A new Magnum feature provides efficient compile-time and runtime CPU detection and dispatch on x86, ARM and WebAssembly. The core idea behind allows adding new variants without having to write any dispatching code.
On 19th December 2010, Magnum saw its first commit. A bunch more commits happened since then and I learned some things along the way.
Flexible and efficient mesh representation, custom attributes, new data types and a ton of new processing, visualization and analyzing tools. GPU-friendly geometry storage as it should be in the 21st century.
The new release brings Python bindings, Basis Universal texture compression, improved STL interoperability, better Unicode experience for Windows users, a more efficient Emscripten application implementation, single-header libraries, new OpenGL driver workarounds and much more.
A new example showcases capabilities of the DART integration. Let’s dive into robotics, explain what DART is able to do and how it compares to Bullet.
Magnum is developed with a “Zen Garden” philosophy in mind, focusing on productivity, predictability and ease of use. Let’s see how that can extend beyond just the library itself — into your daily workflow.
Similarly to the pointer and reference wrappers described in the last article, Magnum’s array views recently received STL compatibility as well. Let’s take that as an opportunity to compare them with the standard implementation in std::span.
Coming to an established workflow for the first time is always an interesting experience because I don’t have any “it’s been like this since forever” insights forming my opinion yet. This time it’s about quaternion interpolation and a sudden optimization opportunity.