Python is a popular language for productive programming, but its major drawback is its slow speed. This puts it at a disadvantage compared to other languages like Rust and C. However, there’s a new language in town that could change everything – Mojo. A superset of Python, Mojo is not just faster than Python, but up to 35,000 times faster than its predecessor. This is a huge deal, especially since Python is the dominant language for artificial intelligence. In this blog, we’ll take a closer look at Mojo and what makes it stand out.
What is mojo programming language?
Mojo is a concise and modern programming language designed for web development. It emphasizes simplicity, speed, and maintainability, making it well-suited for building web applications, APIs, and dynamic websites. Mojo offers features like routing, templating, and database integration to streamline development. It aims to provide a productive and enjoyable experience for web developers by minimizing boilerplate code and providing powerful tools for common web-related tasks.
Five things you need to know about Mojo:
- It comes from a reputable source: Mojo isn’t just some random side project on GitHub. It comes from a company founded by Chris Lattner, the creator of Swift programming language and the LLVM compiler toolchain. If anyone could fix Python’s speed problem, it’s him.
- It’s designed for AI hardware: Mojo is designed for programming on AI hardware like GPUs, running CUDA and other accelerators. It leverages multilevel intermediate representation to scale to exotic hardware types without adding complexity. It even has built-in auto-tuning to optimize your code for your target hardware.
- It’s a superset of Python Mojo is a superset of Python, which means you don’t need to learn another language to take advantage of it. Unlike other modern systems languages like Rust and Zig, Mojo has a lower learning curve for existing Python programmers. It has features on top of Python like VAR and Let declarations and structs, but the base language is fully compatible with Python. It can also interoperate with the Python ecosystem, allowing you to use your favorite libraries like NumPy and Pandas.
- It adds strong type checking to the language: Mojo adds strong type checking to the language, which is essential for optimized performance and error checking. You can still use dynamic types if you prefer, but static types are recommended for optimal performance. It also supports manual memory management with pointers like C++, and it has an ownership system and borrow checker similar to Rust.
- It’s not available to the public yet: Mojo is still in early development and is not yet available to the public. However, it will be open-sourced in the future. Currently, there’s a waitlist to try it out, but lucky early-access users like us can create a file ending in Mojo or 🔥 emoji to run the code.
Mojo vs Rust vs C vs Python vs Zig
Comparing Mojo, Rust, C, Python, and Zig involves considering their strengths and use cases. Here’s a brief overview:
- Purpose: Web development, particularly for building web applications and APIs.
- Focus: Simplified syntax, speed, and productivity for web developers.
- Notable Features: Routing, templating, database integration.
- Use Case: Ideal for web-focused projects and tasks.
- Purpose: Systems programming with a focus on safety, concurrency, and performance.
- Focus: Memory safety, zero-cost abstractions, and preventing common programming errors.
- Notable Features: Ownership model, lifetimes, concurrency without data races.
- Use Case: Suitable for low-level programming, systems development, and performance-critical applications.
- Purpose: Systems programming, operating systems, embedded systems.
- Focus: Efficiency, portability, and close-to-the-hardware control.
- Notable Features: Minimal abstraction, manual memory management.
- Use Case: Widely used in operating systems, embedded systems, and performance-critical applications.
- Purpose: General-purpose programming, scripting, automation, data analysis, web development.
- Focus: Readability, ease of use, rapid development, and extensive libraries.
- Notable Features: Dynamic typing, high-level abstractions, rich ecosystem.
- Use Case: Versatile language suitable for various domains, including web development, data science, and scripting.
- Purpose: General-purpose programming with a focus on performance, control, and safety.
- Focus: Simplicity, portability, and close-to-the-metal programming.
- Notable Features: Manual memory management, compile-time guarantees, strong type system.
- Use Case: Well-suited for systems programming, low-level development, and performance optimization.
Each language has its strengths and is best suited for specific tasks. Choosing the right language depends on your project’s requirements, your familiarity with the language, and the trade-offs you’re willing to make in terms of performance, safety, and development speed.
So, what makes Mojo so fast? We’ll take a look at a matrix multiplication demo to demonstrate its performance gains. First, we benchmark a basic function in Python. Then by simply importing that code into Mojo, it’s executed 14 times faster with no modifications to the code. We can further optimize this code by adding types to the Python implementation with Mojo’s built-in Struct keyword. We can define mutable and immutable variables using VAR and Let, respectively. We also replace def with FN, which is a stricter type of function. Mojo has a built-in type called “single instruction multiple data” that represents a vector where a single instruction can be executed across multiple elements in parallel on the underlying hardware. Once we have this Struct, we can then use it as a type in the Python implementation. When we benchmark the code again, we get a ridiculous 500x performance boost. But we’re still not happy yet. In the inner loop, we can query the vector width instead of hard-coding it, and that gives us a 1000x gain.
In conclusion, Mojo is a language with a lot of potential to change the programming language landscape. With its unique features like being a superset of Python, designed for AI hardware, strong type checking, and memory management, Mojo could become the go-to language for performance-critical applications. However, only time will tell if Mojo can deliver on its promises and surpass established languages like Python and C.
What do you think of Mojo? Do you believe it can kill Python and C at the same time? Let us know in the comments.