Awesome GPU Engineering

A curated “awesome” list of resources for mastering GPU engineering for AI systems, covering everything from GPU architecture and kernel programming to large-scale distributed systems and AI acceleration.

Website: https://github.com/goabiaryan/awesome-gpu-engineering

Overview

Awesome GPU Engineering is an open-source, community-maintained directory of learning materials, tools, and references for GPU-focused engineering work. It is aimed at practitioners building and optimizing GPU-based infrastructure for AI and ML workloads.

The list is licensed under CC BY 4.0, allowing sharing and adaptation with attribution.

Features

Structured Resource Categories

• Foundational Books
  Curated books that cover the fundamentals of GPU architecture, parallel programming, and performance engineering.

• GPU Programming Frameworks
  Resources for popular GPU programming ecosystems and languages, such as CUDA, ROCm, and other GPU compute frameworks used in AI systems.

• Optimization and Performance
  Materials focused on:

  • Kernel optimization
  • Memory access patterns
  • Profiling and performance tuning
  • Throughput and latency improvements on GPUs

• Architecture and Low-Level Design
  References on:

  • GPU hardware architecture
  • Instruction-level behavior
  • Memory hierarchies
  • Low-level design decisions impacting AI workloads

• Systems and Multi-GPU Engineering
  Content for building and operating GPU systems at scale, including:

  • Multi-GPU and multi-node setups
  • Distributed training and inference
  • Systems design for GPU clusters and AI infrastructure

• Tutorials and Courses
  Step-by-step tutorials and structured courses to learn GPU programming and GPU systems engineering.

• Research Papers and Articles
  Selected academic and technical articles related to GPU engineering, performance, and AI acceleration.

• Tools and Utilities
  Practical utilities and software used in GPU work, such as:

  • Profilers and debuggers
  • Monitoring and analysis tools
  • Development helpers

  Learning Tools (subsection)
  Tools specifically aimed at supporting learning and experimentation with GPU concepts and code.

• GPU for AI & ML
  Resources focused on how GPUs are used in machine learning and AI, including:

  • Training and inference acceleration
  • Model deployment on GPU infrastructure

• GPU Systems Design Topics for Interview Prep
  Topics and references useful for preparing for roles involving GPU systems design and GPU infrastructure, especially in interviews.

Community & Governance

• Contributors
  Maintained by multiple contributors; open to community additions via pull requests.

• Contribution Guidelines
  Documented process for contributing new resources or improvements (CONTRIBUTING.md).

• Code of Conduct
  Repository includes a code of conduct (CODE_OF_CONDUCT.md) defining expected behavior for contributors.

• Open License
  Content is available under CC BY 4.0, allowing reuse and adaptation with proper attribution.

Category

• Directory Type: Themed directory / awesome list
• Focus Areas: GPU, AI, Infrastructure, Systems Engineering

Pricing

• Free – Open-source repository and resources, available at no cost under the CC BY 4.0 license.