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This report presents the architecture and the algorithms used in DIET (Distributed Interactive Engineering Toolbox), a hierarchical set of components to build Network Enabled Server applications in a Grid environment. This environment is built on top of different tools which are able to locate an appropriate server depending of the client's request, the(More)
The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer , or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully(More)
Mixed-parallelism, the combination of data-and task-parallelism, is a powerful way of increasing the scalability of entire classes of parallel applications on platforms comprising multiple compute clusters. While multi-cluster platforms are predominantly heterogeneous, previous work on mixed-parallel application scheduling targets only homogeneous(More)
Mixed-parallel applications can take advantage of large-scale computing platforms but scheduling them efficiently on such platforms is challenging. In this paper we compare the two main proposed approaches for solving this scheduling problem on a heterogeneous set of homogeneous clusters. We first modify previously proposed algorithms for both approaches(More)
Scheduling multiple applications on heterogeneous multi-clusters is challenging as the different applications have to compete for resources. A scheduler thus has to ensure a fair distribution of resources among the applications and prevent harmful selfish behaviors while still trying to minimize their respective completion time. In this paper we consider(More)
While most parallel task graphs scheduling research has been done in the context of single homogeneous clusters, heterogeneous platforms have become prevalent and are extremely attractive for deploying applications at unprecedented scales. In this paper we address the need for scheduling techniques for parallel task applications for heterogeneous clusters(More)
Many scientic applications are described through workow structures. Due to the increasing level of parallelism oered by modern computing infrastructures, workow applications now have to be composed not only of sequential programs, but also of parallel ones. Cloud platforms bring on-demand resource provisioning and pay-as-you-go payment charging. Then the(More)
Applications structured as parallel task graphs exhibit both data and task parallelism and arise in many domains. Scheduling these applications efficiently on parallel platforms has been a long-standing challenge. In the case of a single homogeneous platform, such as a cluster, results have been obtained both in theory, i.e., guaranteed algorithms, and, in(More)
—Simulation and modeling for performance prediction and profiling is essential for developing and maintaining HPC code that is expected to scale for next-generation exascale systems, and correctly modeling network behavior is essential for creating realistic simulations. In this article we describe an implementation of a flow-based hybrid network model that(More)
SUMMARY Analyzing and understanding the performance behavior of parallel applications on parallel computing platforms is a long-standing concern in the High Performance Computing community. When the targeted platforms are not available, simulation is a reasonable approach to obtain objective performance indicators and explore various hypothetical scenarios.(More)