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Parthasarathy Ranganathan

Parthasarathy Ranganathan

D-Index & Metrics

Computer Science

D-Index
77
Citations
23414
World Ranking
1277
National Ranking
675

Research.com Recognitions

  • 2014 - ACM Fellow For contributions to the areas of energy efficiency and server architectures.
  • 2011 - ACM Distinguished Member
  • 2006 - ACM Senior Member

Overview

Parthasarathy Ranganathan is affiliated with Google in the United States and has a multidisciplinary research portfolio spanning computer science and medicine. Their work integrates themes from advanced computing systems to health and biological studies.

Their research primarily focuses on computer science with significant contributions to subfields such as computer networks and communications, information systems, electrical and electronic engineering, and industrial and manufacturing engineering. Moreover, their work extends into medicine, addressing pathology and forensic medicine.

Key topics in their research include cloud computing and resource management, parallel computing and optimization techniques, alcohol consumption and health effects, liver disease diagnosis and treatment, diet and metabolism studies, and advanced manufacturing and logistics optimization. Scheduling and optimization algorithms also form a recurrent theme.

Frequent co-authors associated with Parthasarathy Ranganathan are:

  • Vatsalya Vatsalya
  • Melanie L. Schwandt
  • Milad Hashemi
  • Amor J. Royer
  • Maiying Kong

The scientist has published extensively in several notable venues. These include:

  • arXiv (Cornell University)
  • IEEE Micro
  • Communications of the ACM
  • International Journal of Molecular Sciences
  • Preprints.org

Recent publications reflect a combination of computing and medical science research:

  • Learning Performance-Improving Code Edits, 2023, arXiv (Cornell University)
  • AsmDB: Understanding and Mitigating Front-End Stalls in Warehouse-Scale Computers, 2020, IEEE Micro
  • Association of Hypomagnesemia and Liver Injury, Role of Gut-Barrier Dysfunction and Inflammation: Efficacy of Abstinence, and 2-Week Medical Management in Alcohol Use Disorder Patients, 2022, International Journal of Molecular Sciences
  • The Importance of Generalizability in Machine Learning for Systems, 2024, IEEE Computer Architecture Letters
  • Illustration of Gut-Thyroid Axis in Alcohol Use Disorder: Interplay of Gut Dysfunction, Pro-Inflammatory Responses, and Thyroid Function, 2022, Cells

Parthasarathy Ranganathan has received recognition within the computing community through awards that include:

  • ACM Fellow, 2014, for contributions to the areas of energy efficiency and server architectures
  • ACM Distinguished Member, 2011
  • ACM Senior Member, 2006

Best Publications

  • Single-ISA heterogeneous multi-core architectures: the potential for processor power reduction

    Rakesh Kumar;Keith I. Farkas;Norman P. Jouppi;Parthasarathy Ranganathan

  • Making scheduling cool: temperature-aware workload placement in data centers

    Justin Moore;Jeff Chase;Parthasarathy Ranganathan;Ratnesh Sharma

  • No "power" struggles: coordinated multi-level power management for the data center

    Ramya Raghavendra;Parthasarathy Ranganathan;Vanish Talwar;Zhikui Wang

  • Single-ISA Heterogeneous Multi-Core Architectures for Multithreaded Workload Performance

    Rakesh Kumar;Dean M. Tullsen;Parthasarathy Ranganathan;Norman P. Jouppi

  • Heracles: improving resource efficiency at scale

    David Lo;Liqun Cheng;Rama Govindaraju;Parthasarathy Ranganathan

  • Profiling a Warehouse-Scale Computer

    Svilen Kanev;Juan Pablo Darago;Kim Hazelwood;Parthasarathy Ranganathan

  • A Power Benchmarking Framework for Network Devices

    Priya Mahadevan;Puneet Sharma;Sujata Banerjee;Parthasarathy Ranganathan

  • Disaggregated memory for expansion and sharing in blade servers

    Kevin Lim;Jichuan Chang;Trevor Mudge;Parthasarathy Ranganathan

  • Full-System Power Analysis and Modeling for Server Environments

    D. Economou;Suzanne Rivoire;C. Kozyrakis;P. Ranganathan

  • Heterogeneous chip multiprocessors

    R. Kumar;D.M. Tullsen;N.P. Jouppi;P. Ranganathan

  • Ensemble-level Power Management for Dense Blade Servers

    Parthasarathy Ranganathan;Phil Leech;David Irwin;Jeffrey Chase

  • Consistent and durable data structures for non-volatile byte-addressable memory

    Shivaram Venkataraman;Niraj Tolia;Parthasarathy Ranganathan;Roy H. Campbell

  • Dynamically selecting processor cores for overall power efficiency

    Keith Farkas;Norman P. Jouppi;Robert N. Mayo;Parthasarathy Ranganathan

  • A comparison of high-level full-system power models

    Suzanne Rivoire;Parthasarathy Ranganathan;Christos Kozyrakis

  • Reconfigurable caches and their application to media processing

    Parthasarathy Ranganathan;Sarita Adve;Norman P. Jouppi

  • JouleSort: a balanced energy-efficiency benchmark

    Suzanne Rivoire;Mehul A. Shah;Parthasarathy Ranganathan;Christos Kozyrakis

  • GViM: GPU-accelerated virtual machines

    Vishakha Gupta;Ada Gavrilovska;Karsten Schwan;Harshvardhan Kharche

  • Understanding and Designing New Server Architectures for Emerging Warehouse-Computing Environments

    Kevin Lim;Parthasarathy Ranganathan;Jichuan Chang;Chandrakant Patel

  • Rsim: simulating shared-memory multiprocessors with ILP processors

    C.J. Hughes;V.S. Pai;P. Ranganathan;S.V. Adve

  • Weatherman: Automated, Online and Predictive Thermal Mapping and Management for Data Centers

    J. Moore;J.S. Chase;P. Ranganathan

  • Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks

    Amirali Boroumand;Saugata Ghose;Youngsok Kim;Rachata Ausavarungnirun

Frequent Co-Authors

Norman P. Jouppi
Norman P. Jouppi Google (United States)
Vanish Talwar
Vanish Talwar Facebook (United States)
Sarita V. Adve
Sarita V. Adve University of Illinois at Urbana-Champaign
Christos Kozyrakis
Christos Kozyrakis Stanford University
Zhikui Wang
Zhikui Wang Hewlett-Packard (United States)
Naveen Muralimanohar
Naveen Muralimanohar Google (United States)
Kevin Swersky
Kevin Swersky Google (United States)

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