Practical Computer Science
Projects


Energy Efficient Computing

Finding ways to reduce the energy use for computing is one of today's challenges in computer science. There has been a tremendous effort in research towards energy efficient hardware which results in decreasing power consumption values of recent hardware and processors. These hardware developments have to be accompanied by a remodeling of sequential and parallel algorithms, parallel simulations codes, and corresponding implementations. Various parallel implementations may solve the same problem but may have different use of time and/or energy depending on how the implementation exploits the underlying hardware features. The research group Practical Computer Science at the University Chemnitz of Technology investigates several software aspects of energy efficiency, including energy models, energy metrics, energy measurements, scheduling with respect to energy efficiency, energy efficiency libraries, as well as energy savings through reprogramming, i.e. by vectorization.

Vectorization

  • Jakobs, T.: Optimierung der Energie-Effizienz für Algorithmen der Linearen Algebra durch SIMD-Programmierung und AVX-Vektorisierung, TU Chemnitz, Fakultät für Informatik, Doctoral thesis, 2022. Online resource available
  • Jakobs, T.; Naumann, B.; Rünger, G.: Performance and energy consumption of the SIMD Gram–Schmidt process for vector orthogonalization. In: The Journal of Supercomputing. Springer  –  ISSN 1573-0484, 2019. DOI: 10.1007/s11227-019-02839-0 Online resource available
  • Jakobs, T.; Rünger, G.: On the energy consumption of Load/Store AVX instructions. 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): pp. 319-327. IEEE. Poznan, Poland, September 2018. DOI: 10.15439/2018F28 Online resource available
  • Jakobs, T.; Rünger, G.: Examining Energy Efficiency of Vectorization Techniques Using a Gaussian Elimination. In: International Conference on High Performance Computing & Simulation (HPCS 2018): pp. 268-275. IEEE  –  ISBN 978-1-5386-7879-4. Orléans, France, July 2018. DOI: 10.1109/HPCS.2018.00054 Online resource available
  • Jakobs, T.; Hofmann, M.; Rünger, G.: Reducing the Power Consumption of Matrix Multiplications by Vectorization. In: Proceedings of the 19th IEEE International Conference on Computational Science and Engineering (CSE 2016): pp. 1-8. IEEE  –  ISBN 978-1-5090-3593-9. Paris, France, August 2016. DOI: 10.1109/CSE-EUC-DCABES.2016.187 Online resource available

Task-based programming and task scheduling

  • Rauber, T.; Rünger, G.: Comparison of Time and Energy Oriented Scheduling for Task-based programs. In: Proceedings of the 12th International Conference on Parallel Processing and Applied Mathematics (PPAM 2017) (Lecture Notes in Computer Science, vol. 10777): pp. 185-196. Springer  –  ISBN 978-3-319-78024-5. Lublin, Poland, September 2017 (published March 2018). DOI: 10.1007/978-3-319-78024-5_17 Online resource available
  • Rauber, T.; Rünger, G.: Tuning Energy Effort and Execution Time of Application Software. In: Proceedings of the 38th International Conference on Information Systems Architecture and Technology (ISAT 2017) (Advances in Intelligent Systems and Computing, vol. 656): pp. 239-251. Springer  –  ISBN 978-3-319-67228-1. Szklarska Poręba, Poland, September 2017. DOI: 10.1007/978-3-319-67229-8_22 Online resource available
  • Rauber, T.; Rünger, G.: Modeling and Analyzing the Energy Consumption of Fork-Join-based Task Parallel Programs. In: Concurrency and Computation: Practice and Experience, vol. 27, no. 1: pp. 211-236. John Wiley & Sons, Ltd.  –  ISSN 1532-0634, 2015. DOI: 10.1002/cpe.3219 Online resource available
  • Rauber, T.; Rünger, G.: Towards an Energy Model for Modular Parallel Scientific Applications. In: IEEE International Conference on Green Computing and Communications (GreenCom 2012): pp. 523-532. IEEE  –  ISBN 978-0-7695-4865-4. Besançon, France, 2012. DOI: 10.1109/GreenCom.2012.79 Online resource available
  • Rauber, T.; Rünger, G.: Analytical Modeling and Simulation of the Energy Consumption of Independent Tasks. In: Proceedings of the 2012 Winter Simulation Conference (WSC 2012)  –  ISBN 978-1-4673-4781-5. Berlin, Germany, 2012. Online resource available
  • Rauber, T.; Rünger, G.: Energy-aware Execution of Fork-Join-based Task Parallelism. In: 20th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS'12): pp. 231-240. IEEE  –  ISSN 1526-7539. Arlington, Virginia, USA, 2012. DOI: 10.1109/MASCOTS.2012.35 Online resource available
  • Rauber, T.; Rünger, G.: Modeling the Energy Consumption for Concurrent Executions of Parallel Tasks. In: Proceedings of the 14th Communications and Networking Simulation Symposium (CNS'11): pp. 11-18. Society for Computer Simulation International  –  ISBN 978-1-61782-837-9. Boston, MA, USA, 2011. Online resource available

Energy models and measurements

  • Rauber, T.; Rünger, G.: A Scheduling Selection Process for Energy-efficient Task Execution on DVFS Processors. In: Concurrency and Computation: Practice and Experience, vol. 31. Wiley  –  ISSN 1532-0626, Juni 2019. DOI: 10.1002/cpe.5043 Online resource available
  • Rauber, T.; Rünger, G.; Stachowski, M.: Model-based optimization of the energy efficiency of multi-threaded applications. In: Sustainable Computing: Informatics and Systems, vol. 22: pp. 44-61. Elsevier  –  ISSN 2210-5379, 2019. DOI: 10.1016/j.suscom.2019.01.022 Online resource available
  • Rauber, T.; Rünger, G.; Stachowski, M.: Performance and Energy Metrics for Multi-threaded Applications on DVFS Processors. In: Sustainable Computing: Informatics and Systems, vol. 17: pp. 55-68. Elsevier  –  ISSN 2210-5379, 2017 (published March 2018). DOI: 10.1016/j.suscom.2017.10.015 Online resource available
  • Rauber, T.; Rünger, G.; Stachowski, M.: Model-based Optimization of the Energy Efficiency of Multi-threaded Applications. In: Proceedings of the 8th International Green and Sustainable Computing Conference (IGSC 2017): pp. 1-6. IEEE  –  ISBN 978-1-5386-3470-7. Orlando, USA, October 2017 (published 2018). DOI: 10.1109/IGCC.2017.8323578 Online resource available
  • Rauber, T.; Rünger, G.; Stachowski, M.: Towards New Metrics for Appraising Performance and Energy Efficiency of Parallel Scientific Programs. In: Proceedings of the 13th IEEE International Conference on Green Computing and Communication (GreenCom-2017): pp. 466-474. IEEE  –  ISBN 978-1-5386-3066-2. Exeter, United Kingdom, June 2017 (published 2018). DOI: 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.75 Online resource available

Application specific

  • 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
  • Rauber, T.; Rünger, G.: Energy and Performance Improvement of Parallel ODE Solvers by Application-specific Program Transformations. In: Proceedings of the 19th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC-18), IEEE International Parallel and Distributed Processing Symposium Workshops: pp. 967-976. IEEE  –  ISBN 978-1-5386-5555-9. Vancouver, Canada, May 2018. DOI: 10.1109/IPDPSW.2018.00151 Online resource available
  • Rauber, T.; Rünger, G.: How do loop transformations affect the energy consumption of Runge-Kutta methods?. In: Proceedings of the 26th Euromicro International Conference on Parallel, Distributed, and Network-based Processing (PDP 2018): pp. 499-507. IEEE  –  ISBN 978-1-5386-4975-6. Cambridge, United Kingdom, March 2018. DOI: 10.1109/PDP2018.2018.00085 Online resource available
  • Jakobs, T.; Lang, J.; Rünger, G.; Stöcker, P.: Tuning linear algebra for energy efficiency on multicore machines by adapting the ATLAS library. In: Future Generation Computer Systems, vol. 82: pp. 555-564. Elsevier  –  ISSN 0167-739X, 2017 (published May 2018). DOI: 10.1016/j.future.2017.03.009 Online resource available
  • Lang, J.: Data-aware tuning of scientific applications with model-based autotuning. In: Concurrency and Computation: Practice and Experience, vol. 29, no. 4: pp. 1-15. John Wiley and Sons, Ltd  –  ISSN 1532-0634, 2017. DOI: 10.1002/cpe.3885 Online resource available
  • Carretero, J.; Distefano, S.; Petcu, D.; Pop, D.; Rauber, T.; Rünger, G.; Singh, D. E.: Energy-efficient Algorithms for Ultrascale Systems. In: Supercomputing Frontiers and Innovations, vol. 2, no. 2: pp. 77-104  –  ISSN 2313-8734, 2015. DOI: 10.14529/jsfi150205 Online resource available
  • Lang, J.: Energie- und Ausführungszeitmodelle zur effizienten Ausführung wissenschaftlicher Simulationen, TU Chemnitz, Fakultät für Informatik, Doctoral thesis, 2015. Online resource available
  • Lang, J.; Rünger, G.; Stöcker, P.: Towards energy-efficient linear algebra with an ATLAS library tuned for energy consumption. In: 2015 International Conference on High Performance Computing & Simulation (HPCS 2015): pp. 63-70. IEEE, 2015. DOI: 10.1109/HPCSim.2015.7237022 Online resource available
  • Lang, J.: Grüner verschlüsseln – Messung des Energieverbrauchs von Verschlüsselungsalgorithmen. In: Team der Chemnitzer Linux-Tage, (Eds.): Chemnitzer Linux-Tage 2014 – Tagungsband: pp. 25–32. Universitätsverlag Chemnitz  –  ISBN 978-3-944640-08-2. Chemnitz, 2014. Online resource available
  • Lang, J.; Rünger, G.: An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration. In: Journal of Parallel and Distributed Computing, vol. 74, no. 9: pp. 2884-2897. Elsevier  –  ISSN 0743-7315, 2014. DOI: 10.1016/j.jpdc.2014.06.001 Online resource available
  • Lang, J.; Rünger, G.: Measuring and modelling energy consumption for a CPU/GPU conjugate gradient method in an adaptive FEM. In: Proc. of the High-Level Programming for Heterogeneous and Hierarchical Parallel Systems workshop at HiPEAC conference 2014. Wien, Österreich, 2014.
  • Lang, J.; Rünger, G.: High-Resolution Power Profiling of GPU Functions Using Low-Resolution Measurement. In: Wolf, F.; Mohr, B.; an Mey, D. (Eds.): Euro-Par 2013 Parallel Processing (LNCS, vol. 8097): pp. 801–812. Springer  –  ISBN 978-3-642-40046-9, 2013. DOI: 10.1007/978-3-642-40047-6_80 Online resource available