There are many science applications that require scalable task-level parallelism, support for flexible execution and coupling of ensembles of simulations. Most high-performance system software and middleware, however, are designed to support the execution and optimization of single tasks. Motivated by the missing capabilities of these computing systems and the increasing importance of task-level parallelism, we introduce the Ensemble toolkit which has the following application development features: (i) abstractions that enable the expression of ensembles as primary entities, and (ii) support for ensemble-based execution patterns that capture the majority of application scenarios. Ensemble toolkit uses a scalable pilot-based runtime system that decouples workload execution and resource management details from the expression of the application, and enables the efficient and dynamic execution of ensembles on heterogeneous computing resources. We investigate three execution patterns and characterize the scalability and overhead of Ensemble toolkit for these patterns. We investigate scaling properties for up to O(1000)concurrent ensembles and O(1000) cores and find linear weak and strong scaling behaviour.

Pilot-Jobs support effective distributed resource utilization, and are arguably one of the most widely-used distributed computing abstractions - as measured by the number and types of applications that use them, as well as the number of production distributed cyberinfrastructures that support them. In spite of broad uptake, there does not exist a well-defined, unifying conceptual model of Pilot-Jobs which can be used to define, compare and contrast different implementations. Often Pilot-Job implementations are strongly coupled to the distributed cyber-infrastructure they were originally designed for. These factors present a barrier to extensibility and interoperability. This paper is an attempt to (i) provide a minimal but complete model (P*) of Pilot-Jobs, (ii) establish the generality of the P* Model by mapping various existing and well known Pilot-Job frameworks such as Condor and DIANE to P*, (iii) derive an interoperable and extensible API for the P* Model (Pilot-API), (iv) validate the implementation of the Pilot-API by concurrently using multiple distinct Pilot-Job frameworks on distinct production distributed cyberinfrastructures, and (v) apply the P* Model to Pilot-Data.

Pilot-Jobs have become one of the most successful abstractions in distributed computing. In spite of extensive uptake, there does not exist a well defined, unifying conceptual model of pilot-jobs which can be used to define, compare and contrast different implementations. This presents a barrier to extensibility and interoperability. This paper is an attempt to, (i) provide a minimal but complete model (P*) of pilot-jobs, (ii) establish the generality of the P* Model by mapping various existing and well known pilot-jobs frameworks such as Condor and DIANE to P*, (iii) demonstrate the interoperable and concurrent usage of distinct pilot-job frameworks on different production distributed cyberinfrastructures via the use of an extensible API for the P* Model (Pilot-API).

This paper demonstrates the use of appropriate programming abstractions - SAGA and cactus - that facilitate the development of applications for distributed infrastructure. SAGA provides a high-level programming interface to Grid- functionality; Cactus is an extensible, component based framework for scientific applications. We show how SAGA can be integrated with cactus to develop simple, useful and easily extensible applications that can be deployed on a wide variety of distributed infrastructure, independent of the details of the resources. Our model application can gather and analyze network performance data and migrate across heterogeneous resources. We outline the architecture of our application and discuss how it imparts important features required of eScience applications. As a proof-of-concept, we present details of the successful deployment of our application over distinct and heterogeneous Grids and present the network performance data gathered. We also discuss several interesting use cases for such an application - which can be used either as stand-alone network diagnostic agent, or in conjunction with more complex scientific applications.

SAGA is a high-level programming abstraction, which significantly facilitates the development and deployment of Grid-aware applications. The primary aim of this paper is to discuss how each of the three main components of the SAGA landscape - interface specification, specific implementation and the different adaptors for middleware distribution - facilitate application-level interoperability. We discuss SAGA in relation to the ongoing GIN Community Group efforts and show the consistency of the SAGA approach with the GIN Group efforts. We demonstrate how interoperability can be enabled by the use of SAGA, by discussing two simple, yet meaningful applications: in the first, SAGA enables applications to utilize interoperability and in the second example SAGA adaptors provide the basis for interoperability.