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 publisherís 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