Explore/agent app/A Fast Locality Simulator for GEMM Design-Space Exploration on Multi-Chiplet GPUs
A

Euijun Chung, Hyesoon Kim/A Fast Locality Simulator for GEMM Design-Space Exploration on Multi-Chiplet GPUsUnknown

Multi-chiplet GPUs split memory into local and remote HBM regions across a silicon interposer, and reducing the remote HBM traffic is crucial for the performance and energy efficiency of multi-chiplet GPUs. For general matrix multiplication (GEMM), the dominant operator in large language models (LLMs), the resulting inter-chiplet traffic depends strongly on kernel choices such as operand layout, CTA traversal order, and data placement, and the optimal strategy to minimize remote accesses is nontrivial. We present a fast, functional, tile-level locality simulator that models CTA scheduling, per-chiplet L2 caches, and local/remote HBM accesses to evaluate a full-size LLM GEMM configuration. Across representative LLM GEMMs, the simulator shows that remote traffic varies by up to 90x across the design space for the same GEMM dimensions. Moreover, using the simulator as feedback, an agentic AI discovers that a 2D block-swizzle CTA traversal reduces remote traffic over the best 1D traversal by up to 5.1x under round-robin placement, identifying CTA traversal order as a first-order, GEMM-dependent design knob for inter-chiplet traffic.

agent app
GitHubCompare
Refreshed 16h ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads—30d
Last commit—
Refreshed16h ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
48 / 100
Composite score from 6 signals. How we score →
Active project
48Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 90 days0 +0.0%
30d90d1y
latest release
Commit activity · 52 weeksActive contributor activity
LowHigh
JunSepDecMarNow
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

48
Score 48/100
Below average

Alternatives

C
crewai
26.1k · Multi-Agent
87
A
autogen
42.7k · Multi-Agent
71
S
smolagents
11.2k · Coding
84
O
openai-agents-python
9.4k · Multi-Agent
81
Compare all →

Recent activity

Latest commit ——
Indexed by AgentHub crawler16h ago
Monitor for new releasesongoing