. A. Martinez, J. J. Fernandez

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Sparse matrix

March 3, 2011 by hgpu

Unified – A Sharp Turn in the Latest Era of Graphic Processors

Asma Kausar, Aziz Khan, Zulfiqar Ali

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Programming techniques, Review

March 3, 2011 by hgpu

Building Correlators with Many-Core Hardware

Rob V. van Nieuwpoort and John W. Romein

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Tags: Astrophysics, ATI, ATI CAL, ATI IL, ATI Radeon HD 4870, ATI Stream, Cell processor, CUDA, nVidia, Physics, Signal processing, Tesla C1060

March 3, 2011 by hgpu

RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

Bo Wang, Tianji Wu, Feng Yan, Ruirui Li, Ningyi Xu, Yu Wang

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 4870, ATI Stream, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX

March 3, 2011 by hgpu

Parallel Cycle Based Logic Simulation Using Graphics Processing Units

Alper Sen, Baris Aksanli, Murat Bozkurt, Melih Mert

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Tags: Computer science, CUDA, Electronic design automation, nVidia, nVidia Quadro FX 3800

March 3, 2011 by hgpu

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

Alper Sen, Baris Aksanli, Murat Bozkurt

Tags: Algorithms, Computer science, CUDA, Electronic design automation, Graph theory, nVidia

March 3, 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

Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures

Architecture-Aware Optimization Targeting Multithreaded Stream Computing

Redesigning combustion modeling algorithms for the Graphics Processing Unit (GPU): Chemical kinetic rate evaluation and ordinary differential equation integration

Multi-GPU Performance of Incompressible Flow Computation by Lattice Boltzmann Method on GPU Cluster

Real-Time Multiprocessor Systems with GPUs

Smooth Mixed-Resolution GPU Volume Rendering

The sparse matrix vector product on GPUs

Unified – A Sharp Turn in the Latest Era of Graphic Processors

Building Correlators with Many-Core Hardware

RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

Parallel Cycle Based Logic Simulation Using Graphics Processing Units

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

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)