C++

If you’re building software tuned for performance (in C++ or a similar language), you probably know this as a golden rule: Avoid memory allocations in your performance-critical code at (almost) all costs. However, sometimes that’s just not feasible and you need to make sure that your allocations are as efficient as possible.

In a recent project of mine (where performance is not of utmost importance, but also not negligible), I am not only in the situation that I need to allocate lots of small-ish objects with high frequency, but I also want to use std::shared_ptr for memory safety reasons.

In this article, I look into how pool allocators (especially boost::pool_allocator) can help in this situation.

Modern C++ has many neat RAII-based solutions for managing resources. However, every now and then one needs to manually make sure that some code is run when a resource is not needed anymore. One great solution to this problem is a scope guard. The concept is already very old, however I have surprisingly rarely seen them used (and used them too rarely myself). This article describes the concept, shows how to use it and gives some background on scope guards.

I often encounter variations of the following problem: You are given two or more lists (as e.g. std::vector) of elements and you want to sort them “in parallel”, using one of the lists as keys. I always found the solutions to these kind of problems to be clunky - but with C++23 (or the excellent range-v3 library) we get a very elegant way of doing this.

In fact, CodeChecker is much more than just a front-end to Clang’s Static Analyzer (clangsa from here on…) - but using it just to drive clangsa is already awesome enough that I think you should use it to hunt for bugs in your C++ projects.

In this article, I’ll show how to set up and use CodeChecker for a quick one-off analysis of a CMake based C++ project, and outline how to use CodeChecker in a collaborative setting with the option of integrating it into a CI system. If you are not on C++, CodeChecker might still be for you, as it also supports multiple analyzers for e.g. Java, Python, Go, or JavaScript.

std::vector has the interesting property of allowing to be used with incomplete types to a small degree. However, many legacy (and not-so-legacy) code bases use it in ways which are not allowed by the standard, and which do start breaking with C++20. In this article I’ll explain the limitations, a mistake I’ve seen several times now and why this starts breaking now.

When optimizing code for performance, it is often useful to have a very clear idea about what happens in memory. Reducing page faults, cache misses, TLB misses et cetera can be a major factor in speeding up your code. In this post I will demonstrate how you can inspect paging information regarding the memory of your process under Linux.

One C++17 problem I come across every now and then is to determine whether a certain class or function template specialization exists - say, for example, whether std::swap<SomeType> or std::hash<SomeType> can be used. I like to have solutions for these kind of problems in a template-toolbox, usually just a header file living somewhere in my project. In this article I try to build solutions that are as general as possible to be part of such a toolbox.

I recently needed to trace some error related to C++17 class template argument deduction and came across some corner cases. In this article, I document what I learned, show some “paradox” cases (which have nice, clean solutions as per the standard) and demonstrate a suspected Clang bug.