high performance computing on graphics processing units: hgpu.org

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hgpu.org » Programming » CUDA

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

Log File Regular Expression Pattern Matching And Capture With GPUs

António Luís Pinto Silva

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M, Package, pattern matching, Thesis

December 22, 2016 by xhgpu

An EoS-meter of QCD transition from deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stocker, Xin-Nian Wang

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Tags: CUDA, Deep learning, High Energy Physics - Phenomenology, Keras, Machine learning, Neural networks, Nuclear Theory, nVidia, Physics, QCD, TensorFlow, Tesla K20

December 20, 2016 by hgpu

Accelerating Spark RDD Operations with Local and Remote GPU Devices

Yasuhiro Ohno, Shin Morishima, Hiroki Matsutani

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 980 Ti, Spark

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

Speedup for quantum optimal control from GPU-based automatic differentiation

Nelson Leung, Mohamed Abdelhafez, Jens Koch, David I. Schuster

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Tags: Algorithms, CUDA, Deep learning, Mesoscale and Nanoscale Physics, Numerical simulation, nVidia, Package, Physics, Python, Quantum Physics, TensorFlow, Tesla K40

December 17, 2016 by hgpu

Parallel Level set algorithm with MPI and accelerated on GPU

Zhenlin Wang

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Tags: Computational Physics, CUDA, Finite difference, MPI, nVidia, Package, PDEs, Physics

December 17, 2016 by hgpu

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

Ning Xie

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Tags: Code generation, Computer science, CUDA, nVidia, OpenMP, Tesla M2050, Thesis

December 14, 2016 by hgpu

nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

Sven Koitka, Christoph M. Friedrich

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Tags: Benchmarking, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package, Tesla K80

December 14, 2016 by hgpu

GaDei: On Scale-up Training As A Service For Deep Learning

Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bowen Zhou

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Tags: Computer science, CUDA, Deep learning, nVidia, Tesla K20, Tesla K80

December 14, 2016 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

Accelerating Lattice QCD Multigrid on GPUs Using Fine-Grained Parallelization

Streaming GPU Singular Value and Dynamic Mode Decompositions

Log File Regular Expression Pattern Matching And Capture With GPUs

An EoS-meter of QCD transition from deep learning

Accelerating Spark RDD Operations with Local and Remote GPU Devices

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

Speedup for quantum optimal control from GPU-based automatic differentiation

Parallel Level set algorithm with MPI and accelerated on GPU

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

GaDei: On Scale-up Training As A Service For Deep Learning

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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