> To give an explicit and specific example, I'm also interested in graph
> algorithms, without calling libraries written in C/C++ or Julia, is it
> possible to make graph algorithms faster on very-large graphs with what's
> mentioned above?
>
One relevant Python library is https://github.com/rapidsai/cugraph
which is a reimplementation of networkx on top of CUDA. Presumably,
with an appropriate GPU, it provides massive speedups over networkx.
I haven't tried it out yet, but I plan to.
-- William
On Sat, Sep 30, 2023 at 5:52 AM Jing Guo wrote:
>
> Dear all,
>
> I contributed to algebraic geometry and dynamics parts of SageMath. During
> the process, sometimes the tests could take some time to process, which I
> guess is reasonable, since schemes and other objects can take long time to
> compute.
>
> Today I saw a post [0] on JAX from Hacker News, in which currently the first
> top comment mentions that other than ML research, JAX is also suitable for
> scientific computing, as well as large-scale vectorized computations. From
> the GitHub page [1] of JAX, it seems that it makes use of and improves upon
> Autograd and XLA, hence very fast.
>
> I'm aware that SageMath is already fast for a lot of tasks, so I was
> wondering would it be possible to make use similar tricks/techniques and/or
> libraries that can make SageMath even faster?
>
> I found the following interesting comparisons of graph algorithms:
> https://www.timlrx.com/blog/benchmark-of-popular-graph-network-packages-v2
>
> Thank you.
>
> [0]: https://news.ycombinator.com/item?id=37698740
> [1]: https://github.com/google/jax
>
> Jing
>
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--
William (http://wstein.org)
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