RRepoGEO

REPOGEO REPORT · LITE

openai/blocksparse

Default branch master · commit 89074c5c · scanned 5/28/2026, 3:43:20 AM

GitHub: 1,066 stars · 197 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface openai/blocksparse, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    tensorflow, gpu, sparse-matrix, deep-learning, machine-learning, cuda, optimization, block-sparse, convolution
  • highreadme#2
    Strengthen README's opening sentence to highlight core value and audience

    Why:

    CURRENT
    The `blocksparse` package contains TensorFlow Ops and corresponding GPU kernels for block-sparse matrix multiplication. Also included are related ops like edge bias, sparse weight norm and layer norm.
    COPY-PASTE FIX
    The `blocksparse` package provides highly optimized GPU kernels and TensorFlow Ops for efficient block-sparse matrix multiplication and convolution, specifically designed to accelerate large deep learning models on Nvidia GPUs.
  • mediumreadme#3
    Add a brief comparison or differentiator statement in the README

    Why:

    COPY-PASTE FIX
    Unlike general-purpose sparse matrix libraries, `blocksparse` focuses specifically on highly optimized GPU kernels for *block-sparse* operations, offering significant performance advantages for deep learning models with structured sparsity.

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

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface openai/blocksparse
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA cuSPARSE
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA cuSPARSE · recommended 1×
  2. PyTorch · recommended 1×
  3. TensorFlow · recommended 1×
  4. DeepSpeed · recommended 1×
  5. CUTLASS · recommended 1×
  • CATEGORY QUERY
    How to accelerate sparse matrix multiplication operations on Nvidia GPUs for deep learning?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA cuSPARSE
    2. PyTorch
    3. TensorFlow
    4. DeepSpeed
    5. CUTLASS
    6. MinkowskiEngine

    AI recommended 6 alternatives but never named openai/blocksparse. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a TensorFlow library to optimize block-sparse convolutions and matrix operations on GPU.
    you: not recommended
    AI recommended (in order):
    1. DeepMind's Block-Sparse Library (BSL) (deepmind/bsl)
    2. TensorFlow (tensorflow/tensorflow)
    3. NVIDIA's cuSPARSE
    4. TensorFlow Addons (TFA) (tensorflow/addons)
    5. Custom CUDA Kernels

    AI recommended 5 alternatives but never named openai/blocksparse. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of openai/blocksparse?
    pass
    AI named openai/blocksparse explicitly

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

  • If a team adopts openai/blocksparse in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/blocksparse 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 openai/blocksparse solve, and who is the primary audience?
    pass
    AI named openai/blocksparse explicitly

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

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