Modeling and simulation using high-performance computing are playing an increasingly important role in decision making and prediction. For time-critical emergency decision support applications, such as influenza modeling and severe weather prediction, late results may be useless. A specialized infrastructure is needed to provide computational resources quickly. This paper describes the architecture and implementation of SPRUCE, a system for supporting urgent computing on both traditional supercomputers and distributed computing Grids. Currently deployed on the TeraGrid, SPRUCE provides users with "right-of-way tokens" that can be activated from a Web-based portal or Web service invocation in the event of an urgent computing need. Tokens are transferrable and can be restricted to specific resource sets and priority levels. Once a session is activated, job submissions may request elevated priority. Based on local policy, computing resources can respond, for example, by preempting active jobs or raising the job's priority in the queue. This paper also explores the strengths and weaknesses of the SPRUCE architecture and token-based activation for urgent computing applications.
SUMMARYThis work targets the emerging use of software component technology for high-performance scientific parallel and distributed computing. While component software engineering will benefit the construction of complex science applications, its use presents several challenges to performance measurement, analysis, and optimization. The performance of a component application depends on the interaction (possibly nonlinear) of the composed component set. Furthermore, a component is a 'binary unit of composition' and the only information users have is the interface the component provides to the outside world. A performance engineering methodology and development approach is presented to address evaluation and optimization issues in high-performance component environments. We describe a prototype implementation of a performance measurement infrastructure for the Common Component Architecture (CCA) system. A case study demonstrating the use of this technology for integrated measurement, monitoring, and optimization in CCA component-based applications is given.
Abstract-Networks are an effective abstraction for representing real systems. Consequently, network science is increasingly used in academia and industry to solve problems in many fields. Computations that determine structure properties and dynamical behaviors of networks are useful because they give insights into the characteristics of real systems. We introduce a newly built and deployed cyberinfrastructure for network science (CINET) that performs such computations, with the following features: (i) it offers realistic networks from the literature and various random and deterministic network generators; (ii) it provides many algorithmic modules and measures to study and characterize networks; (iii) it is designed for efficient execution of complex algorithms on distributed high performance computers so that they scale to large networks; and (iv) it is hosted with web interfaces so that those without direct access to high performance computing resources and those who are not computing experts can still reap the system benefits. It is a combination of application design and cyberinfrastructure that makes these features possible. To our knowledge, these capabilities collectively make CINET novel. We describe the system and illustrative use cases, with a focus on the CINET user.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.