英特尔TBB的scalable_allocator如何工作？ [英] How does Intel TBB's scalable_allocator work?

问题描述

在英特尔®线程构建模块中， tbb :: scalable_allocator 实际上是做什么的？

What does the tbb::scalable_allocator in Intel Threading Building Blocks actually do under the hood ?

肯定是有效的。我刚刚使用它通过更改单个 std：25％的应用程序执行时间（并且看到CPU利用率从4％系统的从200％增加到350％）。 ：vector< T> 到 std :: vector< T，tbb :: scalable_allocator< T> > 。另一方面，在另一个应用程序中，我已经看到它翻倍了一个已经很大的内存消耗，并发送东西到交换城市。

It can certainly be effective. I've just used it to take 25% off an apps' execution time (and see an increase in CPU utilization from ~200% to 350% on a 4-core system) by changing a single std::vector<T> to std::vector<T,tbb::scalable_allocator<T> >. On the other hand in another app I've seen it double an already large memory consumption and send things to swap city.

英特尔自己的文档不会给予很多（例如本常见问题结尾处的简短部分）。任何人都可以告诉我在我自己去探索代码之前使用了什么技巧？

Intel's own documentation doesn't give a lot away (e.g a short section at the end of this FAQ). Can anyone tell me what tricks it uses before I go and dig into its code myself ?

UPDATE ：第一次使用TBB 3.0 ，并且看到了我从scalable_allocator的最好的加速。将单个向量< int> 更改为向量< int，scalable_allocator< int> > 将事物的运行时间从85秒减少到35秒（Debian Lenny，Core2，从测试中得到TBB 3.0）。

UPDATE: Just using TBB 3.0 for the first time, and seen my best speedup from scalable_allocator yet. Changing a single vector<int> to a vector<int,scalable_allocator<int> > reduced the runtime of something from 85s to 35s (Debian Lenny, Core2, with TBB 3.0 from testing).

推荐答案

分配器上有一个很好的文章：

There is a good paper on the allocator: The Foundations for Scalable Multi-core Software in Intel Threading Building Blocks

我有限的经验：我重载了全局新/删除与我的AI应用程序的tbb :: scalable_allocator。但在时间剖面中没有什么变化。我没有比较内存使用。

My limited experience: I overloaded the global new/delete with the tbb::scalable_allocator for my AI application. But there was little change in the time profile. I didn't compare the memory usage though.

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