# EPIC: Generative AI Platform for Accelerating HPC Operational Data Analytics

arXiv: https://arxiv.org/abs/2509.16212 · HTML: https://mlsanity.com/paper/2509.16212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies](https://mlsanity.com/paper/2308.07610/) (2023)
- [LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection](https://mlsanity.com/paper/2309.01189/) (2023)
- [Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation](https://mlsanity.com/paper/2309.07103/) (2023)
- [Scope is all you need: Transforming LLMs for HPC Code](https://mlsanity.com/paper/2308.09440/) (2023)
- [MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks](https://mlsanity.com/paper/2312.13322/) (2023)
- [LLM as HPC Expert: Extending RAG Architecture for HPC Data](https://mlsanity.com/paper/2501.14733/) (2024)
- [DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems](https://mlsanity.com/paper/1910.06156/) (2019)
- [Operational Data Analytics in Practice: Experiences from Design to Deployment in Production HPC Environments](https://mlsanity.com/paper/2106.14423/) (2021)
- [From Facility to Application Sensor Data: Modular, Continuous and Holistic Monitoring with DCDB](https://mlsanity.com/paper/1906.07509/) (2019)

## Abstract (arXiv)

We present EPIC, an AI-driven platform designed to augment operational data analytics. EPIC employs a hierarchical multi-agent architecture where a top-level large language model provides query processing, reasoning and synthesis capabilities. These capabilities orchestrate three specialized low-level agents for information retrieval, descriptive analytics, and predictive analytics. This architecture enables EPIC to perform HPC operational analytics on multi-modal data, including text, images, and tabular formats, dynamically and iteratively. EPIC addresses the limitations of existing HPC operational analytics approaches, which rely on static methods that struggle to adapt to evolving analytics tasks and stakeholder demands. Through extensive evaluations on the Frontier HPC system, we demonstrate that EPIC effectively handles complex queries. Using descriptive analytics as a use case, fine-tuned smaller models outperform large state-of-the-art foundation models, achieving up to 26% higher accuracy. Additionally, we achieved 19x savings in LLM operational costs compared to proprietary solutions by employing a hybrid approach that combines large foundational models with fine-tuned local open-weight models.