Mojo is a new programming language specifically designed for AI development. It was officially launched in August of 2023 and has already garnered significant attention, boasting over million developers and 50,000 organizations using it. A key aspect of Mojo’s appeal is its
ability to bridge the gap between the ease of use of Python and the high performance of languages like C++ and Rust.
Why Mojo?
One of the main challenges in AI development is the need to use multiple programming languages. Python is often used for simpler parts of neural networks due to its ease of use, but it is relatively slow. To achieve faster speeds, developers often turn to C++, which is more complex and requires a steeper learning curve. Mojo aims to address this challenge by offering a Python-like syntax while delivering significantly faster execution speeds.
Mojo’s Unique Selling Points
- Python-like syntax for easy adoption by Python developers.
- Built on MLIR for high-performance and efficient code generation.
- SIMD ergonomic design for optimized vectorized operations.
- Eager destruction for efficient memory management.
- Rapidly growing community and open-source development.
- Mojo’s combination of ease of use and high performance makes it a strong contender in the AI programming language landscape.
Performance Comparisons
Modular has consistently highlighted Mojo’s performance advantages, particularly in comparison to Python:
- 68,000 times faster than Python by combining advantages of dynamic and static languages.
- 90,000 times faster than Python when running on Mac.
Mojo’s performance stems from its foundation in MLIR, a modern compiler technology that evolved from LLVM. MLIR enables Mojo to generate highly optimized code for CPUs, GPUs, and other accelerators.
While comparisons with Python are prevalent, there is also discussion about Mojo’s performance relative to Rust.
Experts like Luca Palmieri, a Rust contributor, acknowledge Rust’s strengths in systems programming but point out two challenges it faces in AI:
- Slow compilation speed which hinders rapid experimentation.
- The learning curve for Python-familiar AI researchers.
Mojo’s Python-like syntax aims to ease the transition for Python developers while offering comparable performance to Rust. This is exemplified by Mohamed, who quickly learned Mojo and implemented SIMD optimization algorithms in a short time frame.
Advantages of Mojo
Two key advantages of Mojo that contribute to its performance and ease of use are:
- Outstanding SIMD ergonomic design: SIMD (Single Instruction, Multiple Data) allows CPUs to process multiple data elements simultaneously. Mojo is designed with SIMD in mind, making it easier for programmers to leverage its capabilities for significant performance improvements. For example, simple operations like multiplying a SIMD vector can be up to 8 times faster than multiplying individual elements.
- Eager Destruction: Unlike Rust, which releases memory at the end of an object’s scope, Mojo releases it when the object is last used. This is particularly beneficial in AI, where early release of GPU tensors enables larger models to fit in GPU RAM.
Open Source Journey
Initially, Mojo was not open source, which caused some developers to express skepticism and reluctance to invest time in learning it. However, Mojo is now open source, with its core standard library modules available on GitHub. This move towards open source signifies Modular’s commitment to transparency and community involvement in Mojo’s development.
Modular has taken a comprehensive approach to open sourcing Mojo:
- External contributions are welcomed through GitHub pull requests.
- The complete commit history, starting from the initial commit, is available, allowing developers to trace the evolution of the code.
- Nightly builds of the Mojo compiler are released to facilitate testing and integration.
- Plans to open source components of MAX, Modular’s commercial AI platform, are in place.
The choice of the Apache 2 LLVM license with modifications to ensure compatibility with GPL2 code demonstrates a commitment to openness and collaboration.