eprintid: 10093606 rev_number: 12 eprint_status: archive userid: 608 dir: disk0/10/09/36/06 datestamp: 2020-03-18 11:38:13 lastmod: 2020-03-18 11:38:13 status_changed: 2020-03-18 11:38:13 type: article metadata_visibility: show creators_name: Alowayyed, S creators_name: Vassaux, M creators_name: Czaja, B creators_name: Coveney, PV creators_name: Hoekstra, AG title: Towards heterogeneous multi-scale computing on large scale parallel supercomputers ispublished: pub divisions: UCL divisions: A01 divisions: B04 divisions: C06 divisions: F56 keywords: multi-scale modelling, surrogate model, computational science, heterogeneous multiscale computing, high performance computing, exascale note: This version is the version of record. For information on re-use, please refer to the publisherÃs terms and conditions. abstract: New applications that can exploit emerging exascale computing resources efficiently, while providing meaningful scientific results, are eagerly anticipated. Multi-scale models, especially multi-scale applications, will assuredly run at the exascale. We have established that a class of multi-scale applications implementing the heterogeneous multi-scale model follows, a heterogeneous multi-scale computing (HMC) pattern, which typically features a macroscopic model synchronising numerous independent microscopic model simulations. Consequently, communication between microscopic simulations is limited. Furthermore, a surrogate model can often be introduced between macro-scale and micro-scale models to interpolate required data from previously computed micro-scale simulations, thereby substantially reducing the number of micro-scale simulations. Nonetheless, HMC applications, though versatile, remain constrained by load balancing issues. We discuss two main issues: the a priori unknown and variable execution time of microscopic simulations, and the dynamic number of micro-scale simulations required. We tackle execution time variability using a pilot job mechanism to handle internal queuing and multiple sub-model execution on large-scale supercomputers, together with a data-informed execution time prediction model. To dynamically select the number of micro-scale simulations, the HMC pattern automatically detects and identifies three surrogate model phases that help control the available and used core amount. After relevant phase detection and micro-scale simulation scheduling, any idle cores can be used for surrogate model update or for processor release back to the system. We demonstrate HMC performance by testing it on two representative multi-scale applications. We conclude that, considering the subtle interplay between the macroscale model, surrogate models and micro-scale simulations, HMC provides a promising path towards exascale for many multiscale applications. date: 2019-10 date_type: published official_url: http://dx.doi.org/10.14529/jsfi190402 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1772185 doi: 10.14529/jsfi190402 lyricists_name: Coveney, Peter lyricists_id: PCOVE58 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Supercomputing Frontiers and Innovations volume: 6 number: 4 pagerange: 20-43 citation: Alowayyed, S; Vassaux, M; Czaja, B; Coveney, PV; Hoekstra, AG; (2019) Towards heterogeneous multi-scale computing on large scale parallel supercomputers. Supercomputing Frontiers and Innovations , 6 (4) pp. 20-43. 10.14529/jsfi190402 <https://doi.org/10.14529/jsfi190402>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10093606/1/281-1608-2-PB%20%282%29.pdf