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To a programmer, intrinsics look just like regular library functions you include the relevant header, and you can use the intrinsic. Unlike this blog post, that one doesn’t have practical problems nor benchmarks, instead it tries to provide an overview of what’s available. For a more in-depth introduction, you can read my other article on the subject.
![std lib vector code std lib vector code](https://ati.ttu.ee/IAY0340/labs/Tutorials/VHDL/Packages/pictures/Unresolved_std_logic.jpg)
Modern SIMD instructions were introduced to Pentium processors with the release of Pentium 3 in 1999 (that instruction set is SSE, nowadays it’s sometimes called SSE 1), more of them have been added since then. This article focuses on PCs and servers running on modern AMD64 processors.Įven with the focus on AMD64 platform, the topic is way too broad for a single blog post. SIMD instructions are available on many platforms, there’s a high chance your smartphone has it too, through the architecture extension ARM NEON. SIMD stands for “single Instruction, multiple data”. One approach to leverage vector hardware are SIMD intrinsics, available in all modern C or C++ compilers. So far, none of them have completely succeeded, and I’m not convinced it’s possible.
Std lib vector code code#
Language designers, compiler developers, and other smart people have been trying for many years to compile scalar code into vector instructions in a way that would leverage the performance potential. Similarly, when you write int i = j + k to add 2 integer numbers, you could have added four or eight numbers instead, with corresponding SSE2 or AVX2 instructions. The processor could have added four float numbers to another four numbers, or even eight numbers to another eight numbers if that processor supports AVX. If you want to maximize performance, you need to write code tailored to these vectors.Įvery time you write float s = a + b you’re leaving a lot of performance on the table.
![std lib vector code std lib vector code](https://internationalkungfuquest.com/images/vector-in-c-tutorial.jpg)
Unlike scalar processors, which process data individually, modern vector processors process one-dimensional arrays of data. After all, that’s one of the major reasons why we still pick C or C++ language these days.Īll modern processors are actually vector under the hood.
Std lib vector code software#
Many developers write software that’s performance sensitive. For the cases presented in this blog post, vectorization improved performance by a factor of 3 to 12. When done right, supplementing C or C++ code with vector intrinsics is exceptionally good for performance.