Abstract
The chapter discusses the need for efficient energy consumption in high-performance computing systems and proposes the integration of artificial intelligence and machine learning techniques to optimize energy efficiency. It explores AI-driven techniques like reinforcement learning, neural networks, and predictive analytics for energy-aware scheduling, workload allocation, and adaptive power management. The chapter discusses the effectiveness of AI-driven energy optimization strategies in real-world HPC infrastructures, highlighting potential energy savings while maintaining computational performance. It also discusses future directions and challenges in AI-enabled smart energy management, including algorithm refinement, integration with emerging technologies, and scalability considerations. The holistic approach highlights the transformative impact of AI and ML in creating sustainable, energy-efficient paradigms within high-performance computing ecosystems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
