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One of the big differences here is that the Mojo/Python interop package is doing a lot of the heavy lifting of compiling and loading the code. The goal here is to have a language that's close to Python that gives you hardware acceleration as you need it, without having to configure and build C++ code for every platform.

We've run a few events this year (disclosing again that I work for Modular, to put my comments into context), and we've had some great feedback from people who have never done GPU programming about how easy it was to get started using Mojo.

C++ is a powerful and mature language, but it also has a steep learning curve. There's a lot of space for making GPU (and other high performance computing) easier, and platforms/languages like Triton and Julia are also exploring the space alongside Mojo. There's a huge opportunity to make GPU programming easier, and a bit part of that opportunity is in abstracting away as much of the device-specific coding as you can.

I was a HPC C++ programmer for a long time, and I always found recompiling for new devices to be one of the most painful things about working in C++ (for example, the often necessary nightmare of cmake). Mojo offers a lot of affordances that improve on the programming experience at the language, compiler, and runtime levels.



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