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Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Yasuaki Mitani, Fumihiko Ino, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, String matching

January 19, 2017 by hgpu

Light Loss-Less Data Compression, with GPU Implementation

Shunji Funasaka, Koji Nakano, Yasuaki Ito

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080

January 19, 2017 by hgpu

An N log N Parallel Fast Direct Solver for Kernel Matrices

Chenhan D. Yu, William B. March, George Biros

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Tags: Algorithms, Computational Complexity, Computer science, Factorization, Intel Xeon Phi, Machine learning, Package, Performance

January 16, 2017 by hgpu

Application of GPU Computing to Some Urban Traffic Problems

Walid Abdala Rfaei Jradi

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Tags: Algorithms, AMD Radeon R9 290X, ATI, Computer science, OpenCL, Thesis, Vehicular traffic

January 16, 2017 by hgpu

Decoding with Finite-State Transducers on GPUs

Arturo Argueta, David Chiang

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Tags: Algorithms, Computer science, CUDA, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Speech recognition, Tesla K40, Tesla P100

January 16, 2017 by hgpu

Parallelization of BVH and BSP on the GPU

Martin Imre

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Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, Raytracing, Rendering, Sorting, Thesis, Visualization

January 10, 2017 by hgpu

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Daniel George, E. A. Huerta

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Tags: Algorithms, Artificial intelligence, Astrophysics, CUDA, Deep learning, Filtering, Gravitation, Gravitational waves, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Physics, Signal processing, Tesla K40, Tesla P100

January 4, 2017 by hgpu

Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization

M. A. Clark, Balint Joo, Alexei Strelchenko, Michael Cheng, Arjun Gambhir, Richard Brower

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Tags: Algorithms, Computational Physics, CUDA, High Energy Physics - Lattice, nVidia, Physics, QCD, Tesla K20

December 26, 2016 by hgpu

Streaming GPU Singular Value and Dynamic Mode Decompositions

Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton

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Tags: Algorithms, Computer vision, CUDA, Mathematics, Matrix multiplication, Numerical Analysis, nVidia, Sparse matrix, Tesla K40

December 26, 2016 by hgpu

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Fabio Baruffa, Luigi Iapichino, Nicolay J. Hammer, Vasileios Karakasis

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Tags: Algorithms, Astrophysics, Computational Physics, Computer science, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, OpenMP, Performance, Physics

December 20, 2016 by hgpu

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Daniel Winkler, Michael Meister, Massoud Rezavand, Wolfgang Rauch

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Physics

December 20, 2016 by hgpu

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Farhad Merchant, Tarun Vatwani, Anupam Chattopadhyay, Soumyendu Raha, S K Nandy, Ranjani Narayan

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Tags: Algorithms, Computer science, CUDA, Factorization, FPGA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Performance, Tesla C2050

December 17, 2016 by hgpu

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

ThunderKittens: Tile primitives for speedy kernels

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

NVIDIA Nemotron Parse 1.1

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

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Light Loss-Less Data Compression, with GPU Implementation

An N log N Parallel Fast Direct Solver for Kernel Matrices

Application of GPU Computing to Some Urban Traffic Problems

Decoding with Finite-State Transducers on GPUs

Parallelization of BVH and BSP on the GPU

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization

Streaming GPU Singular Value and Dynamic Mode Decompositions

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Recent source codes

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

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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