Conclusions

Julia

• well-designed open-source scientific programming language
• great numeric types, libraries
• great general-purpose types, libraries
• modern, dynamic, flexible
• roughly as fast as compiled C
• easy learning curve
• dynamism and metaprogramming make new things possible
• solves the two-language problem: from tinkering to HPC

Didn't cover

• parallel computing
• general-purpose libraries: Strings, Sets, Arrays, Tuples, Dicts, Time, Streams, Network, Tasks,...
• documentation
• installation
• required libraries: LLVM, OpenBLAS, LAPACK, ARPACK, FFTW, GNU arb-size int & float, ...
• calling C and Fortran
• modules and package manager
• documentation
• code hosting on git
• running Julia on JuliaBox or from command-line

Acknowledgements

• David Sanders, Physics, Universidad Nacional Autónoma de México
• Andreas Noack, CS and AI Lab, MIT
• The Julia team
• NSF grant #1554149