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FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

Ashwin Vishnu Mohanan, Cyrille Bonamy, Pierre Augier

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Tags: Benchmarking, cfd, CUDA, FFT, Fluid dynamics, MPI, Package, Python

July 7, 2018 by hgpu

XGBoost: Scalable GPU Accelerated Learning

Rory Mitchell, Andrey Adinets, Thejaswi Rao, Eibe Frank

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Tags: Computer science, CUDA, Java, Machine learning, nVidia, Package, Python, R, Scala, Tesla V100

July 5, 2018 by hgpu

Dank Learning: Generating Memes Using Deep Neural Networks

Abel L Peirson V, E Meltem Tolunay

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Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, Package, Python, TensorFlow

June 17, 2018 by hgpu

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Jie Yang, Yue Zhang

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Tags: CNN, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

June 17, 2018 by hgpu

NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen

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Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Python

June 2, 2018 by hgpu

Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)

Jyh-Miin Lin

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Tags: Algorithms, CUDA, FFT, Heterogeneous systems, Image processing, Image reconstruction, MRI, nVidia, nVidia GeForce GTX 965 M, OpenCL, Package, Python, Tesla K80

March 31, 2018 by hgpu

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

Sven Warris, N. Roshan N. Timal, Marcel Kempenaar, Arne M. Poortinga, Henri van de Geest, Ana L. Varbanescu, Jan-Peter Nap

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 1070, OpenCL, Package, Python, Sequence alignment, Smith-Waterman algorithm

January 13, 2018 by hgpu

An MPI-Based Python Framework for Distributed Training with Keras

Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

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Tags: Computer science, CUDA, Deep learning, Keras, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Python, Tesla K80

December 24, 2017 by hgpu

ChainerMN: Scalable Distributed Deep Learning Framework

Takuya Akiba, Keisuke Fukuda, Shuji Suzuki

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package, Python

November 5, 2017 by hgpu

GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

Marco Tazzari, Frederik Beaujean, Leonardo Testi

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 1060, Package, Python, Tesla P100

September 28, 2017 by hgpu

Empower Sequence Labeling with Task-Aware Neural Language Model

Liyuan Liu, Jingbo Shang, Frank Xu, Xiang Ren, Huan Gui, Jian Peng, Jiawei Han

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Tags: Computer science, Machine learning, Neural networks, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

September 16, 2017 by hgpu

Gpufit: An open-source toolkit for GPU-accelerated curve fitting

Adrian Przybylski, Bjorn Thiel, Jan Keller-Findeisen, Bernd Stock, Mark Bates

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Tags: Biology, Computer science, CUDA, Microscopy, nVidia, nVidia GeForce GTX 1080, Package, Python

August 17, 2017 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

XGBoost: Scalable GPU Accelerated Learning

Dank Learning: Generating Memes Using Deep Neural Networks

NCRF++: An Open-source Neural Sequence Labeling Toolkit

NengoDL: Combining deep learning and neuromorphic modelling methods

Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

An MPI-Based Python Framework for Distributed Training with Keras

ChainerMN: Scalable Distributed Deep Learning Framework

GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

Empower Sequence Labeling with Task-Aware Neural Language Model

Gpufit: An open-source toolkit for GPU-accelerated curve fitting

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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