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Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

Robert Hochberg

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9600 M GT, nVidia GeForce GT 330 M, Tesla C1060

April 27, 2012 by hgpu

Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

Franz Xaver Bayerl

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Tags: Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 560 Ti, Performance, Thesis

April 19, 2012 by hgpu

Algorithm Construction for GPGPU

Mattias Svanstrom, Simon Hossjer

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Tags: Algorithms, ATI, ATI Radeon HD 5650, Computer science, Matrix multiplication, nVidia, nVidia GeForce 8400 GS, nVidia GeForce GTX 560 Ti, OpenCL, Sorting

April 19, 2012 by hgpu

phiGEMM: a CPU-GPU library for porting Quantum ESPRESSO on hybrid systems

Filippo Spiga, Ivan Girotto

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Tags: Benchmarking, BLAS, Computer science, CUDA, FFT, Matrix multiplication, nVidia, Package, Tesla C2050

April 14, 2012 by hgpu

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

James Bergstra, Nicolas Pinto, David Cox

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Tags: Computer science, CUDA, Filtering, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Tesla C1060, Tesla C2070

March 29, 2012 by hgpu

Strassen’s Matrix Multiplication on GPUs

Junjie Li, Sanjay Ranka, Sartaj Sahni

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Tesla C1060

January 23, 2012 by hgpu

Optimising the DBCSR GPU Implementation

Jay Chetty

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla C2050, Thesis

December 31, 2011 by hgpu

Generating SU(Nc) pure gauge lattice QCD configurations on GPUs with CUDA and OpenMP

Nuno Cardoso, Pedro Bicudo

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, Matrix multiplication, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, Package, Physics, QCD

December 21, 2011 by hgpu

Developing an OO Model for Generalized Matrix Multiplication: Preliminary Considerations

Maria Ganzha, Marcin Paprzycki, Stanislav G. Sedukhin

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Tags: Algorithms, Computer science, Linear Algebra, Matrix multiplication

December 13, 2011 by hgpu

Scalable Query Evaluation in Relational Databases

Rasmus Resen Amossen

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Tags: Algorithms, Computer science, Databases, Matrix multiplication, nVidia, nVidia GeForce GTX 285, OpenCL, Optimization, Python, Thesis

December 5, 2011 by hgpu

Computation of Large Covariance Matrices by SAMMY on Graphical Processing Units and Multicore CPUs

G. Arbanas, M.E. Dunn, D. Wiarda

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Tags: CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Matrix multiplication, Nuclear Experiment, nVidia, Physics, Tesla C2050

December 3, 2011 by hgpu

Autotuning GEMMs for Fermi

Jakub Kurzak, Stanimire Tomov, Jack Dongarra

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Tags: Benchmarking, BLAS, Computer science, CUDA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Package, Tesla S2050

November 20, 2011 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

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high performance computing on graphics processing units: hgpu.org

Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

Performance Exploration of Selected Manually and Automatically Parallelized Codes on GPUs

Algorithm Construction for GPGPU

phiGEMM: a CPU-GPU library for porting Quantum ESPRESSO on hybrid systems

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

Strassen’s Matrix Multiplication on GPUs

Optimising the DBCSR GPU Implementation

Generating SU(Nc) pure gauge lattice QCD configurations on GPUs with CUDA and OpenMP

Developing an OO Model for Generalized Matrix Multiplication: Preliminary Considerations

Scalable Query Evaluation in Relational Databases

Computation of Large Covariance Matrices by SAMMY on Graphical Processing Units and Multicore CPUs

Autotuning GEMMs for Fermi

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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