An optimization approach for agent-based computational models of biological development

Authored by Pablo Gonzalez-de-Aledo, Andrey Vladimirov, Marco Manca, Jerry Baugh, Ryo Asai, Marcus Kaiser, Roman Bauer

Date Published: 2018

DOI: 10.1016/j.advengsoft.2018.03.010

Sponsors: United Kingdom Engineering and Physical Sciences Research Council (EPSRC) United Kingdom Medical Research Council

Platforms: C++

Model Documentation: Other Narrative Pseudocode

Model Code URLs: https://github.com/pablo-aledo/intel-modern-code-challenge

Abstract

Current research in the field of computational biology often involves simulations on high-performance computer clusters. It is crucial that the code of such simulations is efficient and correctly reflects the model specifications. In this paper, we present an optimization strategy for agent-based simulations of biological dynamics using Intel Xeon Phi coprocessors, demonstrated by a prize-winning entry of the ``Intel Modern Code Developer Challenge{''} competition. These optimizations allow simulating various biological mechanisms, in particular the simulation of millions of cells, their proliferation, movements and interactions in 3D space. Overall, our results demonstrate a powerful approach to implement and conduct very detailed and large-scale computational simulations for biological research. We also highlight the main difficulties faced when developing such optimizations, in particular the assessment of the simulation accuracy, the dependencies between different optimization techniques and counter-intuitive effects in the speed of the optimized solution. The overall speedup of 595 x shows a good parallel scalability.

Tags

Simulation Agent-based models Performance Parallel computing System Project Economy Numerical-simulation Optimizations Vectorization Coprocessor Biological Neurodevelopmental disorders