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CUDA vs Propel: What are the differences?
What is CUDA? It provides everything you need to develop GPU-accelerated applications. A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
What is Propel? Machine learning for JavaScript. Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
CUDA and Propel belong to "Machine Learning Tools" category of the tech stack.
Propel is an open source tool with 2.81K GitHub stars and 80 GitHub forks. Here's a link to Propel's open source repository on GitHub.
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What is CUDA?
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
What is Propel?
Propel provides a GPU-backed numpy-like infrastructure for scientific computing in JavaScript.
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What tools integrate with CUDA?
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What are some alternatives to CUDA and Propel?
OpenCL
It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing.
OpenGL
It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.