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Paper

LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization

by Independent / Community 001d2852a5c4976c6ae5cfc2dbe29264f2e0fdfa

Free2AITools Nexus Index

62.3

S: Semantic 50

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A: Authority 68

P: Popularity 43

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

The advancement of machine learning for compiler optimization, particularly within the polyhedral model, is constrained by the scarcity of large-scale, public performance datasets. This data bottleneck forces researchers to undertake costly data generation campaigns, slowing down innovation and hindering reproducible research learned code optimization. To address this gap, we introduce LOOPerSet, a new public dataset containing 28 million labeled data points derived from 220,000 unique, synth...

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Semantic Scholar 3 Citations

Paper Information Summary
Entity Passport
Registry ID	001d2852a5c4976c6ae5cfc2dbe29264f2e0fdfa
License	ArXiv
Provider	semantic_scholar

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Academic & Research Attribution

BibTeX

@misc{001d2852a5c4976c6ae5cfc2dbe29264f2e0fdfa,
  author = {Unknown},
  title = {LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/001d2852a5c4976c6ae5cfc2dbe29264f2e0fdfa}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization [Paper]. Free2AITools. https://api.semanticscholar.org/001d2852a5c4976c6ae5cfc2dbe29264f2e0fdfa

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 68

Popularity (P) 43

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization: Authority (A:68), Popularity (P:43), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"The advancement of machine learning for compiler optimization, particularly within the polyhedral model, is constrained by the scarcity of large-scale, public performance datasets. This data bottleneck forces researchers to undertake costly data generation campaigns, slowing down innovation and hindering reproducible research learned code optimization. To address this gap, we introduce LOOPerSet, a new public dataset containing 28 million labeled data points derived from 220,000 unique, synth..."

❝ Cite Node

@article{Unknown2026LOOPerSet:,
  title={LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈3CitationsSemantic Scholar

🏛️68AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

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params billions: null
context length: null
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citations: 3

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