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?* 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?* 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 -- You received this message because you are subscribed to the Google Groups "sage-devel" group. To unsubscribe from this group and stop receiving emails from it, send an email to sage-devel+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/sage-devel/35e86100-570e-4d71-acdf-7b166ab27469n%40googlegroups.com.
