Random floating point numbers are generated uniformly in $[0, 1)$. The provided RNGs can generate uniform random numbers of the following types: Float16, Float32, Float64, BigFloat, Bool, Int8, UInt8, Int16, UInt16, Int32, UInt32, Int64, UInt64, Int128, UInt128, BigInt (or complex numbers of those types). However, the default RNG is thread-safe as of Julia 1.3 (using a per-thread RNG up to version 1.6, and per-task thereafter).
In a multi-threaded program, you should generally use different RNG objects from different threads or tasks in order to be thread-safe. (which can also be given as a tuple) to generate arrays of random values. Some also accept dimension specifications dims. Most functions related to random generation accept an optional AbstractRNG object as first argument.
Numpy random permute code#
Static analyzer annotations for GC correctness in C code.Proper maintenance and care of multi-threading locks.printf() and stdio in the Julia runtime.Talking to the compiler (the :meta mechanism).
High-level Overview of the Native-Code Generation Process.Subsequences, permutations and shuffling.Noteworthy Differences from other Languages.Multi-processing and Distributed Computing.Mathematical Operations and Elementary Functions.