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, Bd. 76. Springer, August 2020. DOI: 10.1007/s11227-020-03402-y Onlineressource verfügbar

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): S. 115-123. IEEE  –  ISSN 1550-6533. Lyon, Frankreich, September 2018. DOI: 10.1109/CAHPC.2018.8645887 Onlineressource verfügbar

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) (Bd. 15): S. 329-338. ACSIS  –  ISSN 2300-596. Posen, Polen, September 2018. DOI: 10.15439/2018F169 Onlineressource verfügbar

Richter, M.; Rünger, G.: Symbolic matrix multiplication for multi-threaded sparse GEMM utilizing sparse matrix formats. In: International Conference on High Performance Computing & Simulation (HPCS 2018): S. 523-530. IEEE  –  ISBN 978-1-5386-7879-4. Orléans, Frankreich, Juli 2018. DOI: 10.1109/HPCS.2018.00088 Onlineressource verfügbar