high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix multiplication

Compressing Floating-Point Number Stream for Numerical Applications

Hisanobu Tomari, Mary Inaba, Kei Hiraki

Tags: Algorithms, Compression, Computer science, FFT, FPGA, Matrix multiplication

April 20, 2011 by hgpu

Linear optimization on modern GPUs

Daniele G. Spampinato, Anne C. Elster

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Tags: CUDA, Finance, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Optimization

April 18, 2011 by hgpu

A high-performance fault-tolerant software framework for memory on commodity GPUs

Naoya Maruyama, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Fault simulation, FFT, Matrix multiplication, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, Tesla S1070

April 14, 2011 by hgpu

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Noboru Tanabe, Yuuka Ogawa, Masami Takata, Kazuki Joe

Tags: Computer science, Heterogeneous systems, Matrix multiplication, Sparse matrix

April 14, 2011 by hgpu

Speeding up K-Means Algorithm by GPUs

You Li, Kaiyong Zhao, Xiaowen Chu, Jiming Liu

Tags: Algorithms, Computer science, Matrix multiplication, Optimization

April 13, 2011 by hgpu

Dense linear algebra solvers for multicore with GPU accelerators

Stanimire Tomov, Rajib Nath, Hatem Ltaief, Jack Dongarra

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Tags: Algorithms, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Package

March 23, 2011 by hgpu

Improving accuracy for matrix multiplications on GPUs

Matthew Badin, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Performance

March 22, 2011 by hgpu

Dense Matrix Algebra on the GPU

Adam Moravanszky

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Tags: ATI, ATI Radeon 9500 Pro, ATI Radeon 9700 Pro, Computer science, DirectX, Linear Algebra, Matrix multiplication

February 24, 2011 by hgpu

Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Byunghyun Jang, Dana Schaa, Perhaad Mistry, and David Kaeli

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Tags: Computer science, Matrix multiplication, Memory model, nVidia, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 285, OpenCL, Optimization

February 16, 2011 by hgpu

Efficient Sparse Matrix-Vector Multiplication on CUDA

Nathan Bell and Michael Garland

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Sparse matrix

January 24, 2011 by hgpu

An Improved Magma Gemm For Fermi Graphics Processing Units

Rajib Nath, Stanimire Tomov, Jack Dongarra

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

January 23, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Compressing Floating-Point Number Stream for Numerical Applications

Linear optimization on modern GPUs

A high-performance fault-tolerant software framework for memory on commodity GPUs

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Speeding up K-Means Algorithm by GPUs

Dense linear algebra solvers for multicore with GPU accelerators

Improving accuracy for matrix multiplications on GPUs

Dense Matrix Algebra on the GPU

Evaluating one-sided programming models for GPU cluster computations

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Efficient Sparse Matrix-Vector Multiplication on CUDA

An Improved Magma Gemm For Fermi Graphics Processing Units

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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