Open Source in Student Activities

June 17, 2021 @ 6:30 pm – 8:30 pm
Julian Kunkel, Cornelia Boldyreff

On this thematic evening, students illustrate on their research project how open source and data matters to them.

We organize this event annually to give students space to talk about their projects; the talks should be around 20 minutes (including 2-3 min discussion). If you are interested to give a talk, contact us!

Once again, due to COVID-19 it will be a purely virtual meetup. We’ll be live streaming using BigBlueButton to provide a rich online experience for participants. As always the talks will be recorded for later upload to YouTube. You are invited to join and socialize from 18:00, talks will run from 18:30-20:30 with 30 minutes in the end for further discussion and socializing.

In a change to our past practice, there is no requirement to register, you can just connect to BigBlueButton on this link.

18:20 – Feel free to join the online meeting to chat with other participants

18:30 – Short introduction (5 min) of the evening by Julian Kunkel and Cornelia Boldyreff

18:35 – Presentations

20:35 – Closing Discussion

We were live streaming via BigBlueButton and recording the talks for later posting on YouTube.

Note: Please aim to connect at the latest by 18:25 as the event will start at 18:30 prompt.


The videos will be available on YouTube.

Smart Mapping of Scientific Workflows onto Heterogeneous Resources

New and accelerated hardware is constantly being added to the HPCs and Data Centers, increasing the heterogeneity of the scientific simulation environments. To increase the performance of a simulation by better utilizing the allocated resources requires detailed knowledge about the underlying hardware. Mapping tasks to heterogeneous resources is a complex (NP-Complete) problem on its own. If we can create an abstract way to structure a workflow so that it can choose the best mapping by utilizing accelerated hardware, we can address both of the aforementioned problems. Having well-defined operators with associated performance models, we can predict a task’s makespan on a given host, accurately. This prediction can then be used within evolutionary mapping techniques to help explore a near-optimal solution where underlying heterogeneous hardware resources are better utilized with specialized implementations. A workflow, based on such an abstraction, will be able to adapt itself to new hosting environments with minimal change. Having this headspace, scientists can spend time on improving their model/simulation not on intrinsic details required to adapt their workflow to new environments.

Erdem Yilmaz is a software engineer, working at Ultromics, United Kingdom. Progressing on his research project to full-fill the requirements of a PhD in Computer Science Dept. of University of Reading, United Kingdom.

Breaking through the Chinese walls to the open-source and data for my research project

Open source plays important role in many different fields, especially in research. The research project I’m currently working on is not an exception, its goal is to explore the application of machine and deep learning methods in algorithmic trading with the main focus on the trading strategy. In this process I need at least three core components available in open access:

  • Prior research papers in this field
  • Code for the machine learning models described in these papers
  • Data-sets used for training and testing these models

At the beginning of my project, I expected to easily open source all of these, however, in each of these three dimensions I encountered challenges of two kinds: availability and quality. I would like to share on the upcoming thematic evening what are the particular open sourcing challenges I am experiencing and how I’m overcoming these Chinese walls.

Ilya Zaznov is a PhD student in the department of Computer Science, University of Reading, UK. His PhD research area is an application of machine/deep learning methods to the algorithmic trading. The main topic of the thesis is formulated as “Application of Deep Learning methods to the Limit Order Book and Order Flow Data for Stock Price Prediction”.

Performance of the S3 interface (Amazon Simple Storage Service) in an HPC environment

The line between HPC and Cloud is getting blurry. Performance is still the main driver in HPC, on the other hand, cloud storage systems assume to offer low-latency, high throughput, high availability, and scalability. The Simple Storage Service S3 has emerged as the de-facto storage API for object-storage in the Cloud. It is inevitable to assess its performance for HPC workloads and to check if it is already a viable alternative, or if further advances are necessary.

Frank Gadban is a PhD student at the Hamburg University, currently investigating the Convergence between HPC and Cloud.


Maha Abdelmohsen