Notes on Julia Performance

Statically typing the program, or facilitating the type inference of the JIT compiler makes the code run faster. Some notes:

• Avoid global variables and run your performance-critical code within functions rather than in the global scope;
• Annotate the inner type of a container, so it can be stored in memory contiguously;
• Annotate the fields of composite types (use eventually parametric types);
• Loop matrices first by column and then by row.

Notes on profiling :

• To time a part of the code type @time myFunc(args) (be sure you ran that function at least once, or you will measure compile time rather than run-time).
• @benchmark myFunc(args) (from package BenchmarkTools) also works.
• Profile a function: Profile.@profile myfunct() (best after the function has been already ran once for JIT-compilation).
• Print the profiling results: Profile.print() (number of samples in corresponding line and all downstream code; file name:line number; function name;)
• Explore a chart of the call graph with profiled data: ProfileView.view() (from package ProfileView).
• Clear profile data: Profile.clear().