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

A survey on Machine Learning Approaches on Optimization of Workload Mapping Problem in Heterogeneous HPC LandscapeApply

1) Theory: To explore and present "Efficient ML Workload Mapping Techniques" in Heterogeneous HPC Landscape 2) Practical: Data collection and showing an optimize workflow based on a sample workload.

Aasish Kumar Sharma Workflow Optimization

A survey on Machine Learning Approaches on Optimization of Workload Scheduling Problem in Heterogeneous HPC LandscapeApply

1) Theory: To explore and present "Efficient ML Workload Scheduling Techniques" in Heterogeneous HPC Landscape 2) Practical: Data collection and showing an optimize workflow based on a sample workload.

Aasish Kumar Sharma Workflow Optimization

Theoretical Analysis of Mapping Problem Using Quantum ApproachApply

1) Theory: To explore and present how HPC resource mapping problem can be solved using Quantum Approach 2) Practical: To prepare a toy model with a use case of HPC workload and show an try your approach to see the result.

Aasish Kumar Sharma Workflow OptimizationQuantum

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