Yingwen Song

Grid enablement and performance evaluation for simulators of nanoscale measurements of material properties

We have enabled an MPI-based parallel program which is developed by our group to simulate nanoscale measurements of materials properties for a grid computing environment. The performance of this program has been evaluated based on three kinds of MPI middleware, MPICH-G2, GridMPI, and PACX-MPI. The speedup of this program on the grid is comparable with that obtained on supercomputers when CPU number is less than 200, and PACX-MPI gives the best performance and reliability among the three kinds of middleware in our study

A novel PPGA-based clustering analysis method for business cycle indicator selection

A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator selection. In the proposed method, the category of each indicator is coded by real numbers, and some illegal chromosomes are repaired by the identification and restoration of empty class. Two mutation operators, namely the discrete random mutation operator and the optimal direction mutation operator, are designed to balance the local convergence speed and the global convergence performance, which are then combined with migration strategy and insertion strategy. For the purpose of verification and illustration, the proposed method is compared with the K-means clustering algorithm and the standard genetic algorithms via a numerical simulation experiment. The experimental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis algorithm. Meanwhile, the proposed clustering analysis algorithm is also applied to select the business cycle indicators to examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly select some leading indicators, coincident indicators, and lagging indicators to reflect the business cycle, which is extremely operational for some macro economy administrative managers and business decision-makers.

Dynamic Resource Allocation Based on On-the-Fly Reservation

Resource reservation is a vital issue for grid-enabled programs to execute smoothly. Instead of using metascheduling solutions to request grid resources statically, we are developing a tiny C++ library to reserve on-demand computing resources based on Globus Toolkit, local schedulers such as Maui, and SQLite database. The interface class is named CReservation in the library. At the time of preparing this paper, the only implementation available is for Maui scheduler. With such implementation classes, one can request, release, and monitor reservations of computing resources dynamically. To verify the simplicity, usability, and stability of current library, we have performed an in-depth case study with a grid enabled hybrid MD/QM (Molecular Dynamics/Quantum Mechanical) multi-scale simulation program. Our test shows that this library provides an easy and effective approach to manage resource reservations from within grid-enabled applications.

Performance study of load balancing strategies for simulators of nanoscale measurements of material properties

We have compared various load balancing strategies for the program which was developed by our group to simulate nanoscale measurements of material properties. Among the strategies we have examined, static block distributions, static cyclic distributions, self-adapting block distributions, dynamic load balancing, and hybrid load balancing, the hybrid approach has been proven to give the best performance for the program on Hitachi SR8000.

The Grid Enablement and Sustainable Simulation of Multiscale Physics Applications

The understanding of H diffusion in materials is pivotal to designing suitable processes. Though a nudged elastic band (NEB)+molecular dynamics (MD)/quantum mechanics (QM) algorithm has been developed to simulate H diffusion in materials by our group, it is often not computationally feasible for large-scale models on a conventional single system. We thus gridify the NEB+MD/QM algorithm on the top of an integrated framework developed by our group. A two days simulation on H diffusion in alumina has been successfully carried out over a Trans-Pacific Grid infrastructure consisting of supercomputers provided by TeraGrid and AIST. In this paper, we describe the NEB+MD/QM algorithm, briefly introduce the framework middleware, present the grid enablement work, and report the techniques to achieve fault-tolerance and load-balance for sustainable simulation. We believe our experience is of benefit to both middleware developers and application users.

GRPLib: A Web Service Based Framework Supporting Sustainable Execution of Large-Scale and Long-Time Grid Applications

To ensure large-scale and long-time (LSLT) applications to run smoothly in a dynamic and heterogeneous grid environment, we have designed and implemented a WSRF-based framework with which users can reserve resources and request on-demand computing resources. The framework can be architecturely divided into three tiers: the tier providing client-side reservation and allocation APIs, the tier for reservation brokerage and resource allocation, and the tier for backend services. The reservation API is implemented for making and releasing a reservation, as well as for showing available reservations. The allocation API is implemented to request, to check, and to release a resource in a convenient way. The middle tier is designed to hide the complexity of the underlying grid infrastructure, and implemented to provide several allocation algorithms. One of the main backend services is Maui-based reservation service at present. A portal to facilitate the resource management is also available. In this paper, we present the API specification, the architecture, and the implementation of this framework. We also show a detailed experimental example.

Web Service-Based Dynamic Resource and Job Managements for Large-Scale and Long-Time Grid Computations

In order to guarantee large-scale and long-time applications to execute smoothly in a dynamic and heterogeneous grid environment, we have designed and implemented a framework by which users can reserve and allocate on- demand resources, and manage their jobs from within grid- enabled applications. This framework is based on a three- tier architecture that consists of client API, centralized broker of resource and job managements, and backend services on resource reservation and job scheduling. The reservation service is currently based on Maui, and the job scheduling service is DRMAA (Distributed Resource Management Application API) compliant. In this paper, we present the API functionality, the architecture, and the implementation of this framework.