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Measurements of performance of hardware and general purpose classical molecular dynamics simulation software

Jose O. Sotero-Esteva

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Tags: CUDA, Molecular dynamics, nVidia, Physics, Tesla C2075

October 5, 2013 by hgpu

Parametric GPU Code Generation for Affine Loop Programs

Athanasios Konstantinidis, Paul H. J. Kelly, J. Ramanujam, P. Sadayappan

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GT 540 M, nVidia GeForce GTX 580, OpenCL, Package, Tesla K20, Tesla M2070

October 5, 2013 by hgpu

Facial Expression Recognition – Review

Umesh Balkrishna Chavan, Dinesh B Kulkarni

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Tags: Computer science, Computer vision, CUDA, nVidia, Pattern recognition

October 4, 2013 by hgpu

Parallel Computing Using GPU for Efficient Traffic Simulation

Sadat Sakif Ahmed

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 560, Thesis, Vehicular traffic

October 4, 2013 by hgpu

Advanced Optimization Techniques for Sparse Grids on Modern Heterogeneous Systems

Alin Florindor Murarasu

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia Quadro 6000, Thesis

October 4, 2013 by hgpu

Cudagrind: A Valgrind Extension for CUDA

Thomas M. Baumann, Jose Gracia

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Tags: Computer science, CUDA, Memory, nVidia, Package, Software Engineering

October 4, 2013 by hgpu

3D Non-Local Means denoising via multi-GPU

Giuseppe Palma, Francesco Piccialli, Pasquale De Michele, Salvatore Cuomo, Marco Comerci, Pasquale Borrelli, Bruno Alfano

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Tags: Algorithms, CUDA, Filtering, Image processing, nVidia, Signal denoising, Tesla S2050

October 4, 2013 by hgpu

MC-RANSAC: A Pre-processing Model for RANSAC using Monte Carlo method implemented on a GPU

Priyank Trivedi, Tejaswi Agarwal, K. Muthunagai

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Tags: Computer science, Computer vision, Consensus, CUDA, Filtering, Monte Carlo simulation, nVidia

October 4, 2013 by hgpu

GPU-accelerated triangle-triangle intersection tester algorithm

Romain Maffina

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 590, Tessellation, Thesis

October 3, 2013 by hgpu

Compiler Optimizations for SIMD/GPU/Multicore Architectures

Jun Liu

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Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, nVidia, Tesla C2050, Thesis

October 3, 2013 by hgpu

CUDA Enhanced Filtering in a Pipelined Video Processing Framework

Austin Aaron Dworaczyk Wiltshire

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Tags: CUDA, Filtering, Image processing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX Titan, Thesis

October 2, 2013 by hgpu

GPU-powered Simulation Methodologies for Biological Systems

Daniela Besozzi, Giulio Caravagna, Paolo Cazzaniga, Marco Nobile, Dario Pescini, Alessandro Re

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Tags: Biology, Computer science, CUDA, nVidia

October 2, 2013 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

Measurements of performance of hardware and general purpose classical molecular dynamics simulation software

Parametric GPU Code Generation for Affine Loop Programs

Facial Expression Recognition – Review

Parallel Computing Using GPU for Efficient Traffic Simulation

Advanced Optimization Techniques for Sparse Grids on Modern Heterogeneous Systems

Cudagrind: A Valgrind Extension for CUDA

3D Non-Local Means denoising via multi-GPU

MC-RANSAC: A Pre-processing Model for RANSAC using Monte Carlo method implemented on a GPU

GPU-accelerated triangle-triangle intersection tester algorithm

Compiler Optimizations for SIMD/GPU/Multicore Architectures

CUDA Enhanced Filtering in a Pipelined Video Processing Framework

GPU-powered Simulation Methodologies for Biological Systems

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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