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Python on Gpus (Work in Progress!)
Introduction Shrinkage Factor Reference
Prototyping and Developing GPU-Accelerated Solutions with Python and CUDA Luciano Martins and Robert Sohigian, 2018-11-22 Introduction to Python
Tangent: Automatic Differentiation Using Source-Code Transformation for Dynamically Typed Array Programming
Python Guide Documentation 0.0.1
Hyperlearn Documentation Release 1
Zmcintegral: a Package for Multi-Dimensional Monte Carlo Integration on Multi-Gpus
Python Guide Documentation Publicación 0.0.1
Aayush Mandhyan
Tensorflow, Pytorch and Horovod
Numba: a Compiler for Python Functions
Or How to Avoid Recoding Your (Entire) Application in Another Language Before You Start
GPU Computing with Python: Performance, Energy Efficiency And
Python Programming Training
Accelerating a Geometrical Approximated PCA Algorithm Using AVX2 and CUDA
Awkward Arrays in Python, C++, and Numba
Composable Multi-Threading for Python Libraries
Goai and Pygdf: GPU-Accelerated Data Science with Jupyter Notebooks
Top View
Extending Python for High- Performance Data-Parallel Programming
Charmpy: Parallel Programming with Python Objects
Programming Real-Time Sound in Python
High-Performance-Python.Pdf
Accelerating Data Science Productivity with Gpus: from Workstation to Cluster
Advanced Python: Best Practices and Design Patterns
Picking a Python Version: a Manifesto
Profiling and Optimizing Python Code
Numerical and Scientific Computing in Python
Labs for Foundations of Applied Mathematics Python Essentials
High-Performance Python for Crystallographic Computing Alexandre Boulle, Jérôme Kieffer
Numba and Conda Tools for Fast Reliable Data Analysis
High Performance Python PRACTICAL PERFORMANT PROGRAMMING for HUMANS
Turbocharging the AI Pipeline with Python and Anaconda
Understanding Numba the Python and Numpy Compiler
A Comparative Study of High-Productivity High-Performance
View the Slides
Arxiv:2102.05955V1 [Physics.Comp-Ph] 11 Feb 2021