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Computer Science

D-Index
35
Citations
5226
World Ranking
11705
National Ranking
4793

Overview

Maya Gokhale is affiliated with the Lawrence Livermore National Laboratory in the United States. Their research contributions primarily lie within the field of Computer Science, with a focus on several subfields including Computer Networks and Communications, Hardware and Architecture, Information Systems, Artificial Intelligence, and Electrical and Electronic Engineering.

The main topics covered in their work span across Advanced Data Storage Technologies, Parallel Computing and Optimization Techniques, Cloud Computing and Resource Management, Distributed and Parallel Computing Systems, Scientific Computing and Data Management, Advanced Neural Network Applications, and Seismology and Earthquake Studies.

Frequent publication venues for Maya Gokhale include:

  • Proceedings of the IEEE
  • arXiv (Cornell University)
  • IEEE Micro
  • The International Journal of High Performance Computing Applications
  • Hematological Oncology

Among recent papers authored or coauthored by Maya Gokhale are the following:

  • Accelerators for Classical Molecular Dynamics Simulations of Biomolecules, 2022, Journal of Chemical Theory and Computation
  • Enabling Scalable and Extensible Memory-Mapped Datastores in Userspace, 2021, IEEE Transactions on Parallel and Distributed Systems
  • Metall: A persistent memory allocator for data-centric analytics, 2022, Parallel Computing
  • Combining Emulation and Simulation to Evaluate a Near Memory Key/Value Lookup Accelerator, 2021, arXiv (Cornell University)
  • Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy, 2021, arXiv (Cornell University)

Collaborative work is a notable aspect of Maya Gokhale's career, with frequent coauthors including:

  • John Baillieul
  • Gert Cauwenberghs
  • Jocelyn Chanussot
  • Hsiao-Hwa Chen
  • Jack Dongarra

The scope of Gokhale's research integrates developments in high performance computing and data storage with advances in distributed and parallel systems, reflecting a broad engagement with current challenges and technical innovations in computing infrastructure and applications.

Best Publications

  • Processing in Memory: The Terasys Massively Parallel PIM Array

    Unknown

  • Building and using a highly parallel programmable logic array

    M. Gokhale;W. Holmes;A. Kopser;S. Lucas

  • Stream-oriented FPGA computing in the Streams-C high level language

    Unknown

  • Scalable metagenomic taxonomy classification using a reference genome database

    Sasha K. Ames;David A. Hysom;Shea N. Gardner;G. Scott Lloyd

  • Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory

    Roger Pearce;Roger Pearce;Maya Gokhale;Nancy M. Amato

  • NAPA C: compiling for a hybrid RISC/FPGA architecture

    Unknown

  • Accelerating a Random Forest Classifier: Multi-Core, GP-GPU, or FPGA?

    Brian Van Essen;Chris Macaraeg;Maya Gokhale;Ryan Prenger

  • System evaluation of the Intel optane byte-addressable NVM

    Ivy B. Peng;Maya B. Gokhale;Eric W. Green

  • Granidt: Towards Gigabit Rate Network Intrusion Detection Technology

    Unknown

  • Evaluation of the streams-C C-to-FPGA compiler: an applications perspective

    Unknown

  • Trident: From High-Level Language to Hardware Circuitry

    J.L. Tripp;M.B. Gokhale;K.D. Peterson

  • Dynamic reconfiguration for management of radiation-induced faults in FPGAs

    Maya Gokhale;Paul Graham;Michael Wirthlin;D. Eric Johnson

  • Faster parallel traversal of scale free graphs at extreme scale with vertex delegates

    Roger Pearce;Maya Gokhale;Nancy M. Amato

  • Hardware/Software Approach to Molecular Dynamics on Reconfigurable Computers

    Ronald Scrofano;Maya Gokhale;Frans Trouw;Viktor Prasanna

  • Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory

    Roger Pearce;Maya Gokhale;Nancy M. Amato

  • Minerva: Accelerating Data Analysis in Next-Generation SSDs

    Arup De;Maya Gokhale;Rajesh Gupta;Steven Swanson

  • Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity

    Jeffrey S Vetter;Ron Brightwell;Maya Gokhale;Pat McCormick

  • Hardware Technologies for High-Performance Data-Intensive Computing

    M. Gokhale;J. Cohen;A. Yoo;W.M. Miller

  • Real-Time Classification of Multimedia Traffic Using FPGA

    Weirong Jiang;Maya Gokhale

  • DI-MMAP--a scalable memory-map runtime for out-of-core data-intensive applications

    Brian Essen;Henry Hsieh;Sasha Ames;Roger Pearce

  • On the Role of NVRAM in Data-intensive Architectures: An Evaluation

    Brian Van Essen;Roger Pearce;Sasha Ames;Maya Gokhale

  • Reliability Analysis of Large Circuits Using Scalable Techniques and Tools

    D. Bhaduri;S.K. Shukla;P.S. Graham;M.B. Gokhale

  • Accelerating Molecular Dynamics Simulations with Reconfigurable Computers

    R. Scrofano;M.B. Gokhale;F. Trouw;V.K. Prasanna

  • Experience with a Hybrid Processor: K-Means Clustering

    Maya Gokhale;Jan Frigo;Kevin Mccabe;James Theiler

  • Near memory data structure rearrangement

    Maya Gokhale;Scott Lloyd;Chris Hajas

Frequent Co-Authors

Nancy M. Amato
Nancy M. Amato University of Illinois at Urbana-Champaign
Sriram Krishnamoorthy
Sriram Krishnamoorthy University of California, Santa Barbara
John Shalf
John Shalf Lawrence Berkeley National Laboratory
Ron Brightwell
Ron Brightwell Sandia National Laboratories
Robert Ross
Robert Ross Argonne National Laboratory
Jeffrey S. Vetter
Jeffrey S. Vetter Oak Ridge National Laboratory
P. Sadayappan
P. Sadayappan University of Utah
Bronis R. de Supinski
Bronis R. de Supinski Lawrence Livermore National Laboratory
Keren Bergman
Keren Bergman Columbia University

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