Aasish Kumar Sharma

aasish_kumar_sharma.jpg

E-mailaasish-kumar.sharma@gwdg.de

Biography

Aasish is a scientific employee under Julian Kunkel and is interested in performance optimization, big data analysis, data science and computer vision using advanced technologies such as parallel and high-performance computing, as well as, quantum computing.
ResearchGate

Research Interests

  • Optimization
  • High-Performance Computing
  • Data Science

Teaching

Open Thesis Topics

Constraint Programming for Workload Optimization in HPCApply

Constraint programming offers a robust approach to optimize workload scheduling in HPC systems. This project explores formulating resource allocation and task scheduling as constraint satisfaction problems to minimize makespan and maximize resource utilization.

Aasish Kumar Sharma Constraint ProgrammingHPCWorkload Optimization

Integration of Constraint Programming and Machine Learning for HPC SchedulingApply

Machine learning can enhance constraint programming by predicting constraints or guiding solution search. This project investigates hybrid approaches to improve scheduling performance in HPC systems.

Aasish Kumar Sharma Constraint ProgrammingMachine LearningHPC Scheduling

Quantum Constraint Programming for Workload Optimization in HPCApply

Constraint programming offers a robust approach to optimize workload scheduling in HPC systems. This project explores formulating resource allocation and task scheduling as constraint satisfaction problems to minimize makespan and maximize resource utilization.

Aasish Kumar Sharma Constraint ProgrammingHPCQuantum ComputingWorkload Optimization

Theses

  • Performance Analysis of Convolutional Neural Network Applying Quantum Annealing, Aasish Kumar Sharma (Master's Thesis), Advisors: Sanjeeb Prasad Pandey, 2020-12-30, BibTeX URL

Publications

2024

  • Poster: Optimizing Heterogeneous HPC Workflows With Data Transfer Time (Aasish Kumar Sharma, Christian Boehme, Patrick Gelß, Julian Martin Kunkel), NHR Conference 2024, Darmstadt, Darmstadt, Germany, 2024-09-09 BibTeX URL PDF
  • HOSHMAND: Accelerated AI-Driven Scheduler Emulating Conventional Task Distribution Techniques for Cloud Workloads (Michael Bidollahkhani, Aasish Kumar Sharma, Julian Kunkel), IEEE Computers, Software, and Applications Conference, pp. 1-8, IEEE, IEEE, COMPSAC 2024, 2024-07 BibTeX

All publications as BibTex