Biography

Prof. Dr. Kunkel is a Professor in High-Performance Computing at the University of Göttingen and the Deputy Head High-Performance Computing at GWDG. Previously, he was a Lecturer at the Computer Science Department at the University of Reading and a postdoc in the research department of the German Climate Computing Center (DKRZ). He manages several research projects revolving around High-Performance Computing and particularly high-performance storage. Julian became interested in the topic of HPC storage in 2003, during his studies of computer science. Besides his main goal to provide efficient and performance-portable I/O, his HPC-related interests are data reduction techniques, performance analysis of parallel applications and parallel I/O, management of cluster systems, cost-efficiency considerations, and the software engineering of scientific software. Dr. Kunkel is a member of many international program committees, he is a founding member of the IO500 benchmarking effort, the Virtual Institute for I/O, and the HPC Certification Forum. He is committed to excellence in research and teaching.

List of contributions to the research community

Research Interests

• Data-driven workflows
• Parallel file systems
• Application of machine learning methods
• Performance portability
• Data reduction techniques
• Management of cluster systems
• Performance analysis of parallel applications and parallel I/O
• Software engineering of scientific software
• Personalized teaching

Recent Publications

2023

• Secure HPC: A workflow providing a secure partition on an HPC system (Hendrik Nolte, Nicolai Spicher, Andrew Russel, Tim Ehlers, Sebastian Krey, Dagmar Krefting, Julian Kunkel), 2023-01-01 DOI BibTeX

2022

• Performance Evaluation of Open-Source Serverless Platforms for Kubernetes (Jonathan Decker, Piotr Kasprzak, Julian Kunkel), In Algorithms, MDPI, ISSN: 1999-4893, 2022-06-02 URL DOI PDF BibTeX
• Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review (Ekene F. Ozioko, Julian Kunkel, Frederic Stahl), In Journal of Advanced Transportation, Hindawi, 2022-05-30 DOI PDF BibTeX
• 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 DOI PDF BibTeX
• Predicting Stock Price Changes Based on the Limit Order Book: A Survey (Ilia Zaznov, Julian Kunkel, Alfonso Dufour, Atta Badii), In Mathematics, Series: 1234, MDPI, ISSN: 2227-7390, 2022-04-01 URL DOI PDF BibTeX
• Predicting Stock Price Changes Based on the Limit Order Book: A Survey (Ilia Zaznov, Julian Kunkel, Alfonso Dufour, Atta Badii), 2022-01-01 DOI BibTeX
• Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review (Ekene F. Ozioko, Julian Kunkel, Fredric Stahl), 2022-01-01 BibTeX

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 DOI PDF BibTeX
• Toward a Workflow for Identifying Jobs with Similar I/O Behavior Utilizing Time Series Analysis (Julian Kunkel, Eugen Betke), Series: Lecture Notes in Computer Science, pp. 161–173, Springer, 2021-11-01 DOI PDF BibTeX
• Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332) (Philip Carns, Julian Kunkel, Kathryn Mohror, Martin Schulz), In Dagstuhl Reports, pp. 16-75, Schloss Dagstuhl -- Leibniz-Zentrum für Informatik, ISSN: 2192-5283, 2021-09-14 URL DOI PDF BibTeX

@article{2_133248,

	author = {Hendrik Nolte and Nicolai Spicher and Andrew Russel and Tim Ehlers and Sebastian Krey and Dagmar Krefting and Julian Kunkel},
	doi = {10.1016/j.future.2022.12.019},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/133248},
	month = {01},
	title = {Secure HPC: A workflow providing a secure partition on an HPC system},
	type = {article},
	year = {2023},
}

@article{PEOOSPFKDK22,

	abstract = {| Serverless computing has grown massively in popularity over the last few years, and has provided developers with a way to deploy function-sized code units  without having to take care of the actual servers or deal with logging,  monitoring, and scaling of their code. High-performance computing (HPC)  clusters can profit from improved serverless resource sharing capabilities  compared to reservation-based systems such as Slurm. However, before running  self-hosted serverless platforms in HPC becomes a viable option, serverless  platforms must be able to deliver a decent level of performance. Other  researchers have already pointed out that there is a distinct lack of studies  in the area of comparative benchmarks on serverless platforms, especially for  open-source self-hosted platforms. This study takes a step towards filling  this gap by systematically benchmarking two promising self-hosted Kubernetes- based serverless platforms in comparison. While the resulting benchmarks signal
 potential, they demonstrate that many opportunities for performance 
 improvements in serverless computing are being left on the table.},
	author = {Jonathan Decker and Piotr Kasprzak and Julian Kunkel},
	doi = {https://doi.org/10.3390/a15070234},
	issn = {1999-4893},
	journal = {Algorithms},
	month = {06},
	publisher = {MDPI},
	title = {Performance Evaluation of Open-Source Serverless Platforms for Kubernetes},
	type = {article},
	url = {https://www.mdpi.com/1999-4893/15/7/234},
	year = {2022},
}

@article{RICSFMTADV22,

	abstract = {| Autonomous vehicles (AVs) are emerging with enormous potentials to solve many  challenging road traffic problems. The AV emergence leads to a paradigm shift  in the road traffic system, making the penetration of autonomous vehicles fast  and its coexistence with human-driven cars inevitable. The migration from the  traditional driving to the intelligent driving system with AV’s gradual  deployment needs supporting technology to address mixed traffic systems problems,  mixed driving behaviour in a car-following model, variation in-vehicle type  control means, the impact of a proportion of AV in traffic mixed traffic,  and many more. The migration to fully AV will solve many traffic problems:  desire to reclaim travel and commuting time, driving comfort, and accident  reduction. Motivated by the above facts, this paper presents an extensive  review of road intersection mixed traffic management techniques with a  classification matrix of different traffic management strategies and  technologies that could effectively describe a mix of human and autonomous  vehicles. It explores the existing traffic control strategies and analyses  their compatibility in a mixed traffic environment. Then review their drawback  and build on it for the proposed robust mix of traffic management schemes.  Though many traffic control strategies have been in existence, the analysis  presented in this paper gives new insights to the readers on the applications  of the cell reservation strategy in a mixed traffic environment. Though many  traffic control strategies have been in existence, the Gipp’s car-following  model has shown to be very effective for optimal traffic flow performance.},
	author = {Ekene F. Ozioko and Julian Kunkel and Frederic Stahl},
	doi = {https://doi.org/10.1155/2022/2951999},
	journal = {Journal of Advanced Transportation},
	month = {05},
	publisher = {Hindawi},
	title = {Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review},
	type = {article},
	year = {2022},
}

@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},
}

@article{PSPCBOTLOB22,

	abstract = {"This survey starts with a general overview of the strategies for stock price change predictions based on market data and in particular Limit Order Book (LOB) data. The main discussion is devoted to the systematic analysis, comparison, and critical evaluation of the state-of-the-art studies in the research area of stock price movement predictions based on LOB data. LOB and Order Flow data are two of the most valuable information sources available to traders on the stock markets. Academic researchers are actively exploring the application of different quantitative methods and algorithms for this type of data to predict stock price movements. With the advancements in machine learning and subsequently in deep learning, the complexity and computational intensity of these models was growing, as well as the claimed predictive power. Some researchers claim accuracy of stock price movement prediction well in excess of 80%. These models are now commonly employed by automated market-making programs to set bids and ask quotes. If these results were also applicable to arbitrage trading strategies, then those algorithms could make a fortune for their developers. Thus, the open question is whether these results could be used to generate buy and sell signals that could be exploited with active trading. Therefore, this survey paper is intended to answer this question by reviewing these results and scrutinising their reliability. The ultimate conclusion from this analysis is that although considerable progress was achieved in this direction, even the state-of-art models can not guarantee a consistent profit in active trading. Taking this into account several suggestions for future research in this area were formulated along the three dimensions: input data, model’s architecture, and experimental setup. In particular, from the input data perspective, it is critical that the dataset is properly processed, up-to-date, and its size is sufficient for the particular model training. From the model architecture perspective, even though deep learning models are demonstrating a stronger performance than classical models, they are also more prone to over-fitting. To avoid over-fitting it is suggested to optimize the feature space, as well as a number of layers and neurons, and apply dropout functionality. The over-fitting problem can be also addressed by optimising the experimental setup in several ways: Introducing the early stopping mechanism; Saving the best weights of the model achieved during the training; Testing the model on the out-of-sample data, which should be separated from the validation and training samples. Finally, it is suggested to always conduct the trading simulation under realistic market conditions considering transactions costs, bid–ask spreads, and market impact. View Full-Text"},
	author = {Ilia Zaznov and Julian Kunkel and Alfonso Dufour and Atta Badii},
	doi = {https://doi.org/10.3390/math10081234},
	editor = {},
	issn = {2227-7390},
	journal = {Mathematics},
	month = {04},
	publisher = {MDPI},
	series = {1234},
	title = {Predicting Stock Price Changes Based on the Limit Order Book: A Survey},
	type = {article},
	url = {https://www.mdpi.com/2227-7390/10/8/1234},
	year = {2022},
}

@article{2_113814,

	abstract = {"Autonomous vehicles (AVs) are emerging with enormous potentials to solve many challenging road traffic problems. The AV emergence leads to a paradigm shift in the road traffic system, making the penetration of autonomous vehicles fast and its coexistence with human-driven cars inevitable. The migration from the traditional driving to the intelligent driving system with AV’s gradual deployment needs supporting technology to address mixed traffic systems problems, mixed driving behaviour in a car-following model, variation in-vehicle type control means, the impact of a proportion of AV in traffic mixed traffic, and many more. The migration to fully AV will solve many traffic problems: desire to reclaim travel and commuting time, driving comfort, and accident reduction. Motivated by the above facts, this paper presents an extensive review of road intersection mixed traffic management techniques with a classification matrix of different traffic management strategies and technologies that could effectively describe a mix of human and autonomous vehicles. It explores the existing traffic control strategies and analyses their compatibility in a mixed traffic environment. Then review their drawback and build on it for the proposed robust mix of traffic management schemes. Though many traffic control strategies have been in existence, the analysis presented in this paper gives new insights to the readers on the applications of the cell reservation strategy in a mixed traffic environment. Though many traffic control strategies have been in existence, the Gipp’s car-following model has shown to be very effective for optimal traffic flow performance."},
	author = {Ekene F. Ozioko and Julian Kunkel and Fredric Stahl},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/113814},
	month = {01},
	title = {Road Intersection Coordination Scheme for Mixed Traffic (Human-Driven and Driverless Vehicles): A Systematic Review},
	type = {article},
	year = {2022},
}

@article{2_107425,

	abstract = {"This survey starts with a general overview of the strategies for stock price change predictions based on market data and in particular Limit Order Book (LOB) data. The main discussion is devoted to the systematic analysis, comparison, and critical evaluation of the state-of-the-art studies in the research area of stock price movement predictions based on LOB data. LOB and Order Flow data are two of the most valuable information sources available to traders on the stock markets. Academic researchers are actively exploring the application of different quantitative methods and algorithms for this type of data to predict stock price movements. With the advancements in machine learning and subsequently in deep learning, the complexity and computational intensity of these models was growing, as well as the claimed predictive power. Some researchers claim accuracy of stock price movement prediction well in excess of 80%. These models are now commonly employed by automated market-making programs to set bids and ask quotes. If these results were also applicable to arbitrage trading strategies, then those algorithms could make a fortune for their developers. Thus, the open question is whether these results could be used to generate buy and sell signals that could be exploited with active trading. Therefore, this survey paper is intended to answer this question by reviewing these results and scrutinising their reliability. The ultimate conclusion from this analysis is that although considerable progress was achieved in this direction, even the state-of-art models can not guarantee a consistent profit in active trading. Taking this into account several suggestions for future research in this area were formulated along the three dimensions: input data, model’s architecture, and experimental setup. In particular, from the input data perspective, it is critical that the dataset is properly processed, up-to-date, and its size is sufficient for the particular model training. From the model architecture perspective, even though deep learning models are demonstrating a stronger performance than classical models, they are also more prone to over-fitting. To avoid over-fitting it is suggested to optimize the feature space, as well as a number of layers and neurons, and apply dropout functionality. The over-fitting problem can be also addressed by optimising the experimental setup in several ways: Introducing the early stopping mechanism; Saving the best weights of the model achieved during the training; Testing the model on the out-of-sample data, which should be separated from the validation and training samples. Finally, it is suggested to always conduct the trading simulation under realistic market conditions considering transactions costs, bid–ask spreads, and market impact."},
	author = {Ilia Zaznov and Julian Kunkel and Alfonso Dufour and Atta Badii},
	doi = {10.3390/math10081234},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/107425},
	month = {01},
	title = {Predicting Stock Price Changes Based on the Limit Order Book: A Survey},
	type = {article},
	year = {2022},
}

@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},
}

@inproceedings{TAWFIJWSIB21,

	abstract = {"One goal of support staff at a data center is to identify inefficient jobs and to improve their efficiency. Therefore, a data center deploys monitoring systems that capture the behavior of the executed jobs. While it is easy to utilize statistics to rank jobs based on the utilization of computing, storage, and network, it is tricky to find patterns in 100,000 jobs, i.e., is there a class of jobs that aren't performing well. Similarly, when support staff investigates a specific job in detail, e.g., because it is inefficient or highly efficient, it is relevant to identify related jobs to such a blueprint. This allows staff to understand the usage of the exhibited behavior better and to assess the optimization potential. In this article, our goal is to identify jobs similar to an arbitrary reference job. In particular, we sketch a methodology that utilizes temporal I/O similarity to identify jobs related to the reference job. Practically, we apply several previously developed time series algorithms. A study is conducted to explore the effectiveness of the approach by investigating related jobs for a  reference job. The data stem from DKRZ's supercomputer Mistral and include more than 500,000 jobs that have been executed for more than 6 months of operation. Our analysis shows that the strategy and algorithms bear the potential to identify similar jobs, but more testing is necessary."},
	author = {Julian Kunkel and Eugen Betke},
	booktitle = {High Performance Computing: ISC High Performance 2021 International Workshops, Revised Selected Papers},
	conference = {ISC HPC},
	doi = {https://doi.org/10.1007/978-3-030-90539-2_10},
	editor = {},
	isbn = {978-3-030-90539-2},
	location = {Frankfurt, Germany},
	month = {11},
	number = {12761},
	pages = {161–173},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	title = {Toward a Workflow for Identifying Jobs with Similar I/O Behavior Utilizing Time Series Analysis},
	type = {inproceedings},
	year = {2021},
}

@article{UIBISADCSC21,

	abstract = {| Two key changes are driving an immediate need for deeper understanding of I/O  workloads in high-performance computing (HPC): applications are evolving beyond  the traditional bulk-synchronous models to include integrated multistep workflows,  in situ analysis, artificial intelligence, and data analytics methods; and storage  systems designs are evolving beyond a two-tiered file system and archive model to  complex hierarchies containing temporary, fast tiers of storage close to compute  resources with markedly different performance properties. Both of these changes  represent a significant departure from the decades-long status quo and require  investigation from storage researchers and practitioners to understand their  impacts on overall I/O performance. Without an in-depth understanding of I/O  workload behavior, storage system designers, I/O middleware developers, facility  operators, and application developers will not know how best to design or utilize  the additional tiers for optimal performance of a given I/O workload. The goal  of this Dagstuhl Seminar was to bring together experts in I/O performance analysis  and storage system architecture to collectively evaluate how our community is  capturing and analyzing I/O workloads on HPC systems, identify any gaps in our  methodologies, and determine how to develop a better in-depth understanding of  their impact on HPC systems. Our discussions were lively and resulted in identifying  critical needs for research in the area of understanding I/O behavior. We  document those discussions in this report.},
	author = {Philip Carns and Julian Kunkel and Kathryn Mohror and Martin Schulz},
	doi = {https://doi.org/10.4230/DagRep.11.7.16},
	issn = {2192-5283},
	journal = {Dagstuhl Reports},
	month = {09},
	pages = {16-75},
	publisher = {Schloss Dagstuhl -- Leibniz-Zentrum für Informatik},
	title = {Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)},
	type = {article},
	url = {https://drops.dagstuhl.de/opus/volltexte/2021/15589},
	year = {2021},
}