The various HPC systems operated by GWDG are also an object of GWDG research on HPC methods, the results of which are
then used to improve operation and / or user experience. In this context GWDG is also inolved in various third party
projects.
In addition to the service-driven research, academic teaching in the fields of computer science is a focus of our work.
For this reason, we are actively involved in the education of students in many ways. The GWDG currently has three
research groups whose teaching activities are anchored at the
Institute of Computer Science at the University of Göttingen and whose
teaching content is part of various degree programmes.
A complete list of the GWDG research projects can be found
here.
Recent HPC Publications
2022
Improve the Deep Learning Models in Forestry Based on Explanations and Expertise
(Ximeng Cheng, Ali Doosthosseini, Julian Kunkel),
In Frontiers in Plant Science,
Schloss Dagstuhl -- Leibniz-Zentrum für Informatik,
ISSN: 1664-462X,
2022-05-01
DOIPDF
Improve the Deep Learning Models in Forestry Based on Explanations and Expertise
(Ximeng Cheng, Ali Doosthosseini, Julian Kunkel),
In Frontiers in Plant Science,
Schloss Dagstuhl -- Leibniz-Zentrum für Informatik,
ISSN: 1664-462X,
2022-05-01
DOIPDF
2021
User-Centric System Fault Identification Using IO500 Benchmark
(Radita Liem, Dmytro Povaliaiev, Jay Lofstead, Julian Kunkel, Christian Terboven),
pp. 35-40,
IEEE,
2021-12-01
DOIPDF
BibTeX: Improve the Deep Learning Models in Forestry Based on Explanations and Expertise
@article{ITDLMIFBOE22,
abstract = {"In forestry studies, deep learning models have achieved excellent performance in many application scenarios (e.g., detecting forest damage). However, the unclear model decisions (i.e., black-box) undermine the credibility of the results and hinder their practicality. This study intends to obtain explanations of such models through the use of explainable artificial intelligence methods, and then use feature unlearning methods to improve their performance, which is the first such attempt in the field of forestry. Results of three experiments show that the model training can be guided by expertise to gain specific knowledge, which is reflected by explanations. For all three experiments based on synthetic and real leaf images, the improvement of models is quantified in the classification accuracy (up to 4.6%) and three indicators of explanation assessment (i.e., root-mean-square error, cosine similarity, and the proportion of important pixels). Besides, the introduced expertise in annotation matrix form was automatically created in all experiments. This study emphasizes that studies of deep learning in forestry should not only pursue model performance (e.g., higher classification accuracy) but also focus on the explanations and try to improve models according to the expertise."},
author = {Ximeng Cheng and Ali Doosthosseini and Julian Kunkel},
doi = {https://doi.org/10.3389/fpls.2022.902105},
issn = {1664-462X},
journal = {Frontiers in Plant Science},
month = {05},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum für Informatik},
title = {Improve the Deep Learning Models in Forestry Based on Explanations and Expertise},
type = {article},
year = {2022},
}
BibTeX: Improve the Deep Learning Models in Forestry Based on Explanations and Expertise
@article{ITDLMIFBOE22,
abstract = {"In forestry studies, deep learning models have achieved excellent performance in many application scenarios (e.g., detecting forest damage). However, the unclear model decisions (i.e., black-box) undermine the credibility of the results and hinder their practicality. This study intends to obtain explanations of such models through the use of explainable artificial intelligence methods, and then use feature unlearning methods to improve their performance, which is the first such attempt in the field of forestry. Results of three experiments show that the model training can be guided by expertise to gain specific knowledge, which is reflected by explanations. For all three experiments based on synthetic and real leaf images, the improvement of models is quantified in the classification accuracy (up to 4.6%) and three indicators of explanation assessment (i.e., root-mean-square error, cosine similarity, and the proportion of important pixels). Besides, the introduced expertise in annotation matrix form was automatically created in all experiments. This study emphasizes that studies of deep learning in forestry should not only pursue model performance (e.g., higher classification accuracy) but also focus on the explanations and try to improve models according to the expertise."},
author = {Ximeng Cheng and Ali Doosthosseini and Julian Kunkel},
doi = {https://doi.org/10.3389/fpls.2022.902105},
issn = {1664-462X},
journal = {Frontiers in Plant Science},
month = {05},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum für Informatik},
title = {Improve the Deep Learning Models in Forestry Based on Explanations and Expertise},
type = {article},
year = {2022},
}
BibTeX: User-Centric System Fault Identification Using IO500 Benchmark
@inproceedings{USFIUIBLPL21,
abstract = {"I/O performance in a multi-user environment is difficult to predict. Users do not know what I/O performance to expect when running and tuning applications. We propose to use the IO500 benchmark as a way to guide user expectations on their application’s performance and to aid identifying root causes of their I/O problems that might come from the system. Our experiments describe how we manage user expectation with IO500 and provide a mechanism for system fault identification. This work also provides us with information of the tail latency problem that needs to be addressed and granular information about the impact of I/O technique choices (POSIX and MPI-IO)."},
author = {Radita Liem and Dmytro Povaliaiev and Jay Lofstead and Julian Kunkel and Christian Terboven},
booktitle = {In 2021 IEEE/ACM Sixth International Parallel Data Systems Workshop (PDSW)},
conference = {International Parallel Data Systems Workshop (PDSW)},
doi = {https://doi.org/10.1109/PDSW54622.2021.00011},
editor = {},
location = {St. Louis},
month = {12},
pages = {35-40},
publisher = {IEEE},
title = {User-Centric System Fault Identification Using IO500 Benchmark},
type = {inproceedings},
year = {2021},
}
An overview of all GWDG publications can be found here.
Available Projects and Bachelor, Master and PhD Theses
Topic
Professor
Type
Token Management for an API to utilise HPC resources in generic workflows
Parallel applications on HPC systems often rely on system specific MPI (Message Passing Interface) and interconnect libraries, for example for Infiniband or OmniPath networks. This partially offsets one main advantage of containerizing such applications, namely the portability between different platforms. The goal of this project is to evaluate different ways of integrating system specific communication libraries into containers, allowing for porting these containers to a different platform with minimal effort. A PoC should be implemented and benchmarked against running natively on a system. Supervisor: Christian Boehme 📧
Digital Twin of the data center: Creation of a 3D model for the GWDG Data Center for virtual reality walk-throughs
Prof. Julian Kunkel
BSc, MSc
Supervisor:
Digital teaching: Development of examination scenarios for HPC skills
Prof. Julian Kunkel
BSc, MSc
Supervisor:
Development of a provenance aware ad-hoc interface for a data lake