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It can profile into Julia's core code and even (optionally) into C and Fortran libraries. By running "infrequently" there is very little performance overhead; while profiling, your code can run at nearly native speed.
Julia comes with it's own sampling profiler to measure the CPU time spent in functions or the number and size of allocations. There are various packages to visualize these profile traces.
NOTE: Jupyter/IJulia and SVG support has migrated to the ProfileSVG package. This package contains tools for visualizing and interacting with profiling data collected with Julia's built-in sampling profiler.
profile.jl — A simple profiler for Julia — Julia Language development ...
View the profiles of people named Julia. Join Facebook to connect with Julia and others you may know. Facebook gives people the power to share and makes...
There are two main approaches to CPU profiling julia code: Where profiling is enabled for a given call via the @profile macro.
This blog post will take you on a journey through the fundamental concepts, usage methods, common practices, and best practices of the Julia Profile Standard Library.