high performance computing on graphics processing units: hgpu.org

Programming

Hierarchical N-body simulations with auto-tuning for heterogeneous systems

Rio Yokota, Lorena A. Barba

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Tags: Algorithms, CUDA, Heterogeneous systems, Mathematical Software, N-body simulation, Numerical Analysis, nVidia, nVidia GeForce GTX 590, Physics, Tesla C2050

March 22, 2012 by hgpu

High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing

Cagdas Bilen, Yao Wang, Ivan Selesnick

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Tags: Algorithms, Compression, Computer science, Data Structures and Algorithms, Magnetic resonance imaging, MRI, nVidia, nVidia Quadro FX 4800, Sensing

March 22, 2012 by hgpu

Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU

Carla Ramiro Sanchez, Jose J. Lopez-Espin, Domingo Gimenez, Antonio M. Vidal

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

March 21, 2012 by hgpu

Fast Antenna Characterization Using the Sources Reconstruction Method on Graphics Processors

J. A. Lopez-Fernandez, M. Lopez-Portugues, Y. Alvarez, C. Garcia, D. Martinez-Alvarez, F. Las-Heras

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Tags: Algorithms, CUDA, Electrodynamics, nVidia, nVidia GeForce GTX 460

March 21, 2012 by hgpu

CPU-GPU Hybrid Parallel Binomial American Option Pricing

Nan Zhang, Eng Gee Lim, Ka Lok Man, Chi-Un Lei

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Tags: Algorithms, CUDA, Finance, Heterogeneous systems, Hybrid computing, nVidia, nVidia Quadro NVS 160 M

March 21, 2012 by hgpu

Analysis of the Performance of the Fish School Search Algorithm Running in Graphic Processing Units

Anthony J. C. C. Lins, Carmelo J. A. Bastos-Filho, Debora N. O. Nascimento, Marcos A. C. Oliveira Junior, Fernando B. de Lima-Neto

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Search

March 19, 2012 by hgpu

Parallelization of Particle Filter Algorithms

Matthew A. Goodrum, Michael J. Trotter, Alla Aksel, Scott T. Acton, Kevin Skadron

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Video tracking

March 19, 2012 by hgpu

Improving Cache Locality for Ray Casting with CUDA

Yuki Sugimoto, Fumihiko Ino, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Rendering

March 18, 2012 by hgpu

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

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

Compressed Multiple-Row Storage Format

Zbigniew Koza, Maciej Matyka, Sebastian Szkoda, Lukasz Miroslaw

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Tags: Algorithms, Compression, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 480, Physics, Sparse matrix, Tesla C2070

March 15, 2012 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

Programming

Hierarchical N-body simulations with auto-tuning for heterogeneous systems

High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing

Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU

Fast Antenna Characterization Using the Sources Reconstruction Method on Graphics Processors

CPU-GPU Hybrid Parallel Binomial American Option Pricing

Analysis of the Performance of the Fish School Search Algorithm Running in Graphic Processing Units

Parallelization of Particle Filter Algorithms

Improving Cache Locality for Ray Casting with CUDA

CUDA 2D Stencil Computations for the Jacobi Method

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

Compressed Multiple-Row Storage Format

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

Most viewed papers (last 30 days)