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The language is built from the ground up as a fast numerical language. Take LLVM as a JIT and make the most dynamical lispy fast thing on top. It runs circles
by marmaduke 8y ago
The language is built from the ground up as a fast numerical language. Take LLVM as a JIT and make the most dynamical lispy fast thing on top. It runs circles around Python in terms of fast flexible computing.
That said, not enough for me to switch.
- evrydayhustling 8y agoI've heard some notes like that, but with nice wrappers for vectorised operations like numpy and TensorFlow, I don't think python world has been feeling numerical as a pain point (evidently you feel similarly enough not to switch). I wonder if Julia folks have some examples where relative extra speed really moved the needle for some application.
- ChrisRackauckas 8y agoWhen writing differential equation solvers, a lot of cases are far too small for vectorization to help and lots of loops are required. Especially adaptive stochastic differential equation solvers which require using data structures (priority queues, stacks) directly in the middle of tight loops, and you have to control heap-allocations to keep it fast. I couldn't find a way how to do this in MATLAB or Python well (I mean, heap-allocated objects tend to ruin this) but Julia was a great fit.