Susumu Date

Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids

The distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large‐scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high‐energy physics. The analysis of brain‐activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large‐scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod‐G, Gridbus, and Globus. It describes the composition of the neuroscience (brain‐activity analysis) application as parameter‐sweep application and its on‐demand deployment on global Grids for distributed execution. The results of economic‐based scheduling of analysis jobs for three different optimizations scenarios on the world‐wide Grid testbed resources are presented along with their graphical visualization. Copyright

Deploying Scientific Applications to the PRAGMA Grid Testbed: Strategies and Lessons

Recent advances in grid infrastructure and middleware development have enabled various types of applications in science and engineering to be deployed on the grid. The characteristics of these applications and the diverse infrastructure and middleware solutions developed, utilized or adapted by PRAGMA member institutes are summarized. The applications include those for climate modeling, computational chemistry, bioinformatics and computational genomics, remote control of instruments, and distributed databases. Many of the applications are deployed to the PRAGMA grid testbed in routine basis experiments. Strategies for deploying applications without modifications, and those taking advantage of new programming models on the grid are explored and valuable lessons learned are reported. Comprehensive end to end solutions from PRAGMA member institutes that provide important grid middleware components and generalized models of integrating applications and instruments on the grid are also described.

Transient global amnesia (TGA) in an MEG study.

A patient who had experienced an attack of transient global amnesia (TGA) was examined using neurophysiological methods. Magnetoencephalography (MEG) was performed and the Wechsler Memory Scale-Revised (WMS-R) test was administered at 5 days and at more than a month after the TGA episode. MEG data on neuronal activity obtained while the patient was undertaking a working memory task and during rest were analyzed using the wavelet-crosscorrelation method, which reveals time-lag and information flow between related sites in the brain. The WMS-R memory scores showed dramatic improvement when the test was administered a month following the attack, although no significant changes were observed in EEG, MRI and SPECT data. The MEG study revealed that under a working memory load how the neuron works functionally and the information propagates assembly within the right hemisphere, and that these brain functions were not performed adequately shortly after the TGA attack.

Wavelet-Crosscorrelation Analysis: Non-Stationary Analysis of Neurophysiological Signals

Summary:Objective:Wavelet-crosscorrelation analysis is a new application of wavelet analysis used to show the propagation of epileptiform discharges and to localize the corresponding lesions. We have shown previously that this analysis can help predict brain conditions statistically (Mizuno-Matsumoto et al. 2002). Our objective was to assess whether wavelet-crosscorrelation analysis reveals the initiation and propagation of epileptiform activity in human patients.Methods:The data obtained from three patients with simple partial seizures (SPS) using whole-head magnetoencephalography (MEG) were analyzed by the wavelet-crosscorrelation method. Wavelet-crosscorrelation coefficients (WCC), the coherent structure of each possible pair of signals from 64 MEG channels for various periods, and the time lag (TL) in two related signals, were ascertained.Results:We clearly demonstrated both localization of the irritative zone and propagation of the epileptiform discharges.Conclusions:Wavelet-crosscorrelation analysis can help reveal and visualize the dynamic changes of brain conditions. The method of this analysis can compensate for other existing methods for the analysis of MEG, electroencephalography (EEG) or Elecotrocorticography (ECoG).Significance:Our proposed method suggests that revealing and visualizing the dynamic changes of brain conditions can help clinicians and even patients themselves better understand such conditions.

Hadoop framework: impact of data organization on performance

Hadoop, based on the popular MapReduce framework, is an open‐source distributed computing framework that has been gaining much popularity and usage. It aims to allow programmers to focus on building applications that deals with processing large amount of data, without having to handle other issues when performing parallel computations. However, tuning the performance of Hadoop applications is not an easy task due to the level of abstraction of the framework. In this paper, we present three case studies and some of the challenges and issues that are to be considered in performance tuning when running applications in Hadoop. The focus is mainly on the impact of input data on Hadoops performance and how they can be tuned. Copyright

A challenge towards next-generation research infrastructure for advanced life science

Recently, life scientists have expressed a strong need for computational power sufficient to complete their analyses within a realistic time as well as for a computational power capable of seamlessly retrieving biological data of interest from multiple and diverse bio-related databases for their research infrastructure. This need implies that life science strongly requires the benefits of advanced IT. In Japan, the Biogrid project has been promoted since 2002 toward the establishment of a next-generation research infrastructure for advanced life science. In this paper, the Biogrid strategy toward these ends is detailed along with the role and mission imposed on the Biogrid project. In addition, we present the current status of the development of the project as well as the future issues to be tackled.

Heterogeneous database federation using grid technology for drug discovery process

The rapid progress of biotechnology provides an increasing number of life science databases. These databases have been operated and managed individually on the Internet. Under such a circumstance, it is needed to develop an infrastructure that allows to share information contained in these databases and to conduct research collaboration. Grid technology is an emerging technology for seamless and loose integration of diverse resources distributed on the Internet. In order to achieve federation of the heterogeneous databases, we have developed a system for supporting a drug discovery process using Globus Toolkit3/OGSA-DAI. As an essential part of the system, we introduce a protein-compound interaction search based on a meta-data bridging protein and compound information with their interaction types; such as, inhibitor, agonist, antagonist, etc. The effectiveness of our system is demonstrated by searching for the candidate compounds interacting with the glucocorticoid receptor protein.

Architecture of a High-Speed MPI_Bcast Leveraging Software-Defined Network

Collective communication is of great importance in MPI because the execution time of an MPI program is affected by the communication performance it can gain. Particularly these days, when a cluster system composed of multiple computing nodes has become dominant as a large-scale computing system, the execution time of collective communication affects the total execution time of the MPI program. However, in many implementations of MPI, collective communication is developed to make use of unicast-based communication in a repeated and combined way, which may result in inefficient communication. In this paper, we explore the use of a Software-Defined Network, which was originally expected to help network administrators operate networks through central control in a software-programming manner, to accelerate MPI_Bcast, a basic collective communication used in MPI. The evaluation in this paper indicates that our prototyped SDN_MPI_Bcast is superior to MPI_Bcast in OpenMPI in communication performance. Also, the evaluation implies that SDN_MPI_Bcast is feasible.

Efficacy Analysis of a SDN-enhanced Resource Management System through NAS Parallel Benchmarks

In the field of social science, a variety of high-performance computing simulations such as the Monte Carlo simulation and the Multi-agent simulation must be efficiently performed to deal with social scientific big data. To facilitate social scientists in performing their own analysis against such big data, the information infrastructure for social science must be equipped with a core technology that efficiently and effectively leverages limited resources available on the information infrastructure. From such a perspective, a new type of job management technology, which treats not only computational resources such as the Central Processing Unit (CPU) and memory, but also network resources unlike traditional job management, is investigated in this paper. A cluster system with a fat-tree topology interconnect is conventional cluster architecture these days. For this investigation, the National Aeronautics Space Administration Advanced Supercomputing, USA (NAS) Parallel Benchmarks, which contain computation patterns often observed in social scientific simulations, are used to assess the efficacy of the resource allocation by our proposed job management technology on a cluster system with a fat-tree topology interconnect.

OPAL OP: AN EXTENSIBLE GRID-ENABLING WRAPPING TOOL FOR LEGACY APPLICATIONS

This paper describes a new approach (Opal OP: Opal Operation Provider) to wrap existing legacy applications as Grid services. In order to expose, with minimal effort, existing applications as Grid services, Opal OP provides a method for wrapping a legacy application as a program module, or as an operation provider. Traditional wrapping methods usually restrict the way to implement Grid services because these methods provide only a suite of interfaces necessary for using the wrapped application. The proposed Opal OP, on the other hand, doesn’t restrict the way to implement a Grid service from a legacy application. Opal OP is implemented as an operation provider that wraps the application and thus can be used as a module in the Grid service. Application developers can easily develop their own services where legacy applications are wrapped through the utilization of Opal OP. In this paper, we show some scientific applications including a bio-molecular simulation system developed as Grid services using Opal OP. The results show the usefulness and effectiveness of Opal OP.