R1 is a domain specific language widely used for data analysis by the statistics community as well as by researchers in finance, biology, social sciences, and many other disciplines. As R programs are linked to input data, the exponential growth of available data makes high-performance computing with R imperative. To ease the process of writing parallel programs in R, code transformation from a sequential program to a parallel version would bring much convenience to R users. In this paper, we present our work in semi-automatic parallelization of R codes with user-added OpenMP-style pragmas. While such pragmas are used at the frontend, we take advantage of multiple parallel backends with different R packages. We provide flexibility for importing parallelism with plug-in components, impose built-in MapReduce for data processing, and also maintain code reusability. We illustrate the advantage of the on-the-fly mechanisms which can lead to significant applications in data-centered parallel computing.

With many researchers now having easy access to supercomputers, coastal scientists are able to develop and run simulations that model the physical and ecological processes in ocean or nearshore areas in a distributed and collaborative environment. However, the increase in capacity of computational resources does not lead directly to a rapid improvement of the simulations themselves. Instead, it brings a new challenge that motivates scientists to fully utilize the huge amount of simulation data created in supercomputers, thus fostering advanced scientific research. Driven by the urgent need for in-depth investigations in Louisiana coastal areas, especially during hurricane seasons, a data center, which provides research communities with scientific data resources on demand, is imperative. In this paper, we present the design, implementation and use of such a simulation data archive for coastal science. The simulation data archive is capable of providing interfaces based on the requirements of user groups and its application incorporates multiple use cases. The enabling technology, as well as the challenges in the development of this simulation data archive, are also described in this paper.

Transfer learning is a new learning paradigm, in which, besides the training data for the targeted learning task, data that are related to the task (often under a different distribution) are also employed to help train a better learner. For example, out-dated data can be used as such related data. In this paper, we propose a new transfer learning framework for training neural network (NN) ensembles. The framework has two key features: 1) it uses the well-known negative correlation learning to train an ensemble of diverse neural networks from the related data, fully discovering the knowledge in the data; and 2) a penalized incremental learning scheme is used to adapt the neural networks obtained from negative correlation learning to the training data for the targeted learning task. The adaptation is guided by reference neural networks that measure the relatedness between the training and the related data. Experiments on benchmark data sets show that our framework can achieve classification accuracy competitive to existing ensemble transfer learning methods such as TrAdaBoost and TrBagg. We discuss some characteristics of our framework observed in the experiment and the scenarios under which the framework may have superior performance.