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Achievements

Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: YOLOAchievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Achievements

Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: YOLOAchievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

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Organizations

@owensgroup @gunrock

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maawad/README.md

👋 Hi there — I'm Muhammad Awad

I'm a Senior Member of Technical Staff at AMD Research, where I conceive, architect, and tech-lead system-level software, libraries, and runtimes for next-generation computing. I lead cross-organizational teams and coordinate initiatives across ML frameworks, kernel engineering, distributed systems, and research teams.

Before joining AMD, I completed my Ph.D. in Electrical and Computer Engineering at UC Davis, advised by John Owens. My research there focused on building dynamic concurrent data structures on GPUs, such as B-trees, dynamic graphs, multiversioned trees, and high-performance hash tables.

💼 Current Focus @ AMD

At AMD, I lead and contribute to projects spanning:

  • AI-powered tools for GPU performance and productivity
  • Libraries and runtimes for heterogeneous and distributed systems
  • Programming models for AMD GPUs and Ryzen™ AI NPUs

Notable projects:

  • 🔁 Iris: Conceived, architected, and tech-led a Triton-based multi-GPU programming framework from scratch. Leading a cross-organizational team, demonstrated significant speedups in production LLM workloads.
  • 🧠 IntelliKit: Conceived and architected an open-source LLM-ready profiling toolkit for AMD CPUs and GPUs. Serving as tooling lead for AMD's company-wide ML for performance engineering initiative.
  • 🧠 IntelliPerf: Conceived and led development of an LLM-powered autonomous GPU performance engineering framework that profiles, diagnoses, and optimizes kernels end-to-end.
  • ⚙️ IRON: Contributor to IRON, a low-level development stack for AMD Ryzen™ AI NPUs with Python APIs and MLIR compiler passes.

🔬 Academic Research

I'm broadly interested in parallel computing, concurrent data structures, performance analysis, and low-level GPU programming. As a Ph.D. student, I designed and built several GPU-native data structures:

📫 Connect

Pinned Loading

  1. ROCm/iris ROCm/iris Public

    AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

    Python 171 33

  2. owensgroup/BGHT owensgroup/BGHT Public

    BGHT: High-performance static GPU hash tables.

    C++ 71 8

  3. owensgroup/MVGpuBTree owensgroup/MVGpuBTree Public

    GPU B-Tree with support for versioning (snapshots).

    C++ 51 5

  4. gunrock/gunrock gunrock/gunrock Public

    Programmable CUDA/C++ GPU Graph Analytics

    C++ 1.1k 218

  5. owensgroup/GpuBTree owensgroup/GpuBTree Public

    Code for paper "Engineering a High-Performance GPU B-Tree" accepted to PPoPP 2019

    Cuda 58 13

  6. PTX_BCHT PTX_BCHT Public

    Bucketed Cuckoo hash set written in PTX and JIT-compiled.

    C++ 1 1