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Evolution of the HemeLB Parallel Simulation Environment for Human Brain Bloodflow

This overview poster summarizes the major advances made on the HemeLB code. HemeLB is a parallelized lattice-Boltzmann simulation code, which has been shown to scale near-linearly to up to 32768 cores. Recently, we managed to reach 153 million site updates per seconds using 49152 cores on the ARCHER supercomputer (see http://www.cresta-project.eu). In addition, it highlights two recent project, one about domain decomposition (which has been published in much greater detail in the proceedings of the EASC 2014 conference) and one ongoing project about validating HemeLB against clinical velocity measurements.

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Derek Groen, Miguel Bernabeu, Rupert Nash, Sebastian Schmieschek and Peter Coveney 
Cerebrovascular bloodflow Introducing HemeLB 
2008 
Evolution of the HemeLB bloodflow simulation environment 
First journal publication 
on HemeLB. 
2010 
Development of steering 
and visualization client. 
2009 
HemeLB run across sites 
Using MPI-g. 
2011 
Improved domain 
decomposition 
with ParMETIS. 
2013 
Multiscale 3D-1D coupling 
with Python Navier Stokes. 
2013 
Up to 50% faster 
Calculations with SSE. 
2014 
Better load balance using 
weighted decomposition. 
2012 
Support for 2nd order 
accurate wall conditions. 
2014 
First comparison tests with 
clinical data. 
2014 
HemeLB-Chaste coupling 
to simulate vascular 
remodelling processes. 
2013 
More stable model 
constructionwith CGAL. 
2014 
Gained support for 
implementing immersed 
boundary conditions. 
2012 
Performance prediction 
Model. 
2012 
Improved scalability 
With coalesced 
comms design pattern. 
2011 
Code reengineered for 
improved accuracy and 
stability. 
2014 
Support for PT-Scotch, 
Zoltan and ParMETIS in 
domain decomposition. 
Performance improvements 
Accuracy improvements 
Scientific advances 
New functionalities 
2013 
Framework for 
convenient property 
extraction 
Highlight: Comparison against clinical data 
We have compared flow predictions from HemeLB with clinical 
measurements. We have done this work in collaboration with Fergus 
Robertson and Hoskote Chandrashekar from UCL Hospital. 
● We obtained rotational angiography images of a middle cerebral 
artery, as well as velocity measurements (TCD) in 5 planes within 
this artery. 
● We imposed one plane as a velocity-based inlet in HemeLB, and 
ran the code to predict the velocities in the four other planes (we 
used pressure outlets). 
● See below for an overview of the geometry, and a comparison 
between the HemeLB flow predictions and the TCD measurements 
on the plane furthest away from the inlet. 
● We are currently working to repeat this exercise with a second 
patient, and to improve our comparison techniques. 
Highlight: Weighted Decomposition 
HemeLB models sparse vasculature geometries as a lattice of fluid 
sites. These geometries contain bulk sites, wall sites and in/outlet sites. 
Both wall and in/outlet sites are generally more expensive to compute 
than bulk sites, leading to load imbalance among different processes. 
To reduce this load imbalance, we assign weights to each lattice site 
(see right below) before we partition and distribute the domain among 
the processes. Using this approach resulted we have managed to 
reduce the calculation load imbalance by up to 85%. 
The performance of HemeLB has improved considerably over the past 7 years: 
Indeed, for smaller geometries we can now do production simulations (e.g., 5M time steps) 
in less than an hour. However, simulating a full Circle of Willis model on a relevant time 
scale (e.g., 25M time steps) will still take about a day. 
Circle of 
Willis 
run 
(first trial) 
Contributors Recommended reading 
Marco Mazzeo, Steven Manos, Gary Doctors – initial 
developers 
Rupert Nash, Hywel Carver, James Hetherington, Timm 
Krueger – Main developers during 2010-2013. 
Miguel Bernabeu, Derek Groen, Sebastian Schmieschek – 
current HemeLB developers. 
Dan Holmes – Colloids code 2012. 
Jiri Jaros, David Abou Chacra – Performance optimizations 
during 2013. 
Jens Nielsen – CGAL setup tool optimizations 2013. 
Gregor Matura, Fang Chen – pre- and post-processing 2014. 
Aditya Jitta – Comparison against clinical data, 2013. 
2013 
Comparison of different 
Rheology models. 
2012 
Comparison of different 
wall conditions. 
2013 
Support for 
Colloidal particles 
2010 
Python model construction 
tool developed. 
● Stroke is the main cause of about 1.1M 
deaths per year in Europe. 
● About 15% of these strokes are caused by 
bleeding in the brain, e.g. due to the rupture 
of brain aneurysms. 
● These brain aneurysms frequently reside 
in arteries branching from the Circle of 
Willis. 
● HemeLB is a feature-rich simulation environment for modelling blood flow in 
sparse geometries. 
● It relies on the lattice-Boltzmann method and is well suited for execution on 
large supercomputers. 
HemeLB consists of several key components: 
● The main simulation code, which can be coupled to other codes. 
● The setup tool, for constructing 3 dimensional geometries from segmented 
angiography scans. 
● A Python-based framework for constructing initial conditions and analyzing 
output data. 
● An interactive steering and visualization tool. 
● A Python-based automation environment for deploying and executing the code 
on remote machines. 
Circle of Willis with one diminshed artery. 
© Nevit Dilmen 
Simulating bloodflow 
Clinicians can conveniently measure blood 
pressure and flow velocities on patients at rest 
with limited resolution. 
Simulations allow us to estimate and predict 
flow properties in other regimes as well. These 
include: 
● Flow velocity estimates for patients during 
exercise and other forms of activity. 
● Wall stress estimations under all these 
conditions (e.g., wall shear stress). 
● Flow properties in specific locations within a 
geometry, e.g. velocities and stresses in an 
aneurysm sac. 
Both very high and very low wall shear stress 
have been associated with aneurysm 
formation and rupture. 
Sample visualization of a HemeLB simulation. 
1. Performance: JoCS, DOI: 10.1016/j.jocs.2013.03.002 
2. Weighted Decomposition: EASC 2014. Preprint available. 
3. Boundary Conditions: Phys. Rev. E 89, 023303 (2014). 
4. Clinical Validation: work in progress. Slides available. 
5. Multiscale: Interface Focus, DOI: 10.1098/ rsfs.2012.0087 
6. Retinal blood flow: Interface (submitted) arXiv:1311.1640. 
Access the source code at: 
http://ccs.chem.ucl.ac.uk/hemelb 
For requesting preprints, please send an e-mail to Derek Groen 
(djgroennl@gmail.com). 
2012 
Tools for automatic 
compilation and execution 
on remote machines. 
inlet @63mm. 
v plane @49mm. 
2014 
Prediction of vascular 
development in retinas. 
2020 SCIENCE 
www.2020science.net 
UKCOMES 
UK Consortium On Mesoscale 
Engineering Sciences 
Legend

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Evolution of the HemeLB Parallel Simulation Environment for Human Brain Bloodflow

  • 1. Derek Groen, Miguel Bernabeu, Rupert Nash, Sebastian Schmieschek and Peter Coveney Cerebrovascular bloodflow Introducing HemeLB 2008 Evolution of the HemeLB bloodflow simulation environment First journal publication on HemeLB. 2010 Development of steering and visualization client. 2009 HemeLB run across sites Using MPI-g. 2011 Improved domain decomposition with ParMETIS. 2013 Multiscale 3D-1D coupling with Python Navier Stokes. 2013 Up to 50% faster Calculations with SSE. 2014 Better load balance using weighted decomposition. 2012 Support for 2nd order accurate wall conditions. 2014 First comparison tests with clinical data. 2014 HemeLB-Chaste coupling to simulate vascular remodelling processes. 2013 More stable model constructionwith CGAL. 2014 Gained support for implementing immersed boundary conditions. 2012 Performance prediction Model. 2012 Improved scalability With coalesced comms design pattern. 2011 Code reengineered for improved accuracy and stability. 2014 Support for PT-Scotch, Zoltan and ParMETIS in domain decomposition. Performance improvements Accuracy improvements Scientific advances New functionalities 2013 Framework for convenient property extraction Highlight: Comparison against clinical data We have compared flow predictions from HemeLB with clinical measurements. We have done this work in collaboration with Fergus Robertson and Hoskote Chandrashekar from UCL Hospital. ● We obtained rotational angiography images of a middle cerebral artery, as well as velocity measurements (TCD) in 5 planes within this artery. ● We imposed one plane as a velocity-based inlet in HemeLB, and ran the code to predict the velocities in the four other planes (we used pressure outlets). ● See below for an overview of the geometry, and a comparison between the HemeLB flow predictions and the TCD measurements on the plane furthest away from the inlet. ● We are currently working to repeat this exercise with a second patient, and to improve our comparison techniques. Highlight: Weighted Decomposition HemeLB models sparse vasculature geometries as a lattice of fluid sites. These geometries contain bulk sites, wall sites and in/outlet sites. Both wall and in/outlet sites are generally more expensive to compute than bulk sites, leading to load imbalance among different processes. To reduce this load imbalance, we assign weights to each lattice site (see right below) before we partition and distribute the domain among the processes. Using this approach resulted we have managed to reduce the calculation load imbalance by up to 85%. The performance of HemeLB has improved considerably over the past 7 years: Indeed, for smaller geometries we can now do production simulations (e.g., 5M time steps) in less than an hour. However, simulating a full Circle of Willis model on a relevant time scale (e.g., 25M time steps) will still take about a day. Circle of Willis run (first trial) Contributors Recommended reading Marco Mazzeo, Steven Manos, Gary Doctors – initial developers Rupert Nash, Hywel Carver, James Hetherington, Timm Krueger – Main developers during 2010-2013. Miguel Bernabeu, Derek Groen, Sebastian Schmieschek – current HemeLB developers. Dan Holmes – Colloids code 2012. Jiri Jaros, David Abou Chacra – Performance optimizations during 2013. Jens Nielsen – CGAL setup tool optimizations 2013. Gregor Matura, Fang Chen – pre- and post-processing 2014. Aditya Jitta – Comparison against clinical data, 2013. 2013 Comparison of different Rheology models. 2012 Comparison of different wall conditions. 2013 Support for Colloidal particles 2010 Python model construction tool developed. ● Stroke is the main cause of about 1.1M deaths per year in Europe. ● About 15% of these strokes are caused by bleeding in the brain, e.g. due to the rupture of brain aneurysms. ● These brain aneurysms frequently reside in arteries branching from the Circle of Willis. ● HemeLB is a feature-rich simulation environment for modelling blood flow in sparse geometries. ● It relies on the lattice-Boltzmann method and is well suited for execution on large supercomputers. HemeLB consists of several key components: ● The main simulation code, which can be coupled to other codes. ● The setup tool, for constructing 3 dimensional geometries from segmented angiography scans. ● A Python-based framework for constructing initial conditions and analyzing output data. ● An interactive steering and visualization tool. ● A Python-based automation environment for deploying and executing the code on remote machines. Circle of Willis with one diminshed artery. © Nevit Dilmen Simulating bloodflow Clinicians can conveniently measure blood pressure and flow velocities on patients at rest with limited resolution. Simulations allow us to estimate and predict flow properties in other regimes as well. These include: ● Flow velocity estimates for patients during exercise and other forms of activity. ● Wall stress estimations under all these conditions (e.g., wall shear stress). ● Flow properties in specific locations within a geometry, e.g. velocities and stresses in an aneurysm sac. Both very high and very low wall shear stress have been associated with aneurysm formation and rupture. Sample visualization of a HemeLB simulation. 1. Performance: JoCS, DOI: 10.1016/j.jocs.2013.03.002 2. Weighted Decomposition: EASC 2014. Preprint available. 3. Boundary Conditions: Phys. Rev. E 89, 023303 (2014). 4. Clinical Validation: work in progress. Slides available. 5. Multiscale: Interface Focus, DOI: 10.1098/ rsfs.2012.0087 6. Retinal blood flow: Interface (submitted) arXiv:1311.1640. Access the source code at: http://ccs.chem.ucl.ac.uk/hemelb For requesting preprints, please send an e-mail to Derek Groen (djgroennl@gmail.com). 2012 Tools for automatic compilation and execution on remote machines. inlet @63mm. v plane @49mm. 2014 Prediction of vascular development in retinas. 2020 SCIENCE www.2020science.net UKCOMES UK Consortium On Mesoscale Engineering Sciences Legend