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The increasing amount of data being collected from simulations, instruments and sensors creates challenges for existing e-Science infrastructure. In particular, it requires new ways of storing, distributing and processing data in order to cope with both the volume and velocity of the data. The University of Queensland has recently designed and deployed MeDiCI, a data fabric that spans the metropolitan...
Relative debugging helps trace software errors by comparing two concurrent executions of a program - one code being a reference version and the other faulty. By locating data divergence between the runs, relative debugging is effective at finding coding errors when a program is scaled up to solve larger problem sizes or migrated from one platform to another. In this work, we envision potential changes...
Relative debugging traces software errors by comparing two executions of a program concurrently - one code being a reference version and the other faulty. Relative debugging is particularly effective when code is migrated from one platform to another, and this is of significant interest for hybrid computer architectures containing CPUs accelerators or coprocessors. In this paper we extend relative...
In this paper, we present a framework that enables scientists to steer computations executing over large-scale grid computing environments. By using computational steering, users can dynamically control their simulations or computations to reach expected results more efficiently. The framework supports steerable applications by introducing an asynchronous iterative MapReduce programming model that...
The MapReduce programming model allows users to easily develop distributed applications in data centers. However, many applications cannot be exactly expressed with MapReduce due to their specific characteristics. For instance, genetic algorithms (GAs) naturally fit into an iterative style. That does not follow the two phase pattern of MapReduce. This paper presents an extension to the MapReduce model...
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