One Line
Mojo is a language by Modular that enables high performance and compatibility for Python libraries without the need for C or C++.
Slides
Slide Presentation (6 slides)
Key Points
- Mojo is a new programming language designed to address the challenges in AI compute and target accelerators and heterogeneous systems.
- Mojo is a member of the Python family and aims for full compatibility with the Python ecosystem.
- Mojo is designed to provide a superset of Python and embrace the CPython implementation for long-tail ecosystem support.
- Mojo aims to solve the two-world problem in Python, where Python is not suitable for systems programming.
- Mojo is not just an embedded DSL or subset of Python, but a standalone language with its own compilation and runtime system.
Summaries
25 word summary
Mojo, a language by Modular, is designed for accelerators. It eliminates the need for C or C++ in Python libraries, providing high performance and compatibility.
67 word summary
Mojo, a programming language developed by Modular, is designed for accelerators and heterogeneous systems. It introduces intentional differences to be a first-class language but uses CPython for interoperability. Mojo aims to eliminate the need for C or C++ within Python libraries, providing high performance and control over computations. It strives to be a unified language for heterogeneous compute, simplifying development and maintaining compatibility with existing Python code.
131 word summary
Mojo, a programming language developed by Modular, tackles the obstacles in AI compute. It is designed for accelerators and heterogeneous systems, offering compile-time metaprogramming, adaptive compilation techniques, and caching. Mojo is the first major language specifically built for MLIR, an open-source compiler infrastructure supporting various accelerators. It is a member of the Python family, aiming for compatibility with the Python ecosystem and leveraging its popularity and existing packages. While Mojo introduces intentional differences to be a first-class language, it uses CPython for interoperability and provides a migration tool for code from Python. Mojo strives to eliminate the need for C or C++ within Python libraries, providing high performance and control over computations. It aims to be a unified language for heterogeneous compute, simplifying development and maintaining compatibility with existing Python code.
400 word summary
Modular Docs - Why Mojo Mojo is a programming language created by Modular to address the challenges in AI compute. It was developed to target accelerators and heterogeneous systems, with features such as powerful compile-time metaprogramming, adaptive compilation techniques, and caching. Mojo recognizes the importance of the host CPU in AI systems and aims to provide a language that can work with both specialized accelerators and CPUs.
Mojo is designed to work with MLIR, an open-source compiler infrastructure that supports a wide range of accelerators. While other projects also use MLIR, Mojo is the first major language specifically designed for MLIR, making it uniquely powerful for AI workloads.
Mojo is a member of the Python family and aims for full compatibility with the Python ecosystem. It leverages the Python ecosystem's popularity, community, and existing packages, while providing new tools for safe and performant systems-level code. Mojo aims to be a superset of Python and embraces the CPython implementation for long-tail ecosystem support.
While Mojo strives for compatibility with Python, it also introduces intentional differences to be a first-class language. Mojo uses CPython for interoperability and provides a mechanical migration tool for those who want to migrate code from Python to Mojo. This approach allows Mojo to integrate well with the existing Python ecosystem while providing incremental migration benefits.
Python has some well-known limitations, such as poor low-level performance and the global interpreter lock (GIL). While efforts are being made to improve Python's performance and replace the GIL, these approaches do not fully satisfy the needs of Mojo. Mojo aims to eliminate the need for using C or C++ within Python libraries, provide high performance, and offer predictability and control over computations.
Other attempts to improve Python, such as building subsets of Python or embedded domain-specific languages (DSLs), have limitations in terms of interoperability, tooling support, and compatibility with Python's dynamic use cases. Mojo aims to provide a unified language for heterogeneous compute, simplify the development process, and offer better usability and predictability.
In conclusion, Mojo is a programming language specifically designed for AI compute. It addresses the challenges of working with accelerators and heterogeneous systems, while aiming for compatibility with the Python ecosystem. Mojo offers powerful compile-time metaprogramming, integration with MLIR, and a superset of Python features. It strives to provide high performance, predictability, and control over computations, while simplifying the development process and maintaining compatibility with existing Python code.