Grid Enabled Neurosurgical Imaging Using Simulation

http://wiki.realitygrid.org/wiki/GENIUS

Introduction

The GENIUS project aims to model large scale patient specific cerebral blood flow in clinically relevant time frames

Objectives:• To study cerebral blood flow using patient-specific image-based models.• To provide insights into the cerebral blood flow & anomalies.• To develop tools and policies by means of which users can better exploit

the ability to reserve and co-reserve HPC resources.• To develop interfaces which permit users to easily deploy and monitor

simulations across multiple computational resources.• To visualize and steer the results of distributed simulations in real time

HemeLB

Efficient fluid solver for modelling brain bloodflow called HemeLB:

•Uses the lattice-Boltzmann method•Efficient algorithms for sparse geometries•Topology-aware graph growing partitioning technique•Optimized inter- and intra-machine communications•Full checkpoint capabilities.

Getting the Patient Specific Data

20482 x 682 cubic voxels,res: 0.469 mm

5122 pixels x 100 slices,res: 0.468752 mm x 0.8 mm

trilinearinterpolation

Our graphical-editing tool

Data is generated by MRA scanners at the National Hospital for Neurosurgery and Neurology

Cross-site Runs with MPI-gGENIUS has been designed to run across multiple machines using MPI-g• Some problems won’t fit on a single machine, and require

the RAM/processors of multiple machines on the grid.• MPI-g allows for jobs to be turned around faster by using

small numbers of processors on several machines -

essential for clinician • HemeLB performs well on cross site runs, and makes use

of overlapping communication in MPI-g

HemeLB/MPI-g Requires Co-Allocation

• We can reserve multiple resources for specified time periods

• Co-allocation is useful for meta-computing jobs like HemeLB, viz and for workflow applications.

• We use HARC - Highly Available Robust Co-scheduler (developed by Jon Maclaren at LSU).

Slide courtesy Jon Maclaren

HARC• HARC provides a secure co-allocation service

– Multiple Acceptors are used– Works well provided a majority of Acceptors stay alive– Paxos Commit keeps everything in sync– Gives the (distributed) service high availability– Deployment of 7 acceptors --> Mean Time To Failure ~ years– Transport-level security using X.509 certificates

• HARC is a good platform on which to build portals/other services– XML over HTTPS - simplerthan SOAP services– Easy to interoperate with– Very easy to use with the Java Client API

Real Time Visualisation and Steering

• A way to let HemeLB know the parameters to be steered --> we use the RealityGrid steering system to steer the input data on the fly.

• One aim is to do all this for distributed (cross-site) simulations

• For medical applications, need may be urgent

Application Hosting Environment

• Need to utilize resources from globally distributed grids– Administratively distinct– Running different middleware stacks

• Wrestling with middleware can't be a limiting step for scientists

• Need tools to hide complexity of underlying grids

PDA/Cellphone Visualisation and Steering

• User interaction/workflow: not necessarily 9:00am – 5:00pm,from the ‘desk’ environment

• RealityGrid: a tool for modelling and simulating very large or complex condensed matter structures…

• Human Factors issues key• Large-scale applications, scheduling

when resources become available, ‘around the clock’ computation…

• AHE application launching built in to client

AHE - HARC integration

• Users can co-reserve resources from the AHE GUI client using HARC

• When submitting a job, users can either run their jobs in normal queues, or use one of their reservations

• AHE passes reservation through to the Globus GRAM

• AHE client uses HARC Java client API to manage reservations

AHE ReG & WS GRAM Integration

• For steerable applications, AHE server starts up a ReG Steering Web Service (SWS) for the application, & sets the end point reference in the job’s environment

• AHE can submit jobs to GridSAM (described in JSDL) or WS GRAM (described in JDD) by applying a XSL Transform to the job specific WS-ResourceProperties document

• AHE --> WS GRAM can launch single or multisite MPIg jobs

Conclusions

• We have developed computationally efficient tools to manipulate, simulate and visualize patient specific vascular systems

• Goal is to better comprehend blood flow behavior of normal and anomalous cerebral systems and provide patient specific models for clinical guidance in surgical operations

• Application Hosting Environment extended to launch MPI-g cross site runs, integrate ReG steering and visualisation, as well as making HARC cross site reservations