Hello everyone and welcome back to the Cognixia podcast. we are going to talk about a new programming language that is becoming incredibly popular among AI developers, the language is a perfect fit for working with artificial intelligence, it is very simple and user-friendly like Python, and it is as quick as Rust, and has the performance and control like C++. Now, let’s take a second to just appreciate how awesome that is!
This is the Mojo language. Mojo achieves high performance through innovative compiler technologies like integrated caching, multi-threading, cloud distribution, etc. It also includes auto-tuning and meta-programming which enables code optimization for various hardware.
Let us look at some of the key features of the Mojo language:
First, Mojo has a Python-like syntax and dynamic typing ability which makes the Mojo language very easy to learn if you are already familiar with Python. When you are working with modern developments in artificial intelligence and machine learning, then knowing Python is inevitable and with those skills, you can easily work with Mojo too.
Second, you can import and use any of the Python libraries in Mojo, easily. Mojo has complete interoperability with Python, so you won’t have to keep wondering how easy your task would be if only you had access to the matplotlib library, just import it into Mojo and get working!
Third, Mojo follows a very easy unified programming model that is super beginner-friendly and highly scalable for a wide range of use cases based on accelerators. It accomplishes this by combining dynamic and systems language capabilities.
Fourth, the Mojo compiler applies advanced optimizations and GPU/TPU code generation, thereby supporting both Just-in-Time compilation as well as Ahead-of-Time compilation.
Fifth, there are zero cost abstractions, so you can take control of the storage by inline-allocating values into the structures.
Sixth, Mojo offers language-integrated auto-tuning, which enables you to automatically find the best values for your parameters to make the most of your target hardware.
Seventh, you can very easily extend your models with pre- as well as post-processing operations, and you can even replace the operations with custom operations, taking full advantage of kernel fusion, graph rewrites, shape functions, etc.
Finally, the Mojo language gives the user full control over the memory layout, concurrency, and other low-level details.