November 2016
Intermediate to advanced
697 pages
14h 44m
English
Content preview from Julia: High Performance ProgrammingBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Trading performance for accuracy
In this book, we largely focus on performance. However, at this stage, it should be said that accurate math is usually an even bigger concern. All basic floating-point arithmetic in Julia follows strict IEEE 754 semantics. Rounding is handled carefully in all base library code to guarantee the theoretical best error limits. In some situations, however, it is possible to trade off performance for accuracy and vice versa.
The fastmath macro
The @fastmath macro is a tool to loosen the constraints of IEEE floating point operations in order to achieve greater performance. It can rearrange the order of evaluation to something with is mathematically equivalent but that would not be the same for discrete floating point numbers ...
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