What does HackerNews think of neanderthal?

I really like the Neanderthal library because it does a pretty good job of abstracting over Nvidia, AMD, and Intel hardware to provide matrix operations in an extremely performant manner for each one with the same code. Dragan goes into a lot of detail about the hardware differences. His library provides some of the fastest implementations of using the given hardware too, it's not a hand-wavy, half-baked performance abstraction, the code is really fast. https://github.com/uncomplicate/neanderthal

Uses Clojure and Neanderthal (https://github.com/uncomplicate/neanderthal).

Uses open source library Neanderthal https://github.com/uncomplicate/neanderthal

Uses free open source libraries:

https://github.com/uncomplicate/neanderthal

Uses open source libraries

https://github.com/uncomplicate/neanderthal

https://github.com/uncomplicate/clojurecuda

https://github.com/uncomplicate/clojurecl

Software: Clojure with Neanderthal

https://github.com/uncomplicate/neanderthal

Based on an open source library Neanderthal:

https://neanderthal.uncomplicate.org

https://github.com/uncomplicate/neanderthal

This series builds everything from matrix operations provided by Neanderthal [1].

If you have any questions about Clojure or numerical computing with Clojure/Java, or if you spot an error in the article, please discuss here.

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https://github.com/uncomplicate/neanderthal

Neanderthal source code:

https://github.com/uncomplicate/neanderthal

The first part of the article:

Neanderthal vs ND4J - vol 1 - Native performance, Java and CPU

https://dragan.rocks/articles/18/Neanderthal-vs-ND4J-vol1

HN discussion related to the first part: https://news.ycombinator.com/item?id=17375306

Neanderthal on GitHub: https://github.com/uncomplicate/neanderthal

Source code of the library is at: https://github.com/uncomplicate/neanderthal

source code of the library used in the tutorial: https://github.com/uncomplicate/neanderthal

This made me chuckle. If anybody remembers the dim and distant past of various singularitarian mailing lists on the internet, you might remember Eliezer Yudkowsky's theoretical 'annotative' language Flare[1]. It was one of the early steps on the (what was then quite straight in some people's minds) line to AGI.

As for this, possibly worthwhile for the authors to look into Dragan Djuric's work, he's done some really fast matrix stuff on Clojure[2].

1: http://yudkowsky.net/obsolete/plan.html#tech_comp_flare

2: https://github.com/uncomplicate/neanderthal

The source code for the software used: https://github.com/uncomplicate/neanderthal

The software used in the tutorial: https://github.com/uncomplicate/neanderthal

http://neanderthal.uncomplicate.org

Source:

https://github.com/uncomplicate/neanderthal

Also used for Bayesian data analysis on the GPU in:

https://github.com/uncomplicate/bayadera

Direct link to Getting Started: http://clojurecl.uncomplicate.org/articles/getting_started.h...

source code: https://github.com/uncomplicate/clojurecl

Heavily used in other Clojure libraries:

https://github.com/uncomplicate/neanderthal

https://github.com/uncomplicate/bayadera

Also, the CUDA equivalent: https://github.com/uncomplicate/clojurecuda

The source of the library that is used in the article: https://github.com/uncomplicate/neanderthal

Source code: https://github.com/uncomplicate/neanderthal

Neanderthal is a Clojure library for native-speed vectors and matrix computations. It has pluggable engines and comes with support for GPU computations. Program in Clojure and get the full speed of native and GPU libraries.

Of course, it is open source and the code is at https://github.com/uncomplicate/neanderthal