Tuning Application in a Multi-cluster Environment

Abstract. The joining of geographically distributed heterogeneous clusters of workstations through the Internet can be a simple and effective approach to speed up a parallel application execution. This paper describes a methodology to migrate a parallel application from a single-cluster to a collection of clusters, guaranteeing a minimum level of efficiency. This methodology is applied to a parallel scientific application to use three geographically scattered clusters located in Argentina, Brazil and Spain. Experimental results prove that the speedup and efficiency estimations provided by this methodology are more than 90% precision. Without the tuning process of the application a 45% of the maximum speedup is obtained whereas a 94% of that maximum speedup is attained when a tuning process is applied. In both cases efficiency is over 90%

EFFICIENT IMPLEMENTATION OF BRANCH-AND-BOUND METHOD ON DESKTOP GRIDS

The Berkeley Open Infrastructure for Network Computing (BOINC) is an opensource middleware system for volunteer and desktop grid computing. In this paper we propose BNBTEST, a BOINC version of distributed branch and bound method. The crucial issues of distributed branch-and-bound method are traversing the search tree and loading balance. We developed subtaskspackaging method and three dierent subtasks' distribution strategies to solve these

Performance Modeling and Analysis of a Massively Parallel DIRECT— Part 1

Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidisciplinary design optimization applications. Several highdimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. Theoretical and experimental results are compared for two parallel clusters with different system scale and network connectivity. The present work aims at studying the performance sensitivity to important parameters for problem configurations, parallel schemes, and system settings. The performance metrics include the memory usage, load balancing, parallel efficiency, and scalability. An analytical bounding model is constructed to measure the load balancing performance under different schemes. Additionally, linear regression models are used to characterize two major overhead sources—interprocessor communication and processor idleness, and also applied to the isoefficiency functions in scalability analysis. For a variety of highdimensional problems and large scale systems, the massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the generalized design considerations and analysis techniques are beneficial for transforming many global search algorithms to become effective large scale parallel optimization tools

Scalable and Distributed Resource Management for Many-Core Systems

Many-core systems provide researchers with important new challenges, including the handling of very dynamic and hardly predictable computational loads. The large number of applications and cores causes scalability issues for centrally acting heuristics, which always must retain a global view of the entire system. Resource management itself can become a bottleneck which limits the achievable performance of the system. The focus of this work is to achieve scalability of resource management