Self-host math picture

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Philipp Oppermann
2018-08-13 17:07:13 +02:00
parent 06011a6317
commit 812c981f9b
2 changed files with 1 additions and 1 deletions

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@@ -429,7 +429,7 @@ When we discussed calling conventions above, we assummed that a x86_64 CPU only
However, modern CPUs also have a set of _special purpose registers_, which can be used to improve performance in several use cases. On x86_64, the most important set of special purpose registers are the _multimedia registers_. These registers are larger than the general purpose registers and can be used to speed up audio/video processing or matrix calculations. For example, we could use them to add two 4-dimensional vectors _in a single CPU instruction_: However, modern CPUs also have a set of _special purpose registers_, which can be used to improve performance in several use cases. On x86_64, the most important set of special purpose registers are the _multimedia registers_. These registers are larger than the general purpose registers and can be used to speed up audio/video processing or matrix calculations. For example, we could use them to add two 4-dimensional vectors _in a single CPU instruction_:
![`(1,2,3,4) + (5,6,7,8) = (6,8,10,12)`](http://mathurl.com/jz3nvev.png) ![`(1,2,3,4) + (5,6,7,8) = (6,8,10,12)`](vector-addition.png)
Such multimedia instructions are called [Single Instruction Multiple Data (SIMD)] instructions, because they simultaneously perform an operation (e.g. addition) on multiple data words. Good compilers are able to transform normal loops into such SIMD code automatically. This process is called [auto-vectorization] and can lead to huge performance improvements. Such multimedia instructions are called [Single Instruction Multiple Data (SIMD)] instructions, because they simultaneously perform an operation (e.g. addition) on multiple data words. Good compilers are able to transform normal loops into such SIMD code automatically. This process is called [auto-vectorization] and can lead to huge performance improvements.

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