Multithreaded: Summation of a Vector

My goal is it to sum up all elements of a vector. I used in the last post a single thread. In this post I use multiple threads and therefore the full power of my PC. The addition will be done on a shared variable. What at first glance seems like a good idea is a very naive strategy. The synchronization overhead of the summation variable is higher than the performance benefit of my four or two cores.

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Single Threaded: Summation of a Vector

What is the fastest way to add the elements of a std::vector?. A question which I will pursue in the next posts. I use the single threaded addition as reference number. In further posts I discuss atomics, locks, and thread local data.

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Tags: atomics, lock
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Thread-Safe Initialization of a Singleton

There are a lot of issues with the singleton pattern. I'm totally aware of that. But the singleton pattern is an ideal use case for a variable, which has only to be initialized in a thread safe way. From that point on you can use it without synchronization. So in this post I discuss different ways to initialize a singleton in a multithreading environment. You get the performance numbers and can reason about your uses cases for the thread safe initialization of a variable.

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Ongoing Optimization: Relaxed Semantic with CppMem

With the relaxed semantic, we have no synchronisations and ordering constraints on atomic operations.

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Ongoing Optimization: A Data Race with CppMem

But we can do better and further improve the acquire-release semantic of the last post. Why should x be an atomic? There is no reason. That was my first, but incorrect assumption. See why?

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Ongoing Optimization: Acquire-Release Semantic with CppMem

With the acquire-releae semantic, we break the sequential consistency. In the acquire-release semantic the synchronization takes place between atomic operations on the same atomic and not between threads.

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Ongoing Optimization: Sequential Consistency with CppMem

With atomic data types you can tailor your program to your needs and therefore optimize it. But now we are in the domain of the multithreading experts.

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