I ran 4 FireFox sessions each one running youtube videos. I decided to run a test on my Second System (you can view it in my system specs).
Win XP was the last MS OS that needed some help, but since then I never see any video lagging on any of my machines. Your current specs is what I had to deal with.Īlso not much of a fan of memory managers. Mathematical Operations and Elementary Functions.First off, I didn't mean to incite riot here, but your current "My system specs" are not what you showed in the third post.Multi-processing and Distributed Computing.Performance critical code should be inside a function.Measure performance with and pay attention to memory allocation.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. Access arrays in memory order, along columns.Consider StaticArrays.jl for small fixed-size vector/matrix operations.Optimize network I/O during parallel execution.Noteworthy Differences from other Languages.High-level Overview of the Native-Code Generation Process.Talking to the compiler (the :meta mechanism).printf() and stdio in the Julia runtime.Proper maintenance and care of multi-threading locks.Static analyzer annotations for GC correctness in C code.Reporting and analyzing crashes (segfaults).In the following sections, we briefly go through a few techniques that can help make your Julia code run as fast as possible. Performance critical code should be inside a functionĪny code that is performance critical should be inside a function. Code inside functions tends to run much faster than top level code, due to how Julia's compiler works. 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.
The functions should take arguments, instead of operating directly on global variables, see the next point.
Avoid global variablesĪ global variable might have its value, and therefore its type, change at any point. This makes it difficult for the compiler to optimize code using global variables. #Wise memory optimizer starts two instances at boot up code#
Uses of non-constant globals can be optimized by annotating their types at the point of use: global x = rand(1000) 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. #Wise memory optimizer starts two instances at boot up code#.
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