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See the note at the top of the article: Note, this post was originally published September 19, 2013. It was updated on September 19, 2017.
by grej 9y ago
See the note at the top of the article:
Note, this post was originally published September 19, 2013. It was updated on September 19, 2017.
- p1esk 9y agoSo, what exactly was updated?
- bsprings 9y agoWhen I originally wrote the post in 2013, the GPU compilation part of Numba was a product (from Anaconda Inc., nee Continuum Analytics) called NumbaPro. It was part of a commercial package called Anaconda Accelerate that also included wrappers for CUDA libraries like cuBLAS, as well as MKL acceleration on the CPU. Continuum gradually open sourced all of it (and changed their name to Anaconda). The compiler functionality is all open source within Numba. Most recently they released the CUDA library wrappers in a new open source package called pyculib. Some other minor things changed, such as what you need to import. Also, the autojit and cudajit functionality is a bit better at type inference, so you don't have to annotate all the types to get it to compile. We thought it was a good idea to update the post in light of all the changes.