Artificial Intelligence (AI) has been widely adopted in various domains (e.g., computer vision, natural language processing, and reliability analysis). However, its use for performance modeling and evaluation remains limited, and its benefits to the performance engineering field are still unclear. Researchers and practitioners have recently started focusing on methods such as explainable or white-box AI-based solutions in performance engineering, but the tools, methodologies, and datasets that enable wider adoption are still lacking. Meanwhile, the rapid rise of large language models (LLMs) such as ChatGPT poses new challenges in performance optimization and cost containment. LLM pre-training is expensive, and the necessary infrastructure also incurs significant carbon footprint. This workshop aims to bridge research and practice by bringing together academia and industry to share experiences and insights in performance engineering for LLM-based services and AI applications. We target techniques and methodologies to optimize performance while reducing energy consumption and cost.

AIPerfLLM: 3rd International Workshop on Performance Optimization in the LLM world

Incerto Emilio;Masti Daniele;
2025

Abstract

Artificial Intelligence (AI) has been widely adopted in various domains (e.g., computer vision, natural language processing, and reliability analysis). However, its use for performance modeling and evaluation remains limited, and its benefits to the performance engineering field are still unclear. Researchers and practitioners have recently started focusing on methods such as explainable or white-box AI-based solutions in performance engineering, but the tools, methodologies, and datasets that enable wider adoption are still lacking. Meanwhile, the rapid rise of large language models (LLMs) such as ChatGPT poses new challenges in performance optimization and cost containment. LLM pre-training is expensive, and the necessary infrastructure also incurs significant carbon footprint. This workshop aims to bridge research and practice by bringing together academia and industry to share experiences and insights in performance engineering for LLM-based services and AI applications. We target techniques and methodologies to optimize performance while reducing energy consumption and cost.
2025
979-8-4007-1130-5
Performance engineering, AI, Large language models, Optimization
File in questo prodotto:
File Dimensione Formato  
3680256.3721304.pdf

accesso aperto

Descrizione: AIPerfLLM
Tipologia: Versione Editoriale (PDF)
Licenza: Copyright dell'editore
Dimensione 819.46 kB
Formato Adobe PDF
819.46 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/35798
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
social impact