Measure performance with and pay attention to memory allocationĪ useful tool for measuring performance is the macro. So all the performance issues discussed previously apply. In the following REPL session: julia> x = 1.0 Variables defined at top level scope inside modules are also global. It leads to more reusable code and clarifies what the inputs and outputs are.Īll code in the REPL is evaluated in global scope, so a variable defined and assigned at top level will be a global variable. Passing arguments to functions is better style. Uses of untyped globals can be optimized by annotating their types at the point of use: global x = rand(1000) If a global is known to always be of the same type, the type should be annotated. We find that global names are frequently constants, and declaring them as such greatly improves performance: const DEFAULT_VAL = 0 Variables should be local, or passed as arguments to functions, whenever possible. This also applies to type-valued variables, i.e. This makes it difficult for the compiler to optimize code using global variables. The value of an untyped global variable might change at any point, possibly leading to a change of its type. The functions should take arguments, instead of operating directly on global variables, see the next point. The use of functions is not only important for performance: functions are more reusable and testable, and clarify what steps are being done and what their inputs and outputs are, Write functions, not just scripts is also a recommendation of Julia's Styleguide. Code inside functions tends to run much faster than top level code, due to how Julia's compiler works. Performance critical code should be inside a functionĪny code that is performance critical should be inside a function. In the following sections, we briefly go through a few techniques that can help make your Julia code run as fast as possible. Instrumenting Julia with DTrace, and bpftrace.Reporting and analyzing crashes (segfaults).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.Noteworthy Differences from other Languages.Optimize network I/O during parallel execution.Consider StaticArrays.jl for small fixed-size vector/matrix operations.Access arrays in memory order, along columns.The dangers of abusing multiple dispatch (aka, more on types with values-as-parameters).Separate kernel functions (aka, function barriers).Break functions into multiple definitions.Avoid containers with abstract type parameters.Measure performance with and pay attention to memory allocation.Performance critical code should be inside a function.Multi-processing and Distributed Computing.Mathematical Operations and Elementary Functions.
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