Krzysztof Kurowski

EDBT 2026 Demo / reviewers in the wild / expert

dblp:68/1520 · DBLP profile ↗

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16ranked-venue papers

4as first author

4since 2021 · last 2026

0000-0002-4478-6119ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 13 · 3 first-author · 4 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-authorArtificial intelligence and machine learning · 1Software engineering, systems software and programming languages · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1

Year	Publication	Venue	Position
2026	Solving combinatorial optimization and machine learning problems on hybrid near-term quantum photonic computers abstract Quantum computers are increasingly being integrated into classical computing environments, particularly within supercomputing and data centers, where they serve as accelerators for solving a wide range of specific problems. Previous research has indicated that quantum computers have the potential to resolve optimization problems with theoretically lower computational complexity than classical algorithms. However, in practical scenarios, even minor instances of these problems often require substantial computational resources, enhanced quality quantum hardware, and the results may not always be optimal. In this paper, we highlight the importance of hybrid systems, combining quantum and classical computations to address growing problem sizes and computational demands. We focus on optimization problems using hardware and software-enhanced quantum computers operational within a novel quantum–classical hybrid setup, incorporating GPUs and photonic quantum computers. The study specifically addresses combinatorial optimization problems, such as the Max-Cut and the Job Shop Scheduling Problem, in addition to selected machine learning classification use cases. Notably, the quantum algorithm in Max-Cut optimization outperformed complete solution searches, especially for larger problem instances. For the Job-Shop Scheduling Problem, we made a significant advancement by successfully solving substantially larger instances compared to our previous previous work. Furthermore, selected hybrid neural networks incorporating quantum layers showed improved stability, though without a clear quality advantage over classical models. The paper also highlights the rapid progress and technological achievements in both hardware and software used in near-term photonic quantum computers, suggesting a promising future for quantum–classical hybrid systems in useful applications. Mateusz Slysz, Lukasz Grodzki, Piotr Rydlichowski, Dawid Siera, Krzysztof Kurowski, Grzegorz Waligóra, Jan Weglarz	Future Gener. Comput. Syst.	5
2026	Corrigendum to "Solving combinatorial optimization and machine learning problems on hybrid near-term quantum photonic computers" [Future Gener. Comput. Syst. 174 (2026) 107934] Mateusz Slysz, Lukasz Grodzki, Piotr Rydlichowski, Dawid Siera, Krzysztof Kurowski, Grzegorz Waligóra, Jan Weglarz	Future Gener. Comput. Syst.	5
2024	Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev	Future Gener. Comput. Syst.	57
2021	Energy and performance improvements in stencil computations on multi-node HPC systems with different network and communication topologies Milosz Ciznicki, Krzysztof Kurowski, Jan Weglarz	Future Gener. Comput. Syst.	2
2020	Energy efficiency and performance modeling of stencil applications on manycore and GPU computing resources abstract One of the most critical application areas for many operational High-Performance Computing systems worldwide is a numerical weather prediction. Such complex predictions and corresponding computing models have to solve a large number of Partial Differential Equations using stencil computations on structured grids within tight production schedules. A stencil kernel within simulations is often the most demanding computing part and naturally may impact the energy consumption of the whole HPC system. In this paper, we introduce new functional extensions developed for DCworms and GSSIM simulators to predict the energy efficiency of extreme-scale stencil applications on heterogeneous computing resources, in particular consisting of a large number of many-core and GPUs. New energy-efficiency metrics for modeling of heterogeneous computing resources have been successfully added to the simulators thanks to their pluggable and extensible architectures. Our recent improvements related to application-specific performance models in simulators help users to take into account many relevant application parameters, in particular, those related to detailed energy consumption on heterogeneous HPC resources. We show in this paper how to extract stencil application-specific parameters based on real experiments. Moreover, we demonstrate how those parameters can be applied for modeling and simulation experiments to evaluate the overall performance and energy consumption of stencil computations on manycore CPUs and GPUs. Finally, we discuss new DCworms simulator capabilities and demonstrate added-values of performance analysis tools. Krzysztof Kurowski, Milosz Ciznicki, Jan Weglarz	CCGRID	1
2019	Patterns for High Performance Multiscale Computing abstract We describe our Multiscale Computing Patterns software for High Performance Multiscale Computing. Following a short review of Multiscale Computing Patterns, this paper introduces the Multiscale Computing Patterns Software, which consists of description, optimisation and execution components. First, the description component translates the task graph, representing a multiscale simulation, to a particular type of multiscale computing pattern. Second, the optimisation component selects and applies algorithms to find the most suitable mapping between submodels and available HPC resources. Third, the execution component which a middleware layer maps submodels to the number and type of physical resources based on the suggestions emanating from the optimisation part together with infrastructure-specific metrics such as queueing time and resource availability. The main purpose of the Multiscale Computing Patterns software is to leverage the Multiscale Computing Patterns to simplify and automate the execution of complex multiscale simulations on high performance computers, and to provide both application-specific and pattern-specific performance optimisation. We test the performance and the resource usage for three multiscale models, which are expressed in terms of two Multiscale Computing Patterns. In doing so, we demonstrate how the software automates resource selection and load balancing, and delivers performance benefits from both the end-user and the HPC system level perspectives. Saad Alowayyed, Tomasz Piontek, James L. Suter, Olivier Hoenen, Derek Groen, Onnie Luk, Bartosz Bosak, Piotr Kopta, Krzysztof Kurowski, Oliver Perks, Keeran Brabazon, Vytautas Jancauskas, David Coster, Peter V. Coveney, Alfons G. Hoekstra	Future Gener. Comput. Syst.	9
2016	Development of Science Gateways Using QCG - Lessons Learned from the Deployment on Large Scale Distributed and HPC Infrastructures Tomasz Piontek, Bartosz Bosak, Milosz Ciznicki, Piotr Grabowski, Piotr Kopta, Michal Kulczewski, Dawid Szejnfeld, Krzysztof Kurowski	J. Grid Comput.	8
2015	Adaptation of fluid model EULAG to graphics processing unit architecture abstract Summary The goal of this study is to adapt the multiscale fluid solver EULerian or LAGrangian framewrok (EULAG) to future graphics processing units (GPU) platforms. The EULAG model has the proven record of successful applications, and excellent efficiency and scalability on conventional supercomputer architectures. Currently, the model is being implemented as the new dynamical core of the COSMO weather prediction framework. Within this study, two main modules of EULAG, namely the multidimensional positive definite advection transport algorithm (MPDATA) and the variational generalized conjugate residual, elliptic pressure solver Generalized Conjugate Residual (GCR) are analyzed and optimized. In this paper, a method is proposed, which ensures a comprehensive analysis of the resource consumption including registers, shared, and global memories. This method allows us to identify bottlenecks of the algorithm, including data transfers between host and global memory, global and shared memories, as well as GPU occupancy. We put the emphasis on providing a fixed memory access pattern, padding as well as organizing computation in the MPDATA algorithm. The testing and validation of the new GPU implementation have been carried out based on modeling decaying turbulence of a homogeneous incompressible fluid in a triply‐periodic cube. Simulations performed using the standard version of EULAG and its new GPU implementation give similar solutions. Preliminary results show a promising increase in terms of computational efficiency. Copyright © 2014 John Wiley & Sons, Ltd. Krzysztof Rojek, Milosz Ciznicki, Bogdan Rosa, Piotr Kopta, Michal Kulczewski, Krzysztof Kurowski, Zbigniew Pawel Piotrowski, Lukasz Szustak, Damian Karol Wójcik, Roman Wyrzykowski	Concurr. Comput. Pract. Exp.	6
2012	Challenges for Future Platforms, Services and Networked Applications Krzysztof Kurowski	CAiSE	1
2012	Distributed Infrastructure for Multiscale Computing abstract Today scientists and engineers are commonly faced with the challenge of modelling, predicting and controlling multiscale systems which cross scientific disciplines and where several processes acting at different scales coexist and interact. Such multidisciplinary multiscale models, when simulated in three dimensions, require large scale or even extreme scale computing capabilities. The MAPPER project is developing computational strategies, software and services to enable distributed multiscale simulations across disciplines, exploiting existing and evolving e-Infrastructure. The resulting multi-tiered software infrastructure, which we present in this paper, has as its aim the provision of a persistent, stable infrastructure that will support any computational scientist wishing to perform distributed, multiscale simulations. Stefan J. Zasada, Mariusz Mamonski, Derek Groen, Joris Borgdorff, Ilya Saverchenko, Tomasz Piontek, Krzysztof Kurowski, Peter V. Coveney	DS-RT	7
2012	Easy Development and Integration of Science Gateways with Vine Toolkit Piotr Dziubecki, Piotr Grabowski, Michal Krysinski, Tomasz Kuczynski, Krzysztof Kurowski, Dawid Szejnfeld	J. Grid Comput.	5
2007	Grid scheduling simulations with GSSIM abstract Grid simulation tools provide frameworks for simulating application scheduling in various Grid infrastructures. However, while experimenting with many existing tools, we have encountered two main shortcomings: (i) there are no tools for generating workloads, resources and events ; (ii) it is difficult and time consuming to model different Grid levels, i.e. resource brokers, and local level scheduling systems. In this paper we present the Grid Scheduling Simulator (GSSIM), a framework that addresses these shortcomings and provides an easy-to-use Grid scheduling framework for enabling simulations of a wide range of scheduling algorithms in multi-level, heterogeneous Grid infrastructures. In order to foster more collaboration in the community at large, GSSIM is complemented with a portal (http://www.gssim.org) that provides a repository of Grid scheduling algorithms, synthetic workloads and benchmarks for use with GSSIM. Krzysztof Kurowski, Jarek Nabrzyski, Ariel Oleksiak, Jan Weglarz	ICPADS	1
2007	Security and performance enhancements to OGSA-DAI for Grid data virtualization abstract Abstract In this paper we describe our work on enabling dynamic access control and secure management over federated data resources, such as relational or XML databases exposed to public network infrastructures via OGSA‐DAI middleware. We have proposed some extensions to the OGSA‐DAI architecture and successfully implemented new mechanisms enabling secure communication and distributed data integrity along with fine‐grain authorization and policy enforcement to minimize the complexity of the security right management. As a proof of concept some preliminary results of various performance tests of our solutions are also presented in this paper. We then analyze our achievements and describe future work and research. Copyright © 2007 John Wiley & Sons, Ltd. Marcin Adamski, Michal Kulczewski, Krzysztof Kurowski, Jarek Nabrzyski, Alastair C. Hume	Concurr. Comput. Pract. Exp.	3
2006	Context Sensitive Mobile Access to Grid Environments and VO Workspaces abstract This paper examines the problem of giving mobile users a possibility to access interoperability services based on ontologies and predefined concepts providing various meaningful objects and mechanisms to search, discover, invoke, compose and monitor appropriate grid-based services, applications as well as data sources in the Virtual Organization (VO) from any place in the world using mobile devices. Since mobiles as end devices are naturally used by humans to see a ’human-readable’ content, in our opinion, they should also allow them to specify ’human-readable’ queries instead of direct computer-interpretable queries to unknown remote service providers or databases. Based on the knowledge located in the interoperability space user’s requests from mobile devices can be mapped and forwarded automatically to appropriate remote interfaces to take advantages of various functionalities offered by the grid and service providers available today in the Internet. Therefore, more efficient interaction between the user and remote service provider(s) can be established. In our approach, the real ontology services are available from mobile devices via the gateway due to weaknesses of mobile devices and the complexity of the ontology layer within the grid infrastructure. In this paper we describe several grid-based services developed for mobile users as well as some core services which have been adopted to work with mobile devices. Roles of a mobile client, grid-services and gateway played in some novel scenarios of ontology usage are also described. Finally, in this paper we discuss issues related to obstacles and limitations that we have been facing while using the chosen J2ME technology, and the ways we are solving them. Piotr Grabowski, Krzysztof Kurowski, Jarek Nabrzyski, Michael Russell	MDM	2
2006	Workflow applications in GridLab and PROGRESS projects abstract Abstract In this paper we present our motivations and ideas for workflow management based on our experiences gained in two projects: GridLab and PROGRESS. In these projects we have been dealing with real use cases and end users' requirements for workflow management. Therefore, we were able to define and implement functional workflow extensions to the Grid(Lab) Resource Management System (GRMS). GRMS is a resource management system with a workflow engine that executes and manages jobs on remote Grid resources. One can submit to GRMS workflow experiments based on an XML workflow schema, defining flexible mechanisms for dynamic workflow control, including various types of precedence constraints, different locations of the final data products and executables, etc. All of these features allow end users to speed up remote workflow calculations and improve data management mechanisms. Copyright © 2005 John Wiley & Sons, Ltd. Michal Kosiedowski, Krzysztof Kurowski, Cezary Mazurek, Jarek Nabrzyski, Juliusz Pukacki	Concurr. Comput. Pract. Exp.	2
2001	User Preference Driven Multiobjective Resource Management in Grid Environments abstract Scientific and other grand challenge applications are a driving force for developing the computing infrastructure of the future. Their constantly increasing computational power requirements often cannot be met by available systems (F. Gagliardi, 2000). Such a situation has led to the emergence of new platform called "Computational Grid" (I. Foster and C. Kesselman, 1999). It seems very important to identify common and reusable components that are needed and can be used in a computational grid by different applications. Such components could be the Grid information services, security mechanisms, resource management, large data set management, etc. The paper sketches the issues of one of the most important components of the grid: the resource management and scheduling component. We present our work on a multiobjective resource management system. The novelty of the approach lies in its multiobjective nature. We present the Multi-Criteria Resource Broker (MC-Broker), whose scheduling mechanisms are driven by user preferences regarding various scheduling criteria, such as computation time and cost, communication time between distributed processes, level of load balancing and others. Krzysztof Kurowski, Jarek Nabrzyski, Juliusz Pukacki	CCGRID	1