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Concurrent Number Cruncher: An Efficient Sparse Linear Solver on the GPU

Luc Buatois, Guillaume Caumon, Bruno Levy

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Tags: 3D Graphics and Realism, ATI, ATI Radeon X1900, Computer science, Linear Algebra, OpenGL, Sparse matrix

December 22, 2010 by hgpu

New Row-grouped CSR format for storing the sparse matrices on GPU with implementation in CUDA

Tomas Oberhuber, Atsushi Suzuki, Jan Vacata

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

December 13, 2010 by hgpu

Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy

Alfredo Buttari, Jack Dongarra, Jakub Kurzak, Piotr Luszczek, Stanimir Tomov

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Tags: Algorithms, Computer science, Linear Algebra, Mixed precision, Sparse matrix

December 12, 2010 by hgpu

Solving Sparse Linear Systems on NVIDIA Tesla GPUs

Mingliang Wang, Hector Klie, Manish Parashar, Hari Sudan

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Sparse matrix, Tesla T10P

November 27, 2010 by hgpu

Exact Sparse Matrix-Vector Multiplication on GPU’s and Multicore Architectures

Brice Boyer, Jean-Guillaume Dumas, Pascal Giorgi

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Tags: Computer science, CUDA, Mathematical Software, nVidia, nVidia GeForce GTX 280, OpenMP, Package, Sparse matrix, Symbolic Computation

November 11, 2010 by hgpu

Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines

Antonio Wendell De Oliveira Rodrigues, Frederic Guyomarch, Yvonnick Le Menach, Jean-Luc Dekeyser

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Sparse matrix, Tesla C870

November 9, 2010 by hgpu

A matrix approach to tomographic reconstruction and its implementation on GPUs

F. Vazquez, E. M. Garzon, J. J. Fernandez

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Tags: Biology, Image processing, Sparse matrix

November 6, 2010 by hgpu

The GPU as numerical simulation engine

J. Bolz, I. Farmer, E. Grinspun, P. Schroder

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Tags: Computer science, Fluid simulation, Navier-Stokes equations, NSEs, Numerical simulation, nVidia, Sparse matrix

November 5, 2010 by hgpu

Sparse matrix solvers on the GPU: conjugate gradients and multigrid

Jeff Bolz,Ian Farmer,Eitan Grinspun,Peter Schrooder

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Tags: Computer science, Fluid simulation, Navier-Stokes equations, NSEs, Numerical simulation, nVidia, Sparse matrix

November 5, 2010 by hgpu

Implementing sparse matrix-vector multiplication on throughput-oriented processors

Nathan Bell, Michael Garland

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, Package, Sparse matrix

November 4, 2010 by hgpu

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

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high performance computing on graphics processing units: hgpu.org

Concurrent Number Cruncher: An Efficient Sparse Linear Solver on the GPU

New Row-grouped CSR format for storing the sparse matrices on GPU with implementation in CUDA

Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy

Solving Sparse Linear Systems on NVIDIA Tesla GPUs

Exact Sparse Matrix-Vector Multiplication on GPU’s and Multicore Architectures

Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines

A matrix approach to tomographic reconstruction and its implementation on GPUs

The GPU as numerical simulation engine

Sparse matrix solvers on the GPU: conjugate gradients and multigrid

Implementing sparse matrix-vector multiplication on throughput-oriented processors

Recent source codes

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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