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Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

Hans Henrik Brandenborg Sorensen

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050

April 18, 2012 by hgpu

Maximize Performance on GPUs Using the Rake-based Optimization: A Case Study

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL

April 18, 2012 by hgpu

MetaCL – A Model-Based Approach to Programming Heterogeneous Architectures Using OpenCL

William Spencer Crosswy

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Tags: Algorithms, ATI, ATI Radeon HD 6970, Computer science, Heterogeneous systems, Numerical Analysis, OpenCL, Thesis

April 18, 2012 by hgpu

OpenCL vs. OpenMP: A Programmability Debate

Jie Shen, Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: Benchmarking, Computer science, OpenCL, OpenMP

April 18, 2012 by hgpu

High-Performance Matrix-Vector Multiplication on the GPU

Hans Henrik Brandenborg Sorensen

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050

April 18, 2012 by hgpu

Implementation of Massive Artificial Neural Networks with CUDA

Domen Verber

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 595, Tesla C1060

April 17, 2012 by hgpu

Auto-tuning interactive ray tracing using an analytical GPU architecture model

Per Ganestam, Michael Doggett

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Tags: 3D Graphics and Realism, ATI, ATI Radeon HD 5870, Computer science, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 580, OpenCL, Raytracing, Rendering

April 17, 2012 by hgpu

High Performance Stencil Code Algorithms for GPGPUs

Andreas Schäfer and Dietmar Fey

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Tags: Computer science, nVidia, nVidia GeForce GTX 480, Tesla C2050

April 17, 2012 by gentryx

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

Hans Henrik Brandenborg Sorensen

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, Tesla C2050

April 16, 2012 by hgpu

Heterogeneous Highly Parallel Implementation of Matrix Exponentiation Using GPU

Chittampally Vasanth Raja, Srinivas Balasubramanian, Prakash S. Raghavendra

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Tags: Computer science, Heterogeneous systems, Mathematical Software, Numerical Analysis, nVidia, OpenCL, Tesla C2050

April 16, 2012 by hgpu

Efficient Hash Tables on the GPU

Dan Anthony Feliciano Alcantara

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Tags: Algorithms, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, Thesis

April 14, 2012 by hgpu

An implementation of the tile QR factorization for a GPU and multiple CPUs

Jakub Kurzak, Rajib Nath, Peng Du, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 480

April 14, 2012 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

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high performance computing on graphics processing units: hgpu.org

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

Maximize Performance on GPUs Using the Rake-based Optimization: A Case Study

MetaCL – A Model-Based Approach to Programming Heterogeneous Architectures Using OpenCL

OpenCL vs. OpenMP: A Programmability Debate

High-Performance Matrix-Vector Multiplication on the GPU

Implementation of Massive Artificial Neural Networks with CUDA

Auto-tuning interactive ray tracing using an analytical GPU architecture model

High Performance Stencil Code Algorithms for GPGPUs

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

Heterogeneous Highly Parallel Implementation of Matrix Exponentiation Using GPU

Efficient Hash Tables on the GPU

An implementation of the tile QR factorization for a GPU and multiple CPUs

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

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