Eileen Kuehn

Karlsruhe Institute of Technology | KIT · Steinbuch Centre for Computing

With the emergence of the research field of Quantum Machine Learning, interest in finding advantageous real-world applications is growing as well. However, challenges concerning the number of available qubits on Noisy Intermediate Scale Quantum (NISQ) devices and accuracy losses due to hardware imperfections still remain and limit the applicability...

Training parameterized quantum circuits (PQCs) is a growing research area that has received a boost from the emergence of new hybrid quantum classical algorithms and Quantum Machine Learning (QML) to leverage the power of today’s quantum computers. However, a universal pipeline that guarantees good learning behavior has not yet been found, due to s...

The inclusion of opportunistic resources, for example from High Performance Computing (HPC) centers or cloud providers, is an important contribution to bridging the gap between existing resources and future needs by the LHC collaborations, especially for the HL-LHC era. However, the integration of these resources poses new challenges and often need...

Computing resource needs are expected to increase drastically in the future. The HEP experiments ATLAS and CMS foresee an increase of a factor of 5-10 in the volume of recorded data in the upcoming years. The current infrastructure, namely the WLCG, is not sufficient to meet the demands in terms of computing and storage resources. The usage of non...

Like any other scientific discipline, the High Performance Computing community suffers under the publish or perish paradigm. As a result, a significant portion of novel algorithm designs and hardware-optimized implementations never make it into production code but are instead abandoned once they served the purpose of yielding (another) publication....

To overcome the computing challenge in High Energy Physics available resources must be utilized as efficiently as possible. This targets algorithmic challenges in the workflows itself but also the scheduling of jobs to compute resources. To enable the best possible scheduling, job schedulers require accurate information about resource consumption o...

The current experiments in high energy physics (HEP) have a huge data rate. To convert the measured data, an enormous number of computing resources is needed and will further increase with upgraded and newer experiments. To fulfill the ever-growing demand the allocation of additional, potentially only temporary available non-HEP dedicated resources...

Increased operational effectiveness and the dynamic integration of only temporarily available compute resources (opportunistic resources) becomes more and more important in the next decade, due to the scarcity of resources for future high energy physics experiments as well as the desired integration of cloud and high performance computing resources...

To satisfy future computing demands of the Worldwide LHC Computing Grid (WLCG), opportunistic usage of third-party resources is a promising approach. While the means to make such resources compatible with WLCG requirements are largely satisfied by virtual machines and containers technologies, strategies to acquire and disband many resources from ma...

Current and future end-user analyses and workflows in High Energy Physics demand the processing of growing amounts of data. This plays a major role when looking at the demands in the context of the High-Luminosity-LHC. In order to keep the processing time and turn-around cycles as low as possible analysis clusters optimized with respect to these de...

In this position paper we argue for implementing an alternative peer review process for scientific computing contributions that promotes high quality scientific software developments as fully‐recognized conference submission. The idea is based on leveraging the code reviewers' feedback on scientific software contributions to community software deve...

High throughput and short turnaround cycles are core requirements for efficient processing of data-intense end-user analyses in High Energy Physics (HEP). Together with the tremendously increasing amount of data to be processed, this leads to enormous challenges for HEP storage systems, networks and the data distribution to computing resources for...

The GridKa Tier 1 data and computing center hosts a significant share of WLCG processing resources. Providing these resources to all major LHC and other VOs requires efficient, scalable and reliable cluster management. To satisfy this, GridKa has recently migrated its batch resources from CREAM-CE and PBS to ARC-CE and HTCondor. This contribution d...

Demand for computing resources in high energy physics (HEP) shows a highly dynamic behavior, while the provided resources by the Worldwide LHC Computing Grid (WLCG) remains static. It has become evident that opportunistic resources such as High Performance Computing (HPC) centers and commercial clouds are well suited to cover peak loads. However, t...

The heavily increasing amount of data produced by current experiments in high energy particle physics challenge both end users and providers of computing resources. The boosted data rates and the complexity of analyses require huge datasets being processed in short turnaround cycles. Usually, data storages and computing farms are deployed by differ...

As results of the excellent LHC performance in 2016, more data than expected has been recorded leading to a higher demand for computing resources. It is already foreseeable that for the current and upcoming run periods a flat computing budget and the expected technology advance will not be sufficient to meet the future requirements. This results in...

With the increasing data volume of LHC Run2, user analyses are evolving towards increasing data throughput. This evolution translates to higher requirements for efficiency and scalability of the underlying analysis infrastructure. We approach this issue with a new middleware to optimise data access: a layer of coordinated caches transparently provi...

To enable data locality, we have developed an approach of adding coordinated caches to existing compute clusters. Since the data stored locally is volatile and selected dynamically, only a fraction of local storage space is required. Our approach allows to freely select the degree at which data locality is provided. It may be used to work in conjun...

For data centres it is increasingly important to monitor the network usage, and learn from network usage patterns. Especially configuration issues or misbehaving batch jobs preventing a smooth operation need to be detected as early as possible. At the GridKa data and computing centre we therefore operate a tool BPNetMon for monitoring traffic data...

With the second run period of the LHC, high energy physics collaborations will have to face increasing computing infrastructural needs. Opportunistic resources are expected to absorb many computationally expensive tasks, such as Monte Carlo event simulation. This leaves dedicated HEP infrastructure with an increased load of analysis tasks that in t...

Recent developments in high energy physics (HEP) including multi-core jobs and multi-core pilots require data centres to gain a deep understanding of the system to monitor, design, and upgrade computing clusters. Networking is a critical component. Especially the increased usage of data federations, for example in diskless computing centres or as a...

Modern data processing increasingly relies on data locality for performance and scalability, whereas the common HEP approaches aim for uniform resource pools with minimal locality, recently even across site boundaries. To combine advantages of both, the High- Performance Data Analysis (HPDA) Tier 3 concept opportunistically establishes data localit...

With the introduction of federated data access to the workflows of WLCG, it is becoming increasingly important for data centers to understand specific data flows regarding storage element accesses, firewall configurations, as well as the scheduling of batch jobs themselves. As existing batch system monitoring and related system monitoring tools do...

This article presents two different approaches to visualise information from culture, media and creative industries by using RFID based tracking and identification. Besides the required RFID backend, the paper also introduces the information system built on top of the backend. The first approach is based on passive RFID whereas the second uses acti...

This paper presents two different approaches to visualise information from culture and creative industries by using RFID based tracking and identification as well as Wi-Fi for the communication between the different components. Besides the required RFID backend, the paper also introduces a multi media information system built on top of the backend....

This paper presents two different approaches to visualise information from culture, media and creative industries by using RFID based tracking and identification. Besides the required RFID backend, the paper also introduces the information system built on top of the backend. The first approach is based on passive RFID whereas the second uses active...

This paper presents two different approaches to visualise information from culture, media and creative industries by using RFID based tracking and identification. Besides the required RFID backend, the paper also introduces the information system built on top of the backend. The first approach is based on passive RFID whereas the second uses active...

This paper describes the design and implementation of a location and situation based pervasive mobile adventure game named Sportix. The prototype uses different types of sensor data – including 3D acceleration data and XPS – to determine the current position and activity of the player. Depending on the firm classification, data and quests are retri...

This paper describes the design and implementation of location and situation based adventure games. The prototype uses different types of sensor data - including 3D acceleration data and XPS - to determine the current positions and activities of the players. Depending on the continuous classification, data and quests are retrieved accordingly from...

In this paper, we describe the design and implementation of a location and situation based pervasive mobile adventure game named Sportix. The prototype uses different kinds of sensor data - including 3D acceleration data and XPS - to determine the current position and activity of the player. Depending on the firm classification, data and quests are...