Hofmann, M.; Kiesel, R.; Kramer, R.; Rünger, G.; Schaller, T.: Performance Comparison of Parallel Sorting Algorithms for Data-intensive Particle Simulations using OpenCL. To appear in: International Conference on High Performance Computing & Simulation (HPCS 2020): pp. . IEEE  –  ISSN . Barcelona, Spain, 2021.

Kalinnik, N.; Kiesel, R.; Rauber, T.; Richter, M.; Rünger, G.: A performance- and energy-oriented extended tuning process for time-step-based scientific applications. In: The Journal of Supercomputing, vol. 76. Springer, August 2020. DOI: 10.1007/s11227-020-03402-y Online resource available

Kiesel, R.; Rünger, G.: Performance and Energy Evaluation of Parallel Particle Simulation Algorithms for Different Input Particle Data. In: Position Papers of the 2019 Federated Conference on Computer Science and Information Systems (FedCSIS 2019), 12th Workshop on Computer Aspects of Numerical Algorithms (CANA'19) (vol. 19): pp. 31-37. Leipzig, Germany, September 2019. DOI: 10.15439/2019F344 Online resource available

Hofmann, M.; Kiesel, R.; Leichsenring, D.; Rünger, G.: A Hybrid CPU/GPU Implementation of Computationally Intensive Particle Simulations Using OpenCL. In: Proceedings of the 17th IEEE International Symposium On Parallel And Distributed Computing (ISPDC 2018): pp. 9-16. IEEE  –  ISBN 978-1-5386-5330-2. Geneva, Switzerland, June 2018. DOI: 10.1109/ISPDC2018.2018.00011 Online resource available

Hofmann, M.; Kiesel, R.; Rünger, G.: Energy and Performance Analysis of Parallel Particle Solvers from the ScaFaCoS Library. In: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering (ICPE 2018): pp. 88-95. ACM  –  ISBN 978-1-4503-5095-2. Berlin, Germany, April 2018. DOI: 10.1145/3184407.3184409 Online resource available

Kalinnik, N.; Kiesel, R.; Rauber, T.; Richter, M.; Rünger, G.: Exploring Self-Adaptivity towards Performance and Energy for Time-stepping Methods. In: Proceedings of the 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2018): pp. 115-123. IEEE  –  ISSN 1550-6533. Lyon, France, September 2018. DOI: 10.1109/CAHPC.2018.8645887 Online resource available

Kalinnik, N.; Kiesel, R.; Rauber, T.; Richter, M.; Rünger, G.: On the Autotuning Potential of Time-stepping methods from Scientific Computing. In: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems (FedCSIS 2018), 11th Workshop on Computer Aspects of Numerical Algorithms (CANA'18) (vol. 15): pp. 329-338. ACSIS  –  ISSN 2300-596. Poznań, Poland, September 2018. DOI: 10.15439/2018F169 Online resource available