Matrix multiplication | Page 16

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Hybrid parallel programming – evaluation of OpenACC

Leonardo Piletti Chatain

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Tags: Benchmarking, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, OpenACC, Thesis

February 15, 2013 by hgpu

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

Erik Saule, Kamer Kaya, Umit V. Catalyurek

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Tags: Computer science, CUDA, Hardware Architecture, Intel, Intel Phi, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla C2050, Tesla K20

February 6, 2013 by hgpu

GPU Enhanced Stream-Based Matrix Multiplication

L. M. Itu, C. Suciu, F. Moldoveanu, A. Postelnicu

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 260

January 31, 2013 by hgpu

Sparse Matrix Multiplication using CUDA and Mex Interface

Abhishek Deshpande

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Sparse matrix

December 20, 2012 by hgpu

Matrix-Matrix Multiplications on GPUs for Accelerating a Parallel Fluid Dynamics Code

Kenneth Webster

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Tags: CUBLAS, CUDA, Fluid dynamics, Matrix multiplication, nVidia, Tesla M2070, Thesis

December 12, 2012 by hgpu

A Data-Parallel Algorithmic Modelica Extension for Efficient Execution on Multi-Core Platforms

Mahder Gebremedhin, Afshin Hemmati Moghadam, Peter Fritzson, Kristian Stavaker

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Tags: Algorithms, Benchmarking, Compilers, Computer science, Matrix multiplication, nVidia, OpenCL, Tesla M2050

November 27, 2012 by hgpu

Parallel Programming using OpenCL on Modern Architectures

Allan Svejstrup Nielsen, Allan Peter Engsig-Karup, Bernd Dammann

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Tags: ATI, ATI Radeon HD 6990, Computer science, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 590, OpenCL, Overview, Tutorial

October 15, 2012 by hgpu

An Efficient GPU Implementation of Modified Discrete Cosine Transform Using CUDA

Massimo Panella, Luigi Basset

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Tags: Algorithms, Compression, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 295, Signal processing

October 6, 2012 by hgpu

Accelerating Sparse Matrix Kernels on Graphics Processing Units

Kiran Kumar Matam

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla C1060, Tesla C2050, Thesis

September 14, 2012 by hgpu

Performance Comparison Between Cg-based and CUDA-based Matrix Multiplications

Luke West, Jong Kwan Lee

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Tags: Cg, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance

August 28, 2012 by hgpu

Optimization Techniques for CUDA Application

Tushar Athawale, Xie Xu

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Optimization, Performance

August 28, 2012 by hgpu

Parallel programming on GPU using Intel Array Building Blocks

Chatura A. Ettigi

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 260 M, Programming techniques, Thesis

July 31, 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

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high performance computing on graphics processing units: hgpu.org

Hybrid parallel programming – evaluation of OpenACC

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

GPU Enhanced Stream-Based Matrix Multiplication

Sparse Matrix Multiplication using CUDA and Mex Interface

Matrix-Matrix Multiplications on GPUs for Accelerating a Parallel Fluid Dynamics Code

A Data-Parallel Algorithmic Modelica Extension for Efficient Execution on Multi-Core Platforms

Parallel Programming using OpenCL on Modern Architectures

An Efficient GPU Implementation of Modified Discrete Cosine Transform Using CUDA

Accelerating Sparse Matrix Kernels on Graphics Processing Units

Performance Comparison Between Cg-based and CUDA-based Matrix Multiplications

Optimization Techniques for CUDA Application

Parallel programming on GPU using Intel Array Building Blocks

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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