high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix multiplication

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

A Geetha Venkatesh

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Tags: Computer science, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Tesla C2070, Thesis

February 3, 2015 by hgpu

Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model

Emanuel H. Rubensson, Elias Rudberg

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, MPI, nVidia, Sparse matrix, Tesla K20

February 2, 2015 by hgpu

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Matrix multiplication, nVidia, OpenCL, Performance, Tesla K20

January 21, 2015 by hgpu

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Jonathan Wong, Ellen Kuhl, Eric Darve

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

January 5, 2015 by hgpu

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Michael Driscoll

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Tags: 3D Graphics and Realism, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sparse matrix

January 5, 2015 by hgpu

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Jose M. Andion, Manuel Arenaz, Francois Bodin, Gabriel Rodriguez, Juan Tourino

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Tags: Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, Tesla S1070, Tesla S2050

December 14, 2014 by hgpu

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

Kazuya Matsumoto, Naohito Nakasato, Stanislav Sedukhin

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Tags: ATI, ATI Radeon HD 7970, BLAS, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan, OpenCL

October 29, 2014 by hgpu

A Study of the Potential of Locality-Aware Thread Scheduling for GPUs

Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 580, OpenCL, Task scheduling

September 28, 2014 by hgpu

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

Ziming Zhong

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, Numerical simulation, nVidia, Tesla S2050, Thesis

August 27, 2014 by hgpu

A Study on GPU Computing and Accelerating Simulation of Sedimentary Rock Structure

Koushik Mandal

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla C2075, Thesis

July 23, 2014 by hgpu

Efficient implementation of computationally intensive algorithms on parallel computing platforms

Csaba Nemes

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 570, Tesla K20, Thesis

July 17, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

A Study of the Potential of Locality-Aware Thread Scheduling for GPUs

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

A Study on GPU Computing and Accelerating Simulation of Sedimentary Rock Structure

Efficient implementation of computationally intensive algorithms on parallel computing platforms

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Recent source codes

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

Most viewed papers (last 30 days)