This paper describes work in progress to develop a standard for interoperability ariiong high-petforniance scientific coniponents. This research sterns front growing recognition that the scientific coniniunity needs to better manage the coniplexity of ntultidisciplinuiy simulations and better address scalable petforniance issues on parallel and distributed architectures. Driving forces are the need for fast connections among components that perform numerically intensive work and for parallel collective interactions among cornponetits that use multiple processes or threads. This paper focuses on the areas we believe are niost crucial in this context, naiizely, an intetface definition language that supports scientipc abstractions for specifying coriiponent interfaces and a ports connection model for specifiing component interactions.

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Much experience has been gained with the protocols and mechanisms needed for discovery and allocation of remote computational resources. However, the preparation of a remote computer for use by a distributed application also requires the creation of an appropriate execution environment, which remains an ad hoc and often clumsy process. We propose here a codification of the interactions required to negotiate the creation of new execution environments. In brief, we model dynamic virtual environments (DVEs) as first-class entities in a distributed environment, with Grid service interfaces defined to negotiate creation, monitor properties, and manage lifetime. We also show how such DVEs can be implemented in a variety of technologiessandboxes, virtual machines, or simply Unix accounts-and evaluate costs associated with these different approaches. DVEs provide a basis for both customization of a remote computer to meet user needs and also enforcement of resource usage and security policies. They can also simplify the administration of virtual organizations (VOs), by allowing new environments to be created automatically, subject to local and VO policy. Thus, DVEs have the potential to relieve much of the current administrative burden involved in providing and using Grid resources.

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Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.