Performance

As easy as my title and the rules of the C++ core guidelines sound, getting more performance out of the iostreams is no no-brainer.

When you access an element outside a container of the STL, the result is not so promising. Your effect may be an error or undefined behavior. Undefined behavior means all bets are open.

In 99 % of your use cases for a sequential container, you are outstanding with a std::array or a std::vector. What? If you don't believe me, read this post.

Today, I will write about the remaining ten rules of performance. Ten rules seem to be a lot, but only two have content.

In this post, I continue my journey through the rules to performance in the C++ Core Guidelines.  I will mainly write about design for optimization.

Before I write about the rules of performance, I will do a straightforward job. Accessing the elements of a container one by one.

After I've calculated in three different ways the sum of a std::vector I want to draw my conclusions.

Until now, I've used two strategies to summate a std::vector. First, I did the whole math in one thread (Single Threaded: Summation of a vector); second multiple threads shared the same variable for the result (Multithreaded: Summation of a vector). In particular, the second strategy was extremely naive. In this post, I will apply my knowledge of both posts. My goal is that the thread will perform their summation as independently from each other as possible and therefore reduce the synchronization overhead. 

My goal is 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.

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