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CUDA 2D Stencil Computations for the Jacobi Method

Jose Maria Cecilia, Jose Manuel Garcia, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Differential equations, Linear Algebra, nVidia, Optimization, Tesla C1060, Tesla C870

March 16, 2012 by hgpu

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Dimitar Lukarski

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FEM, Finite difference, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla M1060, Tesla S1070, Thesis

March 16, 2012 by hgpu

Developing a CUDA solver for large sparse matrices for MARIN

Martijn de Jong

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Tags: Computer science, CUDA, Mathematics, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 580, nVidia Quadro NVS 295, Thesis

March 16, 2012 by hgpu

Multi-platform Linear Algebra

E. Ketelaer, D. Lukarski

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Tags: Computer science, CUDA, Differential equations, Distributed data structures, Linear Algebra, nVidia, Package, Partial differential equations, PDEs

March 16, 2012 by hgpu

On the Use of Small 2D Convolutions on GPUs

Shams A.H. Al Umairy, Alexander S. van Amesfoort, Irwan D. Setija, Martijn C. van Beurden, Henk J. Sips

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Tags: Computer science, CUDA, Electrodynamics, FFT, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Optimization, Physics, Tesla C1060

March 15, 2012 by hgpu

Iterative Statistical Kernels on Contemporary GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Cloud, Clustering, Computer science, nVidia, OpenCL, Sparse matrix, Tesla C1060, Tesla C2070, Tesla M2050

March 15, 2012 by hgpu

Performance analysis and optimization of the OP2 framework on many-core architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, nVidia, OpenMP, Optimization, Package, Performance, Tesla C2050

March 15, 2012 by hgpu

A Spiking Neural P system simulator based on CUDA

Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor

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Tags: Algorithms, Computer science, CUDA, Membrane computing, nVidia, nVidia GeForce 9400 M, Package

March 15, 2012 by hgpu

Targeting heterogeneous architectures via macro data flow

M. Aldinucci, M. Danelutto, P. Kilpatrick, M. Torquati

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, Tesla C2050

March 13, 2012 by hgpu

Expressive Array Constructs in an Embedded GPU Kernel Programming Language

Koen Claessen, Mary Sheeran, Bo Joel Svensson

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Tags: Algorithms, Code generation, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sorting

March 13, 2012 by hgpu

Parallel Branch and Bound on a CPU-GPU System

Abdelamine Boukedjar, Mohamed Esseghir Lalami, Didier El-Baz

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Tags: Computer science, CUDA, nVidia, Tesla C2050

March 13, 2012 by hgpu

Analyzing CUDA’s Compiler through the Visualization of Decoded GPU Binaries

Cedric Nugteren, Bart Mesman, Henk Corporaal

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Performance, Visualization

March 13, 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

CUDA 2D Stencil Computations for the Jacobi Method

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Developing a CUDA solver for large sparse matrices for MARIN

Multi-platform Linear Algebra

On the Use of Small 2D Convolutions on GPUs

Performance analysis and optimization of the OP2 framework on many-core architectures

A Spiking Neural P system simulator based on CUDA

Targeting heterogeneous architectures via macro data flow

Expressive Array Constructs in an Embedded GPU Kernel Programming Language

Parallel Branch and Bound on a CPU-GPU System

Analyzing CUDA’s Compiler through the Visualization of Decoded GPU Binaries

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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