Pre-exascale Architectures: OpenPOWER Performance and Usability Assessment for French Scientific Community
Exascale implies a major pre-requisite in terms of energy efficiency, as an improvement of an order of magnitude must be reached in order to stay within an acceptable envelope of 20 MW. To address this objective and to continue to sustain performance, HPC architectures have to become denser, embedding many-core processors (to several hundreds of computing cores) and/or become heterogeneous, that is, using graphic processors or FPGAs. These energy-saving constraints will also affect the underlying hardware architectures (e.g., memory and storage hierarchies, networks) as well as system software (runtime, resource managers, file systems, etc.) and programming models. While some of these architectures, such as hybrid machines, have existed for a number of years and occupy noticeable ranks in the TOP 500 list, they are still limited to a small number of scientific domains and, moreover, require significant porting effort. However, recent developments of new paradigms (especially around OpenMP and OpenACC) make these architectures much more accessible to programmers. In order to make the most of these breakthrough upcoming technologies, GENCI and its partners have set up a technology watch group and lead collaborations with vendors, relying on HPC experts and early adopted HPC solutions. The two main objectives are providing guidance and prepare the scientific communities to challenges of exascale architectures.
The work performed on the OpenPOWER platform, one of the targeted platform for exascale, is described in this paper.
KeywordsOpenPOWER assessment Technological watch OpenMP OpenACC Benchmarks Usability Programmability
GENCI thanks all its partners within the project for their support as well as IBM and nVIDIA experts and all the developers that contributed to the work performed on this platform.
- 2.Grasseau, G., Chamont, D., Beaudette, F., Bianchini, L., Davignon, O., Mastrolorenzo, L., Ochando, C., Paganini, P., Strebler, T.: Matrix element method for high performance computing platforms. In: Journal of Physics: Conference Series, vol. 664, Bristol, Institute of Physics Publishing (2015). 092009Google Scholar
- 4.Parnaudeau, P., Suzuki, A., Sac-Epee, J.M.: GPS: an efficient & spectrally accurate code for computing gross-pitaevskii equation. In: Research Posters Session, ISC-2015, Germany, 12–16 July 2015Google Scholar
- 5.Lavallée, P.-F., de Verdière, G.C., Wautelet, P., Lecas, D., Dupays, J.-M.: Porting and optimizing hydro to new platforms and programming paradigms - lessons learntGoogle Scholar
- 7.Brun, E., Chauveau, S., Malvagi, F.: Patmos: a prototype Monte Carlo transport code to test high performance architectures. In: M&C 2017 - International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering, Jeju, Korea, 16–20 April 2017Google Scholar
- 10.Intel software development emulator. https://software.intel.com/en-us/articles/intel-software-development-emulator. Accessed 26 Apr 2017
- 12.Agullo, E., Buttari, A., Guermouche, A., Lopez, F.: Task-based multifrontal QR solver for GPU-accelerated multicore architectures. In: HiPC, pp. 54–63. IEEE Computer Society (2015)Google Scholar