REPOGEO REPORT · LITE

baidu-research/DeepBench

Default branch master · commit da81ba78 · scanned 5/23/2026, 2:47:49 PM

GitHub: 1,105 stars · 241 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 baidu-research/DeepBench, 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

2 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 focus on fundamental operations

Why:

CURRENT

The primary purpose of DeepBench is to benchmark operations that are important to deep learning on different hardware platforms. Although the fundamental computations behind deep learning are well understood, the way they are used in practice can be surprisingly diverse.

COPY-PASTE FIX

DeepBench is a benchmarking suite specifically designed to measure the performance of **individual, fundamental deep learning operations** (such as matrix multiplications, convolutions, and recurrent layers) across various hardware platforms. Unlike end-to-end model benchmarks, DeepBench focuses on these low-level primitives to provide insights for hardware architects and deep learning system developers.

mediumhomepage#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://github.com/baidu-research/DeepBench (or a dedicated project page if one exists)
```

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 baidu-research/DeepBench

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

tensorflow/benchmarks

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

tensorflow/benchmarks · recommended 1×
torch/benchmarks · recommended 1×
MLPerf Inference/Training Benchmarks · recommended 1×
Deep Learning Performance Toolkit · recommended 1×
NVIDIA Deep Learning Performance Guide · recommended 1×

CATEGORY QUERY
How can I benchmark deep learning operations across different hardware platforms?
you: not recommended
AI recommended (in order):
1. TensorFlow Benchmarks (tensorflow/benchmarks)
2. PyTorch Benchmarks (torch/benchmarks)
3. MLPerf Inference/Training Benchmarks
4. Deep Learning Performance Toolkit
5. NVIDIA Deep Learning Performance Guide
6. NVIDIA DLProf
7. ONNX Runtime Benchmarking Tool
8. Google Cloud AI Platform Benchmarking
9. AWS SageMaker Benchmarking
AI recommended 9 alternatives but never named baidu-research/DeepBench. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help compare neural network computation performance on various hardware accelerators?
you: not recommended
AI recommended (in order):
1. NVIDIA Nsight Systems
2. NVIDIA Nsight Compute
3. TensorFlow Profiler
4. TensorBoard Profiler
5. PyTorch Profiler
6. Intel VTune Profiler
7. ONNX Runtime Profiler
8. DeepSpeed (microsoft/DeepSpeed)
9. Megatron-LM (NVIDIA/Megatron-LM)
AI recommended 9 alternatives but never named baidu-research/DeepBench. 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 baidu-research/DeepBench?
pass
AI named baidu-research/DeepBench explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts baidu-research/DeepBench in production, what risks or prerequisites should they evaluate first?
pass
AI named baidu-research/DeepBench 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 baidu-research/DeepBench solve, and who is the primary audience?
pass
AI named baidu-research/DeepBench explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/baidu-research/DeepBench"><img src="https://repogeo.com/badge/baidu-research/DeepBench.svg" alt="RepoGEO" /></a>

Pro

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baidu-research/DeepBench — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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