Ricardo Bianchini is a computer scientist specializing in server and data center power and energy management. He currently serves as a Technical Fellow and Corporate Vice President at Microsoft Azure, where he leads the Azure Compute Capacity and Efficiency group. His work focuses on enhancing the efficiency and sustainability of Microsoft's online services and datacenters.[1]
Bianchini was a Professor of Computer Science at Rutgers University from 2000[5] to 2015. In 2014 he started at Microsoft as its Chief Efficiency Strategist, became a Distinguished Engineer in the Microsoft Research group and then in the Azure group. He is now a Technical Fellow and Corporate VP at Microsoft. Bianchini has served as a program chair, steering committee member, and keynote speaker[6][4][7] for many academic conferences including ASPLOS,[8] Eurosys,[9] and ICDS.[10]
Anant Agarwal, Ricardo Bianchini, David Chaiken, Kirk L Johnson, David Kranz, John Kubiatowicz, Beng-Hong Lim, Kenneth Mackenzie, Donald Yeung "The MIT Alewife Machine: Architecture and Performance" (1995)[11]
Eduardo Pinheiro, Ricardo Bianchini, Enrique V Carrera, Taliver Heath "Load balancing and unbalancing for power and performance in cluster-based systems" (2001)[12]
Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, Ricardo Bianchini "Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms" (2017)[13]
Pratyush Patel, Esha Choukse, Chaojie Zhang, Íñigo Goiri, Brijesh Warrier, Nithish Mahalingam, Ricardo Bianchini "Characterizing Power Management Opportunities for LLMs in the Cloud" (2024)[14]
↑ Patel, Pratyush; Choukse, Esha; Zhang, Chaojie; Goiri, Íñigo; Warrier, Brijesh; Mahalingam, Nithish; Bianchini, Ricardo (2024-04-27). "Characterizing Power Management Opportunities for LLMs in the Cloud". Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3. ASPLOS '24. Vol.3. New York, NY, USA: Association for Computing Machinery. pp.207–222. doi:10.1145/3620666.3651329. ISBN979-8-4007-0386-7.
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