Here is my list of good reads from June:
Here is my list of good reads from May: Non-Ownership and Generic Programming and Regular types, oh my! Using C++17 std::optional Error Handling and std::optional std::accumulate vs. std::reduce How to Make SFINAE Pretty – Part 1: What SFINAE Brings to Code How to Make SFINAE Pretty – Part 2: the Hidden Beauty of SFINAE How…
If you wanted to create templates with non-type template parameters, you had to specify both the type and the value. In C++17, this is no longer the case, as template <auto> helps simplify these scenarios.
Transform-reduce is a pattern in which a set of data is first modified by applying a transformation on each of the elements and then it is reduced to a single value. In C++, this can be implemented straightforwardly with std::transform and std::accumulate. In C++17, an alternative for std::accumulate is available; std::reduce sums a range of elements just like std::accumulate, except that it does so out of order. That means you cannot use it with operators that are not communicative or associative (including overloads of operator+ that don’t exhibit these properties). On the other hand, there is yet another algorithm called std::transform_reduce that applies a functor to all the elements of a range and then reduces them, all in an out of order manner. And then, there are also parallel versions of these algorithms. In this post, I will try to compare the performance of these possible alternatives for implementing transform-reduce.
The title might be a little bit misleading because, on one hand, you might not find these things funny if you are stumbling upon them and not understanding what is going on, and, on the other hand, they are not really strange when you pay attention to what is going on. However, here is a list of five (randomly picked) C++ features that would probably get you giving a second thought to what’s going on.
Here is my list of good reads from April.
The C++ preprocessor is a text replacement tool used to transform the source code in order to produce a single text file that is then passed to the actual compiler. It has various capabilities, such as including files, conditional compilation, text macro replacement, error emitting, stringizing, or token concatenation. Often developers use the preprocessor when other alternatives are available and are more appropriate. In this article, I will show five examples of when and how you can avoid the use of the preprocessor.
In a previous post, I wrote about the C++ unit-testing framework Catch2. Catch uses another library, called Clara, for parsing command line arguments. Clara is an open-source, single-header, simple, composable and easy to use parser written by the author of Catch2. In this post, I will show how you can use Clara in C++ to parse command line arguments.
Here is my list of good reads from March.
In my book, Modern C++ Programming Cookbook, I discussed several testing frameworks for C++, more precisely, Boost.Test, Google Test, and Catch (which stands for C++ Automated Test Cases in a Header). Since the publishing of the book, a new version of Catch, called Catch2 has been released. This provides new functionalities, but also a series of breaking changes with Catch, including the drop of support for pre-C++11 compilers. For a list of changes, you can see the Catch2 release notes. Catch2 is available as a single-header library, is open-sources and cross-platform, and written for C++11/14/17/latest. In this article, I want to give a brief example of how you can write tests for C++ using Catch2.