tensorstore VS librapid

shhh...don't tell them about Tensorflow, or tf.Tensor, or theano.tensor, or paddle.to_tensor or torch.Tensor or torch.Tensor or mindspore.Tensor or tensorstore or ... But in all seriousness your pitchforks might be a little late to the party here. Rightly or wrongly (large) segments of the machine learning community have adopted terminology to describe things (for your sake, I hope you have not seen what they did with the term 'deconvolution'). This is not a new phenomena either and has been happening for at least a decade and almost certainly longer. However, there are other libraries that use the array terminology np.array, mx.nd.array, chainer, jax.numpy.array so it is definitely not unanimous. Whether you think this is an egregious mis-representation of the nomenclature or not is irrelevant now as it has a pretty established use within the community. Language is an evolving untamed beast - don't get angry, get building!

There are tons of other ways to store vector data, one was just recently released - https://github.com/google/tensorstore

Continue reading | Github | Google Full Blog

Today we are introducing TensorStore, an open-source C++ and Python software library designed for storage and manipulation of n-dimensional data that:

In addition to all of these changes, I used LibRapid and Surge to write a simple Flappy Bird genetic AI as a proof of concept. The code runs well on all platforms and can simulate over 20,000 birds without too much trouble. If you want to check it out, here's the GitHub page: https://github.com/Pencilcaseman/FlappyBirdAI

LibRapid: https://github.com/LibRapid/librapid Documentation: https://librapid.readthedocs.io/en/latest/

tl;dr

I'm developing an incredibly fast library for arrays and mathematics, and I've implemented a few new features and made some improvements. I'd love for you to check it out!

Links:

GitHub: https://github.com/LibRapid/librapid/

Documentation: https://librapid.readthedocs.io/en/latest/

Discord: https://discord.com/invite/cGxTFTgCAC

Hey everyone!

I am the lead developer of LibRapid (https://github.com/LibRapid/librapid/), a high-performance C++ library for array manipulation and mathematics. I've been working hard to bring you some significant updates and improvements to the library, and I'm excited to share them with you! These changes will make it even easier for you to use LibRapid in your projects and enable you to do even more with it.

Here's a rundown of the most notable changes:

CUDA improvements: We've fixed several bugs and made performance enhancements for the CUDA implementation.

Expanded support for BLAS libraries: LibRapid now has greater support for BLAS libraries, including Intel's MKL.

Test suite and documentation updates: We've updated our test suite and overhauled the documentation for clarity and ease of use.

Matrix Transposition: The library now supports matrix transposition with highly optimised, architecture-specific SIMD implementations and generic implementations for non-trivial types.

New math utility functions: We've added various new utility functions for complex numbers, multi-precision arithmetic, and more.

Matrix transposition and SIMD: LibRapid now supports matrix transposition and SIMD matrix transpose for SSE and AVX2, improving performance and memory alignment.

Random number generation: We've started implementing a new random number generation library.

Fast Fourier Transforms: LibRapid now supports fast Fourier transforms on 1D arrays, with more features coming soon! (We're using FFTW, so you can be sure you are getting the best possible performance)

Array BLAS operations: We're implementing BLAS functions that operate on our high-level array type, allowing you to access the high-performace, specialised routines without the tedious interface. (Many operations already use BLAS functions internally)

You can find more detailed information about the library and example code and usage guides in the documentation (https://librapid.readthedocs.io/en/latest/).

We're always looking for feedback and contributions, so if you're interested in using LibRapid or helping out, please feel free to create a pull request or contact us via our Discord Server (https://discord.com/invite/cGxTFTgCAC). I'm putting in a lot of effort to enhance LibRapid and would greatly appreciate any sponsorships to support my work. If you or your organisation is interested in sponsoring LibRapid, please feel free to reach out to me to discuss your ideas.

We hope you enjoy the latest updates to LibRapid, and look forward to seeing what you build with it!

I am the lead developer of LibRapid, a high-performance C++ library for array manipulation and mathematics. I've been working hard to bring you some significant updates and improvements to the library, and I'm excited to share them with you! These changes will make it even easier for you to use LibRapid in your projects and enable you to do even more with it.

LibRapid is a high-performance C++ library focusing primarily on linear algebra operations, though also supporting a wide range of classes, functions and utilities such as vectors, complex numbers and more.

LibRapid GitHub Repository

Probably LibRapid, which is my high-performance array library for C++. Nothing huge but I'm quite proud of the features it currently implements (given time restrictions and rewrites) and it is, in many cases, faster than things like Eigen and XTensor :)