high performance computing on graphics processing units: hgpu.org

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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

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

Hierarchical QR factorization algorithms for multi-core cluster systems

Jack Dongarra, Mathieu Faverge, Thomas Herault, Julien Langou, Yves Robert

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Tags: Algorithms, BLAS, Computer science, Factorization, Linear Algebra

November 20, 2011 by hgpu

Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Fengguang Song, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, BLAS, Computer science, CUDA, Factorization, Heterogeneous systems, nVidia, Tesla M2070

November 20, 2011 by hgpu

Auto-tunable GPU BLAS

Jarle Erdal Steinsland

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Tags: Algorithms, ATI, ATI Radeon HD 5750, ATI Radeon HD 5870, BLAS, Computer science, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Tesla C2070, Thesis

November 18, 2011 by hgpu

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

Przemyslaw Stpiczynski

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

October 21, 2011 by hgpu

A framework to implement a multifrontal scheme on GPU architectures with OpenCL

Federico Edoardo Bonelli

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Tags: BLAS, Computer science, Linear Algebra, nVidia, OpenCL, Performance, Thesis

September 27, 2011 by hgpu

Compiling Python to a hybrid execution environment

Rahul Garg, Jose Nelson Amaral

Tags: ATI, ATI CAL, BLAS, Compilers, Computer science, OpenCL, Python

August 21, 2011 by hgpu

High Performance Matrix Inversion on a Multi-core Platform with Several GPUs

Pablo Ezzatti, Enrique S. Quintana-Orti, Alfredo Remon

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, Presentation, Tesla C1060

July 5, 2011 by hgpu

Accelerating GPU kernels for dense linear algebra

Rajib Nath, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280

June 4, 2011 by hgpu

Mixed-Tool Performance Analysis on Hybrid Multicore Architectures

Peng Du, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Performance

June 3, 2011 by hgpu

Application of the OpenCL API for Implementation of the NIPALS Algorithm for Principal Component Analysis of Large Data Sets

Joshua C. Bowden

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Tags: BLAS, Computer science, GPU cluster, MPI, nVidia, nVidia Quadro FX 580, OpenCL, Performance, Tesla C1060, Tesla S2050, Tesla T10

March 31, 2011 by hgpu

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

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high performance computing on graphics processing units: hgpu.org

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

Autotuning GEMMs for Fermi

Hierarchical QR factorization algorithms for multi-core cluster systems

Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Auto-tunable GPU BLAS

Solving Linear Recurrences on Hybrid GPU Accelerated Manycore Systems

A framework to implement a multifrontal scheme on GPU architectures with OpenCL

Compiling Python to a hybrid execution environment

High Performance Matrix Inversion on a Multi-core Platform with Several GPUs

Accelerating GPU kernels for dense linear algebra

Mixed-Tool Performance Analysis on Hybrid Multicore Architectures

Application of the OpenCL API for Implementation of the NIPALS Algorithm for Principal Component Analysis of Large Data Sets

Recent source codes

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

TRUST: a thermalhydraulic software package for CFD simulations

Modular: The Modular Platform (includes MAX & Mojo)

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

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