So why is python such a slow programming language and how can we speed it up? In this video I'll be discussing the slow speed of the python language and why languages like Java, C and C++ can perform between 10x-200x faster.

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Parallel and distributed computing (http://docs.julialang.org/en/release-0.1/manual/parallel-computing/) have been an integral part of Julia's capabilities from an early stage. This session describes existing basic capabilities, which can be used as building blocks for higher level parallel libraries.

Lab: https://github.com/JuliaLang/julia-tutorial/raw/master/NumericalOptimization/tutorial.pdf
Solution: https://github.com/JuliaLang/julia-tutorial/blob/master/NumericalOptimization/Tutorial.jl Visit http://julialang.org/ to download Julia.

I just upgraded to the new Windows May update which now has WSL 2 (a very lightweight, Hyper V VM running full Linux). I essentially now want to move all my development over to Linux. But I would like to test if there are any regressions in performance when running Julia on the VM compared with the native binary.  What are some of your favorite snippets of code to run benchmarks?

One of the improvements I was most looking forward to in v1.5 is the new Pkg infrastructure with expected speed improvements for non-US users. But so far my end-user experience is largely unchanged. Is this expected, or should things be significantly faster by now? Maybe the new servers haven’t come online yet?  For example, in the (amazing) Juliacon Pluto video, the response time for running ]add Pluto was instantaneous. I almost spurted coffee all over my screen when I saw it. But this is what...