Introduction to Mojo Programming: Combining C++ Speed with Python Accessibility

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A top-choice for AI development, Mojo is a new programming language that blends Python’s usability with high-performance found in languages like C++ and Rust. It empowers developers with exceptional programmability of AI hardware and extensibility of AI models.

Mojo is designed to gradually become a superset of Python, just like TypeScript is a superset of JavaScript. By offering a simple and concise syntax that enables a neural network to be implemented with much less effort than other languages, Mojo is actually being marketed as the faster alternative to Python.

Demonstrating a promising future potential for the world of computer programming, in August of 2023, Mojo’s parent company Modular, managed to raise $100 million in fresh funding, supported by institutional backers such as Alphabet Inc’s GV start-up fund.

In this article, Bocasay, our offshore IT agency based in Vietnam, provides an overview of all you need to know about Mojo programming.

Discover Mojo Programming for AI development
Discover Mojo Programming for AI development

Mojo Origins

The Mojo programming language is the brainchild of the Modular. The company was founded in 2022 by Chris Lattner, the original architect of the Swift programming language, and Tim Davis, a former Google employee. Mojo’s initial design was released internally by Modular in September of 2022.

Designed to make AI programming more accessible to a wider range of developers, Mojo programming is well-suited for both beginners and seasoned software engineers, as it provides a platform that ultimately incorporates Python’s ease-of-use with C’s high-speed and performance.

Key Characteristics of Mojo Programming 

Bridging the gap between accessibility and performance, Mojo was designed to cater to evolving development needs in high-performance systems programming and artificial intelligence. Here are some of Mojos’ essential characteristics:

⌲ Easy Python Transition: Mojo was very much designed for easy transition from Python. Its syntax is similar to Python’s and it is very easy to import any Python module into a Mojo program, as well as to create Python types from Mojo types. It is important to note that Mojo does not have a standard dictionary at this point, which means that it is not yet possible to create a Python dictionary from a Mojo dictionary. Nonetheless, it is still possible to work with Python dictionaries within Mojo.

⌲ Impressive Speed Performance: Enabling efficient programming on AI hardware, according to Modular, Mojo is 35000 times faster than Python. Mojo’s impressive high-speed performance is achieved through the use of Multi-Level Intermediate Representation (MLIR), scaling hardware types seamlessly without introducing complexity. In contrast to Python which relies on runtime interpretation, Mojo is translated into machine code through the use of a Low-Level Virtual Machine (LLVM), basically optimizing performance through the use of Mojo-specific features.  

⌲ Inferred Static Typing: Mojo uses inferred static typing, also known as type inference. This is a feature found in certain programming languages where the type of a variable is deduced, or automatically determined by the compiler or interpreter at compile-time, or during the static analysis phase. In languages with static typing, the type of a variable is typically declared explicitly by the programmer, but in languages with type inference, the compiler or interpreter can analyze the code and infer the types without the need for explicit type declarations in every instance.

⌲ LLVM & MLIR Backend Compilation: LLVM (Low Level Virtual Machine) and MLIR (Multi-Level Intermediate Representation) are both compiler infrastructure projects used by Mojo, and they play significant roles in the backend compilation process of various programming languages. While LLVM is a more established and broader project, MLIR was designed with a focus on addressing specific challenges in compiler technology. LLVM focuses on providing a low-level intermediate representation and optimizations for generating efficient machine code, while MLIR extends this approach with a multi-level representation. LLVM and MLIR are both integral parts of modern compiler infrastructure.

⌲ Not Open-Source: Mojo is not yet an open-source language. According to Modular, core parts of Mojo, such as the standard library, are expected to become open-source over time. Being a young programming language, Modular doesn’t provide an established plan for open-sourcing, and has stated its intention to continue to incubate it within the company until more of Mojo’s internal architecture becomes more established. Ultimately, at this point, Modular believes that a tight-knit group of engineers sharing a common vision can move faster than a community development effort.  

⌲ Borrow Checker: Influenced by the Rust language, a borrow checker feature is expected to be added to Mojo in the next couple of months. A borrow checker is a component of a programming language’s compiler or runtime system that enforces rules related to borrowing and ownership in the context of memory management. It plays a crucial role in enabling memory safety without the need for garbage collection. The concept of borrowing and ownership is a key feature of languages that use ownership-based type systems, such as Rust. 

Mojo’s Future in Emerging Technologies

While Modular currently uses Mojo to develop AI algorithms, it is important to remember that Mojo is a general purpose programming language. As the language gradually evolves into a superset of Python, Mojo can eventually be used within a wide range of general programming tasks such as data transformations, HPC and for writing pre and post processing operations.

Due to its architecture and evolving features, Mojo is well-placed to become a key player in emerging technologies such as artificial intelligence, machine learning and the Internet of Things. These cutting-edge technologies require programming languages able to efficiently manage large amounts of data, provide high-speed performance and easy integration with legacy systems. 

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