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Task-based runtime systems are an important branch of parallel programming research, since tasks decouple computation from the compute units, giving the runtime systems greater flexibility than a thread-based solution. This makes it easier to deal with the ever-increasing complexity of parallel architectures by providing a separation of concerns-the specification of parallelism is separated from the implementation of the parallel computations on a specific architecture. The Open Community Runtime is one such system, aimed at large-scale parallel systems. Unlike many other task-based runtime systems, the creators not only provided an implementation but there is also a comprehensive specification document. This has allowed us to create an independent implementation, called OCR-Vx. In this article, we present our experience of developing the runtime system, put our work in the context of the specification and the other implementations, and describe key lessons that we have learned during our work. We discuss the design and implementation issues of task-based runtime systems and applications including task synchronization and scheduling, data management, memory consistency, the relation between shared-memory and distributed-memory runtime systems, NUMA architectures, and heterogeneous systems. The article is aimed at audiences not familiar with OCR, since we believe these lessons could be valuable for developers working on other task-based runtime systems or designing new ones.

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The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

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We introduce the architecture of @neuLink, a service-oriented environment for biomedical knowledge discovery which has been developed in the course of EU Integrated Project @neurIST. The application integrates data from databases with information extracted from unstructured text sources. Moreover, @neuLink supports the analysis of primary biomolecular data associated with individual patients and thus enables the interpretation of molecular data inside a clinical research environment. Based on an assembly of data services, @neuLink interacts with the complex @neurIST grid infrastructure through a dedicated data access and data mediation service. Data types integrated by @neuLink are covering the entire span of biomolecular entities: from gene names in text to entries in EntrezGene; from mentions of drugs to Drugbank, from information on allelic variants in scientific literature to entries in dbSNP. The architecture of @neuLink allows easy integration of other webservice-based applications and thus the spectrum of analysis capabilities of @neuLink can be extended following the requirements of the users of the @neurIST system.

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Service-oriented Grid technologies are increasingly utilized for the realization of future biomedical IT infrastructures since they offer unprecedented opportunities for the integration of advanced analysis and simulation applications as well as distributed heterogeneous data sources and information systems. The European Union's @neurIST project is developing a Grid-based IT infrastructure for the management of all processes linked to research, diagnosis, and treatment development for complex and multifactorial diseases encompassing data repositories, computational analysis services, and information systems handling multiscale, multimodal information at distributed sites. This paper provides an overview of the @neurIST Grid middleware and outlines the infrastructure offered for the provision of advanced compute and data services to support computationally demanding modeling and simulation tasks and to access heterogeneous distributed data sources through semantic integration.

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