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We explore the use of asynchronous many-task (AMT) programming models for the implementation of in situ analysis towards the goal of maximizing programmer productivity and overall performance on next generation platforms. We describe how a broad class of statistics algorithms can be transformed from a traditional single-programm multiple-data (SPMD) implementation to an AMT implementation, demonstrating...
Increased parallelism and use of heterogeneous computing resources is now an established trend in High Performance Computing (HPC), a trend that, looking forward to Exascale, seems bound to intensify. Despite the evolution of hardware over the past decade, the programming paradigm of choice was invariably derived from Coarse Grain Parallelism with explicit data movements. We argue that message passing...
Cloud computing drives the design and development of diverse programming models for massive data processing. the transformer programming framework aims to facilitate the building of diverse data-parallel programming models. transformer has two layers: a common runtime system and a model-specific system. using transformer, the authors show how to implement three programming models: dryad-like data...
A model management workflow typically consists of a number of interdependent model-management tasks such as model validation, transformation, merging, comparison and text generation. As such workflows are becoming more and more complex, errors in individual tasks are likely to be propagated to subsequent tasks and eventually compromise the operation of the entire workflow. Therefore, building confidence...
The size of unified modeling language (UML) models used in practice is very large and ranges up to hundreds and thousands of classes. Querying of these models is used to support their quality assessment by information filtering and aggregating. For both, human cognition and automated analysis, there is a need for fast querying. In this context performance of model queries becomes an important issue...
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