使用SSE指令 [英] Using SSE instructions

问题描述

我已经写在其中一个大整数数组中的每个元素执行C ++的循环。在循环中，我掩盖了整数的某些位，然后找到最小值和最大值。我听说，如果我使用SSE指令执行这些操作将运行得更快相比，使用按位写一个正常的循环，以及如果其他条件。我的问题是我应该去这些SSE指令？另外，如果我的code在不同的处理器上运行时会发生什么？它仍然可以工作或这些指令是特定的处理器？

解决方案

1. SSE指令是特定处理器。你可以看一下哪些处理器支持维基百科上上交所版本。


2. 如果SSE code会更快与否取决于很多因素：第一是当然的问题是否是内存限制或CPU绑定。如果内存总线是瓶颈SSE不会有太大的帮助。尽量简化您的整数计算，如果这使得code更快，它可能是CPU绑定的，你必须加速它的一个很好的机会。


3. 请注意，写SIMD- code是比写C ++了很多困难 - code和所产生的code是更难改变。始终保持C ++ code是最新的，你会想它作为一个评论，并检查你的汇编code是正确的。


4. 考虑使用像IPP库，实现对各种处理器进行优化公共的低级别SIMD操作。

I have a loop written in C++ which is executed for each element of a big integer array. Inside the loop, I mask some bits of the integer and then find the min and max values. I heard that if I use SSE instructions for these operations it will run much faster compared to a normal loop written using bitwise AND , and if-else conditions. My question is should I go for these SSE instructions? Also, what happens if my code runs on a different processor? Will it still work or these instructions are processor specific?

解决方案

1. SSE instructions are processor specific. You can look up which processor supports which SSE version on wikipedia.
2. If SSE code will be faster or not depends on many factors: The first is of course whether the problem is memory-bound or CPU-bound. If the memory bus is the bottleneck SSE will not help much. Try simplifying your integer calculations, if that makes the code faster, it's probably CPU-bound, and you have a good chance of speeding it up.
3. Be aware that writing SIMD-code is a lot harder than writing C++-code, and that the resulting code is much harder to change. Always keep the C++ code up to date, you'll want it as a comment and to check the correctness of your assembler code.
4. Think about using a library like the IPP, that implements common low-level SIMD operations optimized for various processors.

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