REPOGEO REPORT · LITE

BBuf/how-to-optim-algorithm-in-cuda

Default branch master · commit e3a8d745 · scanned 5/26/2026, 2:27:55 PM

GitHub: 3,025 stars · 277 forks

AI VISIBILITY SCORE

22 /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

1 / 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 BBuf/how-to-optim-algorithm-in-cuda, 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

highreadme#1

Reposition the README's opening to clarify it's a study notebook

Why:

CURRENT

CUDA, GPU kernel, and AI infrastructure optimization notes.

COPY-PASTE FIX

A public study and engineering notebook collecting hands-on CUDA kernels, GPU optimization notes, and AI infrastructure material.

highabout#2

Update the GitHub repository description to reflect its 'notebook' nature

Why:

CURRENT

how to optimize some algorithm in cuda.

COPY-PASTE FIX

A public study notebook and hands-on guide for CUDA, GPU kernel, and LLM inference/training optimization.

highlicense#3
Add a LICENSE file to the repository root
Why:
COPY-PASTE FIX
```
Add a LICENSE file (e.g., MIT or Apache-2.0) to the repository root.
```

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 BBuf/how-to-optim-algorithm-in-cuda

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

cuDNN

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

cuDNN · recommended 2×
NVIDIA CUDA Toolkit · recommended 1×
Nsight Compute · recommended 1×
Nsight Systems · recommended 1×
NVIDIA cuBLAS · recommended 1×

CATEGORY QUERY
How to improve performance of custom GPU kernels for deep learning applications?
you: not recommended
AI recommended (in order):
1. NVIDIA CUDA Toolkit
2. Nsight Compute
3. Nsight Systems
4. NVIDIA cuBLAS
5. cuDNN
6. TVM (Tensor Virtual Machine)
7. OpenAI Triton
8. PyTorch
9. TensorFlow
10. PyTorch JIT (TorchScript)
11. XLA (Accelerated Linear Algebra)
12. ROCm (Radeon Open Compute platform)
13. HIP (Heterogeneous-compute Interface for Portability)
14. rocBLAS
15. MIOpen
AI recommended 15 alternatives but never named BBuf/how-to-optim-algorithm-in-cuda. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking practical guides for optimizing LLM inference and training on modern GPU hardware.
you: not recommended
AI recommended (in order):
1. CUDA Toolkit
2. cuDNN
3. TensorRT
4. NVIDIA DALI (NVIDIA/DALI)
5. NVIDIA NCCL (NVIDIA/nccl)
6. NVIDIA Triton Inference Server (triton-inference-server/server)
7. Hugging Face Transformers (huggingface/transformers)
8. Hugging Face Accelerate (huggingface/accelerate)
9. Hugging Face Optimum (huggingface/optimum)
10. ONNX Runtime (microsoft/onnxruntime)
11. OpenVINO (openvinotoolkit/openvino)
12. PyTorch (pytorch/pytorch)
13. DeepSpeed (microsoft/DeepSpeed)
14. OpenAI Triton (openai/triton)
15. FlashAttention-2 (Dao-AILab/flash-attention)
AI recommended 15 alternatives but never named BBuf/how-to-optim-algorithm-in-cuda. 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 BBuf/how-to-optim-algorithm-in-cuda?
pass
AI named BBuf/how-to-optim-algorithm-in-cuda explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts BBuf/how-to-optim-algorithm-in-cuda in production, what risks or prerequisites should they evaluate first?
pass
AI did not name BBuf/how-to-optim-algorithm-in-cuda — 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?
In one sentence, what problem does the repo BBuf/how-to-optim-algorithm-in-cuda solve, and who is the primary audience?
pass
AI did not name BBuf/how-to-optim-algorithm-in-cuda — 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?

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite