high performance computing on graphics processing units: hgpu.org

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Lattice Boltzmann Simulations on a GPU: An optimization approach using C++ AMP

Kristoffer Clausen Thyholdt

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Tags: Algorithms, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia Quadro 1000 M, Optimization, Thesis

November 18, 2012 by hgpu

Performance modeling of atomic additions on GPU scratchpad memory

Juan Gomez-Luna, Jose Maria Gonzalez-Linares, Jose Ignacio Benavides, Nicolas Guil

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Tags: Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Optimization

November 14, 2012 by hgpu

Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU Computation

Haicheng Wu, Gregory Diamos, Srihari Cadambi, Sudhakar Yalamanchili

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Tags: Computer science, CUDA, nVidia, Optimization, Tesla C2070

November 14, 2012 by hgpu

A Framework for Automated Generation of Specialized Function Variants

Nicholas Chaimov

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Tags: Cloud, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, Optimization, Tesla C2070, Tesla S1070, Thesis

November 6, 2012 by hgpu

cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications

Mads Ruben Burgdorff Kristensen, Simon Andreas Frimann Lund, Troels Blum, Brian Vinter

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Tags: Computer science, OpenCL, Optimization, Package, Programming Languages, Python

November 5, 2012 by hgpu

Parallel Genetic Programming on Graphics Processing Units

Douglas A. Augusto, Heder S. Bernardino, Helio J.C. Barbosa

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Tags: Algorithms, Computer science, Genetic programming, OpenCL, Optimization, Review

October 31, 2012 by hgpu

Silhouette Extraction using Graphics Processing Units

Kristian Haga Karstensen

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Tags: Algorithms, Computer vision, Image processing, nVidia, nVidia GeForce GTX 460, OpenCL, Optimization, Package, Thesis

October 29, 2012 by hgpu

Kernel Specialization for Improved Adaptability and Performance on Graphics Processing Units (GPUs)

Nicholas John Moore

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Tags: Computer science, CUDA, FPGA, Image reconstruction, nVidia, Optimization, Tesis, Tesla C1060, Tesla C2070

October 26, 2012 by hgpu

Lattice Group Models: GPU Acceleration and Numerics

Stefan Brechtken

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Tags: Boltzmann equation, CUDA, Fluid dynamics, Numerical simulation, nVidia, nVidia GeForce GTX 580, Optimization

October 14, 2012 by hgpu

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Ion Lungu, Dana-Mihaela Petrosanu, Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Optimization, Performance

October 14, 2012 by hgpu

Accelerating Cost Aggregation for Real-Time Stereo Matching

Jianbin Fang, Ana Lucia Varbanescu, Jie Shen, Henk Sips, Gorkem Saygili, Laurens van der Maaten

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Tags: Algorithms, Image processing, nVidia, nVidia Quadro FX 5000, nVidia Quadro NVS 140 M, OpenCL, Optimization

October 13, 2012 by hgpu

Extendable Pattern-Oriented Optimization Directives (extended version)

Huimin Cui, Jingling Xue, Lei Wang, Yang Yang, Xiaobing Feng, Dongrui Fan

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Tags: Compilers, Computer science, nVidia, nVidia GeForce GTX 285, Optimization, Performance

October 13, 2012 by hgpu

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Lattice Boltzmann Simulations on a GPU: An optimization approach using C++ AMP

Performance modeling of atomic additions on GPU scratchpad memory

Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU Computation

cphVB: A System for Automated Runtime Optimization and Parallelization of Vectorized Applications

Parallel Genetic Programming on Graphics Processing Units

Silhouette Extraction using Graphics Processing Units

Kernel Specialization for Improved Adaptability and Performance on Graphics Processing Units (GPUs)

Lattice Group Models: GPU Acceleration and Numerics

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Accelerating Cost Aggregation for Real-Time Stereo Matching

Extendable Pattern-Oriented Optimization Directives (extended version)

Recent source codes

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

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

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