Software Unscripted GPU Programming and Language Design with Chris Lattner
50 snips
Jul 25, 2025 
Chris Lattner, the mastermind behind Swift and LLVM, now leads Mojo, a groundbreaking language aimed at maximizing hardware potential. He dives into GPU programming, discussing Mojo's advantages over traditional languages like Rust and CUDA. Lattner highlights how Mojo simplifies high-performance computing and bridges the gap between systems programming and Python. He also reflects on the evolving role of AI in compiler development, emphasizing the importance of collaboration between human programmers and AI tools while exploring the need for robust software solutions in an ever-changing hardware landscape.
AI Snips
Chapters
Transcript
Episode notes
Mojo Hackathon GPU Success
- A Mojo hackathon enabled 100 people to write fast GPU kernels on AMD GPUs within a day.
- Community projects include games, ray tracers, and AI, showing rapid adoption and diversity.
Extend Python with Mojo for Speed
- Move slow Python code into Mojo using .mojo files for seamless integration and speed.
- Leverage Mojo features like SIMD, threads, and GPU for performance without leaving the Python ecosystem.
Mojo's Split Compilation Model
- Mojo enables writing functions callable on both CPU and GPU with shared and explicit differences.
- This 'split compilation' technique balances code reuse and control for heterogeneous hardware.