Hello! My name is Matt Suiche. I am an independent researcher, advisor, and investor. I previously served as the Head of Detection Engineering at Magnet Forensics. Our organization was passionately dedicated to justice and protecting the innocent, a mission we embarked on more intensely after the 2022 acquisition of my cybersecurity start-up, Comae Technologies.
My life-long fascination with learning and understanding complex systems first led me to cybersecurity. My teenage years were spent immersed in reverse engineering, which ignited a profound curiosity about technology that continues to this day. I’ve since explored various fields including operating systems architecture, programming languages, virtualization, modern web application development, and generative art. Furthermore, I’ve delved into numerous domains such as privacy, surveillance, forensics, blockchain, and community development among others.
Introduction 🔗This document analyzes AMD GPU support implementation in Triton’s Gluon framework, examining architecture-specific optimizations, performance characteristics, and implementation details relative to NVIDIA GPU support.
For background on Gluon and its motivation as a lower-level alternative to Triton, see my previous post: “Gluon: When Triton Isn’t Low-Level Enough”.
Background: GPU Programming Architecture Landscape đź”—The GPU programming ecosystem has evolved with distinct architectural approaches between NVIDIA and AMD, creating implementation challenges for cross-platform frameworks.
Introduction 🔗Byte Pair Encoding (BPE) tokenization is used in modern language models, but efficient training implementations are limited. OpenAI’s tiktoken handles inference well, while HuggingFace’s tokenizers supports training but has complexity and overhead. RustBPE is a Rust implementation that provides training capabilities with better performance.
RustBPE was developed by Andrej Karpathy as part of the nanochat project. This analysis covers the RustBPE implementation, including its architecture, performance characteristics, and Python integration.
Background đź”—I recently encountered the GPU MODE TriMul challenge while exploring GPU optimization. Coming from a systems engineering background without prior PyTorch or Triton experience, this challenge provided an opportunity to learn GPU performance engineering through a practical problem.
The Triangle Multiplicative Update (TriMul) is a core operation in AlphaFold2 and AlphaFold3—the protein structure prediction systems that earned the 2024 Nobel Prize in Chemistry. The operation’s O(n³) complexity creates severe performance bottlenecks in production, forcing AlphaFold3 to use batch size 1 during training despite having under 1B parameters.
