Hiromichi Kobashi

Multi-scale computational modelling in biology and physiology

Abstract Recent advances in biotechnology and the availability of ever more powerful computers have led to the formulation of increasingly complex models at all levels of biology. One of the main aims of systems biology is to couple these together to produce integrated models across multiple spatial scales and physical processes. In this review, we formulate a definition of multi-scale in terms of levels of biological organisation and describe the types of model that are found at each level. Key issues that arise in trying to formulate and solve multi-scale and multi-physics models are considered and examples of how these issues have been addressed are given for two of the more mature fields in computational biology: the molecular dynamics of ion channels and cardiac modelling. As even more complex models are developed over the coming few years, it will be necessary to develop new methods to model them (in particular in coupling across the interface between stochastic and deterministic processes) and new techniques will be required to compute their solutions efficiently on massively parallel computers. We outline how we envisage these developments occurring.

A meta Problem Solving Environment (PSE)

In this paper, we introduce a new framework called PSE Park for constructing a Problem Solving Environment (PSE); it enables us to construct PSEs easily. PSE Park outputs PSEs depending on users demand/input. In this sense, PSE Park is a kind of PSE for PSE, and helps users to construct PSEs. PSE Park consists of four engines: PIPE server, core, registration engine, and console. A PSE designed and constructed in PSE Park consists of several cores, which are functions of a PSE. The PIPE server manages the cores on the basis of the core map, which expresses the flow of the cores for a specific PSE. The output of each core is retrieved and merged by the PIPE server. All outputs of the cores are saved and easily reused. The cores are independent of programming languages because each core is executed individually as a process in PSE Park. They are registered by using the registration engine, and users access the engines via the console. All data including the core itself, definitions related to the core, the core map, results, and so on are stored in a distributed key-value store on the cloud computing environment. PSE Park retrieves the data by using a key name that can identify individual data uniquely. We applied PSE Park to develop the job execution PSE and the PSE for partial differential equation (PDE)-based problems. The job execution PSE helps Finite Difference Time Domain (FDTD) simulation execution. This PSE outputs the simulation results of the electric field. PDE-based PSE supports some simulation steps. Seven cores were used to construct this example PSE. By using this PSE, users can execute a PDE-based simulation and obtain a detailed document about the simulation. We believe that the concept of PSE Park, i.e., a framework for PSE development, presents a meaningful new direction for problem solving environments.

Cerise: an RDF store with adaptive data reallocation

The Resource Description Framework (RDF) standard from W3C is an excellent format to store data with a dynamic schema. Thus far, organisations from both public and private sectors have been publishing their data in RDF format. There are two distinct challenges of managing RDF data: flexible schema suggests that one has to deal with data relations that are dynamic and constantly changing; there is no fixed schema meaning that it is difficult to optimise for a particular type of queries. This paper describes Cerise, a distributed and adaptive RDF data store that adapts the underlying storage and query execution according to the history of queries. Cerise co-locates (on the same data segment) data that are accessed frequently together to reduce overall disk and network latency. Evaluation results show that Cerise is able to outperform state-of-the-art production ready RDF databases.

A Distributed Linkage Method for a Large Amount of Event Data

In this paper, we propose a new distributed linkage method for the great amount of event data. This method is used for event-data processing such as system behavior visualization, which can diagnose and provide information about complex distributed IT system behavior from communication messages. In this method, we use two techniques: probe and data distribution by ¿source¿. The probe links event data based on the linkage model, which expresses the transaction of communication messages. In distributed computing environment, the counts of probe communication are very important for scalability. In our method, event data are allocated based on their ¿source¿ information because this can reduce the counts of probe communication. However, this approach is hard to link effectively in case of Gap model. Gap model means that it has a lack of the relation between message and next message. For Gap model, our method divides linkage processing based on the gap point. Each linkage processing runs concurrency and merged individually. We evaluate our method from the view-points of the throughput related with the load and the number of event processing servers. By using this method, the event-data processing can perform highly in the distributed computing environment. Experimental data demonstrated the viability of our propose method.

Grid Service Platform: Design and Implementation of Grid Middleware for Telecom Carriers

We have developed the Grid Service Platform (GSP), which is a grid middleware for telecom carriers. GSP can support not only non-interactive batch style services, but also interactive real-time services. Moreover, GSP attains autonomous resource sharing between services based on the priority of each service. We conducted a field trial of GSP on a testbed that consisted of three sites in Japan and France. Two different types of services were implemented on GSP: a video conferencing service (interactive) and a batch queuing service (non-interactive). As a result, the performance of both services were simultaneously enhanced. The session capacity of the video conferencing service was improved by up to 30%, while the total execution time of batch jobs was reduced by 12%.