high performance computing on graphics processing units: hgpu.org

hgpu.org » Algorithms

Implementation and Evaluation of Scientific Simulations on High Performance Computing Architectures

Bibrak Qamar, Jahanzeb Maqbool

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Tags: Algorithms, CUDA, Finance, Fluid dynamics, Numerical Analysis, nVidia, nVidia GeForce GTX 480, Review, Thesis, Visualization

December 16, 2011 by hgpu

A GPU-based Approximate SVD Algorithm

Blake Foster, Sridhar Mahadevan, Rui Wang

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Tags: Algorithms, Computer science, CUDA, Matrix decomposition, nVidia, nVidia GeForce GTX 480, Package

December 15, 2011 by hgpu

GPU Algorithms for the Estimation of Environmental Models Based on Large Datasets

Francesco Finazzi, Michela Cameletti

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Tags: Algorithms, CUDA, Earth and Space Sciences, Factorization, nVidia, Tesla C1060

December 15, 2011 by hgpu

GPU Collision Detection in Conformal Geometric Space

Eduardo Roa, Victor Theoktisto, Marta Fairen, Isabel Navazo

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Tags: Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, Optimization

December 15, 2011 by hgpu

Minimising Testing in Genetic Programming

W. B. Langdon

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Genetic programming, nVidia, Software Engineering

December 15, 2011 by hgpu

Free surface flow simulations on GPGPUs using the LBM

Christian Janssen, Manfred Krafczy

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Tags: Algorithms, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 275, Tesla C1060

December 15, 2011 by hgpu

Speed sign detection and recognition by convolutional neural networks

Maurice Peemen, Bart Mesman, Henk Corporaal

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Image recognition, Neural networks, nVidia, nVidia GeForce GTX 460

December 15, 2011 by hgpu

Graph Generation on GPUs using Dynamic Memory Allocation

A. Leist, K.A. Hawick

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Graph theory, Memory model, nVidia, nVidia GeForce GTX 580, Performance

December 14, 2011 by hgpu

Power consumption of mixed precision in the iterative solution of sparse linear systems

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

December 14, 2011 by hgpu

Rethinking Runtime Verification on Hundreds of Cores: Challenges and Opportunities

Tayfun Elmas, Semih Okur, Serdar Tasiran

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 465, Performance

December 14, 2011 by hgpu

Toward Accelerating the Matrix Inversion Computation of Symmetric Positive-Definite Matrices on Heterogeneous GPU-Based Systems

Huda Ibeid, Dinesh Kaushik, David Keyes, Hatem Ltaief

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Matrix inversion, nVidia, Tesla C2070

December 14, 2011 by hgpu

Voxelized Minkowski sum computation on the GPU with robust culling

Wei Li, Sara McMains

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Tags: Algorithms, CAD, Computer science, CUDA, Floating point error, nVidia, nVidia Quadro FX 5800, Voxelization

December 14, 2011 by hgpu

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Implementation and Evaluation of Scientific Simulations on High Performance Computing Architectures

A GPU-based Approximate SVD Algorithm

GPU Algorithms for the Estimation of Environmental Models Based on Large Datasets

GPU Collision Detection in Conformal Geometric Space

Minimising Testing in Genetic Programming

Free surface flow simulations on GPGPUs using the LBM

Speed sign detection and recognition by convolutional neural networks

Graph Generation on GPUs using Dynamic Memory Allocation

Power consumption of mixed precision in the iterative solution of sparse linear systems

Rethinking Runtime Verification on Hundreds of Cores: Challenges and Opportunities

Toward Accelerating the Matrix Inversion Computation of Symmetric Positive-Definite Matrices on Heterogeneous GPU-Based Systems

Voxelized Minkowski sum computation on the GPU with robust culling

Recent source codes

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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