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Overview

The Computing Systems research team at TCS focuses on the development of cost effective, high performing fault tolerant systems for modern applications.  Apart from analytics and ML/DL, it also includes engineering research leading to the development of performance models, middleware and frameworks for accelerating development and deployment of data driven and transactional applications. The team also works on the benchmarking and analysis of bleeding-edge hardware and software technologies to identify applications that can benefit from such frameworks.

Fields

The sub-areas active within this research area include:

  • Performance for AI: This team is focused on building scalable high-performance architectures for ML/DL/RL pipelines, including both training and inference, by accelerating data management, innovations in ML/DL/RL algorithms, high-performant feature stores, and hybrid deployments using serverless architectures.
  • AI for Performance: In this group, they focus on use of ML/DL/RL algorithms to model and accelerate system performance
  • Special-purpose hardware and FPGA: This team keeps track of the latest hardware developments and design custom hardware to enhance the performance of enterprise systems
  • Performance modeling and optimization: This group works on building highly accurate predictive performance models by considering a number of applications, systems, and workload characteristics, and optimizing workload and system configurations to enhance the overall system performance by leveraging these models
  • Quantum computing: This team works on identifying algorithms and applications that are likely to benefit from quantum computing in the near future, in addition to design systems interfacing with quantum computers

People & Patents

  • Research team: Led by Principal Scientist Manoj Nambiar, and Principal Scientist Rekha Singhal, the team comprises of Dheeraj Chahal; Mayank Mishra; Shruti Kunde; and Sunil Puranik

  • Academic partners: Tata Institute of Fundamental Research, India

  • Patents: This team has been granted more than 10 patents
  • Publications since 2016:
  1. Cloning IO intensive workloads using synthetic benchmarks

    International Conference on Performance Engineering

    15/12/2016

    Dheeraj Chahal,

  2. Trace Replay Based I/O Performance Studies for Enterprise Workload Migration

    Computer Measurement Group

    18/10/2016

  3. Dheeraj Chahal,

    PerfExt++: Performance Extrapolation of IO Intensive Workloads

    International Conference on Performance Engineering

    22/01/2017

  4. Dheeraj Chahal,

    QUDOS : Model driven software performance engineering - challenges and ways ahead

    International Conference on Performance Engineering

    19/02/2018

  5. Manoj Karunakaran Nambiar,

    Predicting the Runtime of Memory Intensive Batch Workloads

    International Conference on Parallel Processing

    28/05/2018

  6. Dheeraj Chahal, Manoj Karunakaran Nambiar, Benny Mathew,

    Simulation Based Job Scheduling Optimization for Batch Workloads

    International Conference on Performance Engineering

    07/12/2018

  7. Dheeraj Chahal, Manoj Karunakaran Nambiar, Benny Mathew,

    Migrating a Recommendation System to Cloud Using ML Workflow

    International Conference on Performance Engineering

    04/02/2020

  8. Dheeraj Chahal, Manoj Karunakaran Nambiar, Sharod Roy Choudhury, Ravi Ojha,

    Determining web service dependencies from limited TCP packets

    International Conference on COMmunication Systems and NETworkS

    08/11/2018

  9. Dhaval Shah1, Manoj Karunakaran Nambiar,

    Lightweight web Service Dependency, Tool

    International Conference on COMmunication Systems and NETworkS

    03/12/2018

  10. Dhaval Shah1, Manoj Karunakaran Nambiar,

    Early Experiences with Quantum Annealing

    International Conference on Performance Engineering

    13/03/2019

  11. Manoj Karunakaran Nambiar,

    Cracking the Monolith : Challenges in Data Transitioning to Cloud Native Architectures

    European Conference on Software Architecture

    05/07/2018

  12. Mayank Mishra, Mayank Mishra, Chetan Phalak, Shruti Kunde,

    ATA: Architecture-based Technology Advisor Tool

    European Conference on Software Architecture

    04/07/2018

  13. Shruti Kunde, Chetan Phalak,

    Performance Optimization of OpenFOAM on Clusters of Intel Xeon Phi TM Processors

    IEEE International Conference on High Performance Computing

    25/09/2017

  14. Ravi Ojha, Manoj Karunakaran Nambiar,

    Efficient Multiway Hash Join on Reconfigurable Hardware

    International Conference on Very Large Data bases

    20/06/2019

  15. Rekha Singhal,

    Accelerated Polystore System for Heterogeneous Workloads

    International Conference on Distributed Computing Systems

    28/03/2019

  16. Rekha Singhal,

    Tutorial on Benchmarking Big Data Analytic Systems

    International Conference on Performance Engineering

    05/02/2020

  17. Rekha Singhal,

    Tutorial on Benchmarking Big Data Analytic System

    International Conference on Performance Engineering

    20.04.2020

  18. Rekha Singhal,

    Migrating a Recommendation System to Cloud Using ML Workflow

    International Conference on Performance Engineering

    20.04.2020

  19. D Chahal, Ravi Ojha,

    The vision on accelerating enterprise IT system 2.0

    ACM Special Interest Group on Management of Data

    14.06.2020

  20. Rekha Singhal, M Nambiar, D Chahal, Mishra M, Shruti Kunde,

    Recommending in changing times

    ACM Conference on Recommender Systems

    22.09.2020

  21. Shruti Kunde, Mishra M, Amey Pandit, Rekha Singhal, M Nambiar, Gautam Shroff,

    Benchmarking performance of RaySGD and Horovod for big data applications

    2020 IEEE International Conference on Big Data

    10.12.2020

    Shruti Kunde, Amey Pandit, Rekha Singhal

 

 

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