REPOGEO REPORT · LITE

uccl-project/uccl

Default branch main · commit e00bdfd3 · scanned 5/18/2026, 10:37:27 PM

GitHub: 1,369 stars · 146 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 uccl-project/uccl, 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

Add a clear, concise identity statement at the very top of the README.

Why:

CURRENT

The README currently starts with a centered div and navigation links before the 'About' section.

COPY-PASTE FIX

UCCL is an efficient, high-performance communication library for GPUs, specifically designed for distributed AI/ML and HPC workloads.

mediumreadme#2

Elevate the performance comparison with NCCL/RCCL to a prominent section.

Why:

CURRENT

The performance comparison is currently within a <details> tag under the 'About' section.

COPY-PASTE FIX

## Why UCCL? Outperforming NCCL/RCCL
UCCL-collective (UCCL-Tran) is a drop-in replacement for NVIDIA NCCL and AMD RCCL, designed to deliver significantly higher performance for GPU communication. For example, UCCL-collective outperforms NCCL by up to **2.5x** for AllReduce on six HGX servers (across two racks) with 8x400G CX-7 RoCE NICs and 8xH100 GPUs.

lowtopics#3

Review and expand existing GitHub topics for better specificity.

Why:

CURRENT

ai, allreduce, amd, broadcom, collective, cuda, gpu, hpc, kvcache, llm, moe, networking, nvidia, p2p, rdma

COPY-PASTE FIX

ai, allreduce, amd, broadcom, collective, cuda, distributed-computing, gpu, hpc, kvcache, llm, machine-learning, moe, networking, nvidia, p2p, rdma, deep-learning

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 uccl-project/uccl

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

NVIDIA NCCL

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

NVIDIA NCCL · recommended 2×
open-mpi/ompi · recommended 1×
openucx/ucx · recommended 1×
Intel MPI · recommended 1×
ofiwg/libfabric · recommended 1×

CATEGORY QUERY
How to achieve high-performance, portable GPU communication for distributed AI/ML workloads?
you: not recommended
AI recommended (in order):
1. NVIDIA NCCL
2. Open MPI (open-mpi/ompi)
3. UCX (openucx/ucx)
4. Intel MPI
5. OFI (ofiwg/libfabric)
6. Horovod (horovod/horovod)
7. PyTorch Distributed (pytorch/pytorch)
8. Gloo (facebookincubator/gloo)
9. TensorFlow Distributed (tensorflow/tensorflow)
AI recommended 9 alternatives but never named uccl-project/uccl. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Are there drop-in replacements for standard GPU collective communication with better performance?
you: not recommended
AI recommended (in order):
1. NVIDIA NCCL
2. Open MPI
3. Intel MPI Library
4. HPC-X
5. UCX
6. AMD RCCL
AI recommended 6 alternatives but never named uccl-project/uccl. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 uccl-project/uccl?
pass
AI named uccl-project/uccl explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts uccl-project/uccl in production, what risks or prerequisites should they evaluate first?
pass
AI named uccl-project/uccl 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 uccl-project/uccl solve, and who is the primary audience?
pass
AI named uccl-project/uccl 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)

[![RepoGEO](https://repogeo.com/badge/uccl-project/uccl.svg)](https://repogeo.com/en/r/uccl-project/uccl)

HTML

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

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uccl-project/uccl — 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