In this article, I will discuss how, Mojo programming language may overcome the Python programming limitations. It will also discuss advantages and features of Mojo language.
Mojo— a new programming language for all AI developers.
Mojo bridges the gap between research and production by combining the best of Python syntax with systems programming and metaprogramming. Mojo combines the usability of Python with the performance of C, seamlessly inter-op with the Python ecosystem, unlocking unparalleled programmability of AI hardware and extensibility of AI models.
Mojo Features
Usability and programmability: Write everything in one language. Write Python or scale all the way down to the metal. Program the multitude of low-level AI hardware. No C++ or CUDA required.
Performance: Utilize the full power of the hardware, including multiple cores, vector units, and exotic accelerator units, with the world’s most advanced compiler and heterogeneous runtime. Achieve performance on par with C++ and CUDA without the complexity.
Python: Python supports single threaded execution.
Mojo: Mojo supports multiple threaded execution (Parallel processing across multiple cores).
Moja is 35000 times faster than Python, and it is also faster than C/ C++ and Rust.
Interoperability: As Mojo is superset of Python it can access the entire Python ecosystem. The programmer can experience true interoperability with the Python ecosystem. Seamlessly intermix arbitrary libraries like Numpy and Matplotlib and your custom code with Mojo.
Extensibility: Upgrade your models and the Modular stack
Easily extend your models with pre and post-processing operations, or replace operations with custom ones. Take advantage of kernel fusion, graph rewrites, shape functions, and more.
Progressive Types: Leverage types for better performance and error checking.
Zero cost abstractions: Take control of storage by inline-allocating values into structures.
Ownership and borrow checker: Take advantage of memory safety without the rough edges.
Portable parametric algorithms: Leverage compile-time meta-programming to write hardware-agnostic algorithms and reduce boilerplate.
Language integrated auto-tuning: Automatically find the best values for your parameters to take advantage of target hardware.
The full power of MLIR
Parallel heterogenous runtime
Paralization: Mojo leverages MLIR, which enables Mojo developers to take advantage of vectors, threads, and AI hardware units.
Fast compile times
Note: MLIR (Multi-Level IR) is a compiler intermediate representation with similarities to traditional three-address SSA representations (like LLVM IR or SIL), but which introduces notions from polyhedral loop optimization as first-class concepts. The MLIR project is a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together.
Why Mojo?
Mojo an innovative and scalable programming model that could target accelerators and other heterogeneous systems that are pervasive in the AI field. This meant a programming language with powerful compile-time metaprogramming, integration of adaptive compilation techniques, caching throughout the compilation flow, and other features that are not supported by existing languages.
Conclusions:
As we know Python is the best programming language for developing AI, ML and DL based applications. Due to performance, slow execution and single threaded issues there is a need for new programming language which improves the performance and faster execution of AI/ ML based applications. The Mojo overcomes the limitations of Python and improves the performance of AI/ML applications.
