gpu-mode/Triton-Puzzles

• highreadme#1

  Reposition the README's core purpose statement to the introduction

  Why:

  CURRENT

  The README currently introduces Triton generally before stating the puzzle's specific purpose.

  COPY-PASTE FIX

  Copy

  Replace the current introductory paragraphs after the title with this, keeping the Colab link:
  
  ```
  # Triton Puzzles
  
  w/ Tejas Ramesh and Keren Zhou based on Triton-Viz
  
  [](https://colab.research.google.com/github/srush/Triton-Puzzles/blob/main/Triton-Puzzles.ipynb)
  
  This set of interactive puzzles teaches you how to use Triton, an open-source high-level language for GPU programming, from first principles. You will start with trivial examples and build your way up to real algorithms like Flash Attention and Quantized neural networks. These puzzles **do not** need to run on GPU since they use a Triton interpreter.
  
  Programming for accelerators such as GPUs is critical for modern AI systems. This often means programming directly in proprietary low-level languages such as CUDA. Triton is an alternative open-source language that allows you to code at a higher-level and compile to accelerators like GPU. Coding for Triton is very similar to Numpy and PyTorch in both syntax and semantics. However, as a lower-level language, there are a lot of details that you need to keep track of. In particular, one area that learners have trouble with is memory loading and storage which is critical for speed on low-level devices.
  ```

  OpenDoneDismiss
• mediumtopics#2

  Add more specific topics to improve categorization

  Why:

  CURRENT

  machine-learning, puzzle

  COPY-PASTE FIX

  Copy

  machine-learning, puzzle, gpu-programming, triton-lang, interactive-learning, education

  OpenDoneDismiss
• mediumhomepage#3

  Add a homepage URL to the repository's About section

  Why:

  COPY-PASTE FIX

  Copy

  https://colab.research.google.com/github/srush/Triton-Puzzles/blob/main/Triton-Puzzles.ipynb

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  What are the best resources for learning accelerator programming using a high-level language?

  you: not recommended
  AI recommended (in order):

  1. CUDA C/C++ Programming Guide and Samples
  2. OpenCL Specification and Khronos Group Resources
  3. HIP
  4. SYCL Specification
  5. Codeplay ComputeCpp
  6. Intel DPC++
  7. oneAPI
  8. OpenACC Specification
  9. NVIDIA HPC SDK
  10. PGI
  11. Julia
  12. CUDA.jl
  13. AMDGPU.jl
  14. oneAPI.jl
  15. Numba
  16. PyTorch
  17. TensorFlow

  AI recommended 17 alternatives but never named gpu-mode/Triton-Puzzles. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Interactive way to master GPU kernel optimization and memory access patterns?

  you: not recommended
  AI recommended (in order):

  1. NVIDIA Nsight Compute
  2. AMD Radeon GPU Analyzer (RGA)
  3. Intel VTune Profiler
  4. CUDA Occupancy Calculator (NVIDIA)
  5. CodeXL
  6. Intel GPA
  7. RenderDoc
  8. GPUView (Microsoft)

  AI recommended 8 alternatives but never named gpu-mode/Triton-Puzzles. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of gpu-mode/Triton-Puzzles?

  pass
  ⚠AI did not name gpu-mode/Triton-Puzzles — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts gpu-mode/Triton-Puzzles in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named gpu-mode/Triton-Puzzles explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo gpu-mode/Triton-Puzzles solve, and who is the primary audience?

  pass
  ⚠AI did not name gpu-mode/Triton-Puzzles — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?