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Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. This paper explores cloud computing for large-scale data-intensive scientific applications. In this paper, we present a web-based distributed volunteer computing platform to enable large scale hydrological simulations and model runs in an open and integrated environment.Įxploring Cloud Computing for Large-scale Scientific Applications A relational database system is utilized for managing data connections and queue management for the distributed computing nodes. The framework distributes the model simulation to thousands of nodes in small spatial and computational sizes. Using a web-based system allows users to start volunteering their computational resources within seconds without installing any software. Users will easily enable their websites for visitors to volunteer sharing their computer resources to contribute running advanced hydrological models and simulations. Using a client-side scripting language like JavaScript, we have developed an open distributed computing framework that makes it easy for researchers to write their own hydrologic models, and run them on volunteer computers. Recent developments in web technologies and standards allow client-side scripting languages to run at speeds close to native application, and utilize the power of Graphics Processing Units (GPU). It discusses the cost considerations leading to the selection of educational television channels rather than telephone lines for…Ī Web-based Distributed Voluntary Computing Platform for Large Scale Hydrological Computationsĭistributed volunteer computing can enable researchers and scientist to form large parallel computing environments to utilize the computing power of the millions of computers on the Internet, and use them towards running large scale environmental simulations and models to serve the common good of local communities and the world. This paper describes the design and implementation of a large scale computer terminal output controller which supervises the transfer of information from a Control Data 6400 Computer to a PLATO IV data network. In this review we present a brief history of neuromorphic engineering then focus on some of the principal current large-scale projects, their main features, how their approaches are complementary and distinct, their advantages and drawbacks, and highlight the sorts of capabilities that each can deliver to neural modellers.Ī Large Scale Computer Terminal Output Controller.ĮRIC Educational Resources Information Center These large-scale projects are associated with major new funding initiatives for brain-related research, creating a sense that the time and circumstances are right for progress in our understanding of information processing in the brain.
Recently, a number of large-scale neuromorphic projects have emerged, taking the approach to unprecedented scales and capabilities. This early work influenced others to carry developments forward, and advances in VLSI technology supported steady growth in the scale and capability of neuromorphic devices. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the late 1980s. Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. Large-scale neuromorphic computing systems
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