The NeuroGrid Project

Economic and On Demand "Brain Activity Analysis" on the World Wide Grid Using Nimrod-G and Gridbus Technologies


The lack of computational power within an organization for analyzing scientific data, and the distribution of knowledge (by scientists) and technologies (advanced scientific devices) are two major problems commonly observed in scientific disciplines. One such scientific discipline is brain science. The analysis of brain activity data gathered from the MEG (Magnetoencephalography) instrument is an important research topic in brain science since it helps doctors in identifying symptom of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and this exhaustive analysis needs large-scale processing resources. The emerging Grid technologies that enable the sharing, selection, and aggregation of geographically distributed resources can help in solving these problems. However, application development, resource management and scheduling in Grid environments is a complex undertaking.

We have developed a MEG data analysis system by leveraging Grid technologies, primarily Nimrod-G and Gridbus. The wavelet analysis program has been parameterised using the Nimrod-G parameter specification language. This application is enabled for distributed processing on the Grid with with minimal software engineering cost and development time. An application specific meta-job processing plug-in module has been developed to enable the composition of a set of MEG analysis jobs as a single coarse-grain processing job.

Furthermore, the Nimrod-G grid broker supports users’ quality-of-service (QoS) requirements driven brain activity analysis application scheduling on the Grid. In this system, we attempted to reduce analysis time (deadline) and cost (budget) and to seamlessly integrate resources (computational, data, and MEG instrument). Our evaluation results show that the system is highly efficient in reducing the analysis time and cost. The results demonstrate that grid technology is effective and promising for real-life medical and scientific problems.

SC 2002 Conference HPC Chellenge Entry

In the SC 2002 HPC challenge, we access 0.9 GB of brain activity data collected using the 64-sensors MEG instrument (located in Osaka, Japan) for one hour duration and perform on-demand brain activity analysis on globally distributed computers. This problem generates 7257600 analysis jobs and expected to take 102 days, on a commodity computer with a PentiumIII/500MHz processor and a 256MB memory. When such analysis is performed using our Grid brokering system, users/doctors will be able to steer analysis activity depending on QoS requirements (deadline, budget, optimization preference) and priorities. We demonstrate adaptive and dynamic selection of resources at runtime depending on their availability, capability, cost, data location, and users QoS requirements for brain activity analysis. In this HPC challenge, we will make use of the World-Wide Grid testbed resources located in Australia, Japan, Singapore, Europe (Germany, Italy, Czech Republic, UK), and USA.


Computational Resources



Related Early Work using MPI (Explicit Parallelisation of Application)