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As an aside: I do a lot of numerical research and have mainly been using python with Jax due to how trivial it is to run my code on the GPU with Jax. However, I
by tnecniv 3y ago
As an aside: I do a lot of numerical research and have mainly been using python with Jax due to how trivial it is to run my code on the GPU with Jax. However, I’ve gotten increasingly fed up with Python as a language and Julia is basically my ideal language, feature wise. Do you think it’s worth the switch and, if so, is it easy to run stuff on the GPU these days? Last I checked it still required some care to do that.
- jakobnissen 3y agoI think it depends on a few things. Python are light years ahead of Julia when it comes to machine learning, so if your GPU code is that, don't pick Julia yet. Do you need a lot of niche libraries? Then maybe not: Julia has many fewer packages than Python. Also, what is your tolerance for "rough edges"? Despite Julia being 10 years old at this point I feel like I run into rough edges in Julia quite often: Underspecified behaviour of function in edge cases, a compiler whose optimisations are somewhat unreliable at producing the most efficient code, and some language nice-to-haves that are conspicuously missing such as a good linter. Nonetheless, I've found it to be better than Python for my use cases. I also hear that Julia's GPU capabilities are excellent