Abstract:

This transcript captures a technical presentation by Chris Lattner and Abdul from Modular, introducing the Mojo programming language. Mojo is designed to tackle the challenges of performance, usability, and portability in GPU software development, particularly for AI. The talk delves into Mojo's Pythonic syntax combined with systems-level performance, its powerful metaprogramming capabilities (parameters, compile-time execution), and its approach to achieving portability across different hardware (Nvidia, AMD, CPUs) without sacrificing direct hardware access (e.g., PTX intrinsics). Key discussion points include tile-based programming, layout abstractions, and how Mojo aims to unify CPU and GPU code development. The speakers emphasize that Mojo is not vaporware, showcasing its recently open-sourced kernel library (nearly half a million lines of code), performance benchmarks against established libraries (like cuBLAS and ROCm), and integration with standard development tools (debuggers, profilers). The presentation contrasts Mojo with existing solutions like CUDA, Triton, and Python DSLs, and concludes with information on its community license, open-sourcing efforts, and calls for community engagement, including an upcoming hackathon.

Mojo: A New Programming Language for High-Performance, Portable GPU Development

• 0:00:21 Introduction: The talk is a deep technical dive into Mojo, covering GPU programming languages, models, metaprogramming, and compilers.
• 0:01:30 Talk Overview: Agenda includes current GPU software, Mojo introduction, metaprogramming, portability, tile-based programming, composing operations, performance, tools, and comparisons.
• 0:02:16 Not Vaporware: All features discussed are downloadable and usable today on CPUs, Nvidia, and AMD GPUs, free for many uses.
• 0:03:01 Major Open Source Release: Modular has open-sourced a massive GPU kernel library (almost 500,000 lines of Mojo code) under an Apache 2 license.
• 0:04:11 The "Many Language Problem" in AI: Python for modeling, C++/Rust for CPU performance, CUDA for GPU performance, leading to rewrites and fragmented teams.
• 0:06:05 Current GPU Kernel Systems Trade-offs:
  • CUDA: High performance, improving usability, not portable.
  • Triton: Good usability & portability, sacrifices performance.
  • OpenCL: Portable, but low performance & usability.
  • Rockm: Good performance on AMD, not focused on usability/portability elsewhere.
  • Mojo aims to deliver on all three: Performance, Usability, Portability.
• 0:08:48 AI Compilers as a Bottleneck: GenAI moves too fast for compiler engineers to keep up with new algorithms and hardware features. Mojo aims to empower kernel engineers.
• 0:10:25 Modular's Goal: A unified, GenAI-native software platform built from first principles for heterogeneous compute (CPUs, Nvidia, AMD initially).
• 0:12:19 What is Mojo? A Pythonic systems programming language. It uses Python syntax but is a new, fast language with its own toolchain (LSP, debugger, etc.).
• 0:17:41 Mojo Performance:
  • CPU: Significantly faster than Python, comparable or faster than Rust in examples, full SIMD/threading support.
  • GPU: Full power of silicon, familiar GPU programming model (thread/block indices), general-purpose (not just AI).
• 0:20:00 Full Hardware Access: Mojo allows direct programming of hardware features like Nvidia TMAs or AMD-specific features, often without compiler changes.
• 0:24:27 Powerful Metaprogramming:
  • Key Idea: Gives kernel engineers compiler-like powers to adapt to new hardware/algorithms in library code.
  • 0:27:04 parameter: Compile-time arguments (e.g., SIMD[DType, width]).
  • 0:28:12 alias: Compile-time variables and expressions.
  • 0:29:18 Unified Language/Meta-language: Use Pythonic constructs (loops, ifs) at compile-time for code generation and specialization.
  • 0:30:10 Example: A list can be filled and its contents stored in the binary at compile time.
  • 0:34:43 Higher-Order Functions: Can be used as compile-time metaprogramming combinators (e.g., a generic vectorize function).
  • 0:37:18 Takeaway: Reduces reliance on compiler engineers for new hardware features or custom optimizations.
• 0:46:30 Unifying CPU & GPU Code (Abdul): The same Mojo function can often be launched on CPU or GPU. Even basic types like Integer are library-defined.
• 0:49:04 Hardware-Specific Intrinsics: Mojo allows direct calls to PTX (Nvidia) or LVM intrinsics (AMD) from library code, abstracted behind common function APIs (e.g., an exp2 function).
• 0:50:52 Guaranteed Unrolling: parameter for provides guaranteed loop unrolling, not just a hint.
• 0:54:43 Portability & Abstraction:
  • for_each (elementwise): Implements different strategies for CPU (chunking, vectorization) and GPU (thread/block mapping, LDS usage) under one API.
  • Low-level CUDA-like programming (thread/block IDs) is also possible.
• 0:58:07 JIT Compilation & Performance:
  • Mojo JIT compiles code for the target hardware, adapting to specifics like warp_size without emulation.
  • Optimized for fast JIT compile times (e.g., Llama 7B full model compilation under 1 min).
  • Zero-cost abstractions: Features like closures, exceptions, traits don't add runtime overhead if not used.
• 1:00:58 Deep Dive: elementwise & thread_index.x: Shows how even fundamental GPU concepts like thread_index.x are library constructs in Mojo, calling different underlying intrinsics based on target hardware.
• 1:04:10 Traits for Extensibility:
  • Example: MHAMask trait allows generic attention kernels to work with various mask types (causal, chunked like in Llama 4) by implementing the trait.
  • This avoids code duplication and complex if-else chains in the main kernel.
• 1:07:40 Mojo Compiler's GPU Agnosticism: Chris Lattner clarifies Mojo compiler itself has no built-in GPU knowledge; all GPU-specifics (launching, intrinsics) are handled via library code and metaprogramming.
• 1:20:13 Tile-Based Programming:
  • For complex operations like MatMul/Attention, inspired by Cutlass/Cute but implemented in Mojo for better usability than C++ templates.
  • Layouts: Abstract mapping of N-D coordinates to linear memory, crucial for performance. Implemented as Mojo library code.
  • Allows composing complex layouts (tiling, swizzling) and visualizing them.
• 1:26:19 Pipelining Strategies: Different hardware (MI300, A100, H100, Blackwell) benefits from different pipelining (double buffering, async copies, warp specialization). Mojo's abstractions allow these to be implemented in a portable way at the library level.
• 1:27:54 Real MatMul Code (MI300 Example): Shows production-quality, abstracted MatMul code in Mojo, utilizing tensor cores. The underlying MMA (Matrix Multiply-Accumulate) operations are parameterized and hardware-agnostic at the call site.
• 1:33:12 Composing Operations & Graph Compilation:
  • Kernels are puzzle pieces. Mojo's graph compiler (part of MAX engine) can fuse operations (e.g., MatMul + SiLU).
  • SiLU example: Defined by its mathematical components; the graph compiler synthesizes the fused kernel.
• 1:37:29 Performance Benchmarks (Work in Progress):
  • MatMul FP16 (H100): Mojo vs. cuBLAS - shows near parity, sometimes beating.
  • MatMul FP16 (MI300): Mojo vs. rocBLAS/hipBLAS LT - competitive, sometimes faster. Mojo MatMul can fuse arbitrary epilogues.
  • Attention (MI300): Mojo vs. Triton.
• 1:41:31 Tooling & Debuggability:
  • Integrates with standard tools: Nsight Compute, rocProf, CUDA GDB, ROC GDB, Compute Sanitizer.
  • Provides source mapping from PTX/assembly back to Mojo code.
  • Cross-compilation support: Can emit assembly for a target (e.g., B200) without local hardware.
  • Full editor support (LSP) with debugging features (breakpoints, log points).
• 1:50:47 Mojo vs. Python DSLs: Mojo offers superior performance, direct hardware control without compiler hacking, generality beyond specific domains, powerful metaprogramming for portable abstractions, and a large open-source kernel library.
• 1:54:30 Mojo's Current Downside: It's new, so the ecosystem and stability are still maturing compared to established systems like CUDA.
• 1:56:40 Open Source Kernel Library: Emphasized again, with full Git history, available on GitHub (modular/max).
• 1:58:24 Distribution: pip install mojo and pip install max are available/coming soon.
• 1:59:06 MAX Inference Engine: Built on Mojo, provides high-performance LLM serving with features like paged attention, continuous batching, speculative decoding. Supports hundreds of LLMs.
• 2:00:32 Modular Community License: Free for non-commercial/research and free for production/commercial use with attribution. Some limitations for non-Nvidia/non-CPU accelerators are being discussed.
• 2:02:03 Current Hardware Focus: Ampere/Hopper (mature), Blackwell/AMD MI300 (functional, performance ongoing). Primarily GenAI/LLM focused. Community help welcome for other areas (HPC, other CPUs like AMX/SVE).
• 2:03:20 Call to Action:
  • Download Max and Mojo.
  • Join the community (Discord).
  • Attend the Hackathon in Hillsboro, CA (chance to win RTX 5090).
  • Modular is hiring.
• 2:05:16 Multi-GPU Support: all_reduce, all_gather primitives are implemented in Mojo, portable across AMD/Nvidia, and can be fused with other operations. These are often faster than NCCL for intra-node communication. FFI allows using NCCL/RCCL if needed.
• 2:09:19 Mojo Compiler Open Sourcing: Committed to open-sourcing the Mojo compiler by the end of next year (2025), possibly sooner. The delay is to ensure stability and a consistent vision before full community involvement in compiler development.