Abstract. Emerging challenges for scientific communities are to efficiently process big data obtained by experimentation and computational simulations. Supercomputing architectures are available to support scalable and high performant processing environment, but many of the existing algorithm implementations are still unable to cope with its architectural complexity. One approach is to have innovative technologies that effectively use these resources and also deal with geographically dispersed large datasets. Those technologies should be accessible in a way that data scientists who are running data intensive computations do not have to deal with technical intricacies of the underling execution system. Our work primarily focuses on providing data scientists with transparent access to these resources in order to easily analyze data. Impact of our work is given by describing how we enabled access to multiple high performance computing resources through an open standards-based middleware that takes advantage of a unified data management provided by the the Global Federated File System. Our architectural design and its associated implementation is validated by a usecase that requires massivley parallel DBSCAN outlier detection on a 3D point clouds dataset.Key words: UNICORE, Genesis II, statistical data mining, data processing, distributed file system, security, standards, parallel processing AMS subject classifications. 68M14 1.Introduction. An ever increasing number of datasets from scientific experimentation such as earth observatories or computational simulations generate an enormous amount of information for discovering useful knowledge. In order to analyze data, the area of statistical data mining provides useful methods and tools to extract and explore useful patterns or prediction models. The field of statistical data mining comes with intuitive methods to learn from data, using a wide variety of algorithms for clustering, classification and regression. Several implementations are available, for example, Matlab, R, Octave [3], or scikit-learn. Mostly, these tools offer serial implementation of the algorithms, which is quite challenging (i.e. insufficient memory, extremely long running times, etc.) for processing the volume of data having terabytes or petabytes of magnitude. Considering that amount, the resources running the data processing tools require large number of processors, as well as much more primary and secondary storage. Therefore, parallel tools and platforms such as Hadoop [15] implementing the map reduce paradigm [18] and selected massively parallel algorithm developments based on the MPI and OpenMP environments are commonly used.We observe mainly tools for (High Performance Computing) HPC and High Throuput Computing (HTC) paradigms evolving concurrently, but each supporting their own set of requirements. Scientific communities, either from biology, physics and medicine adopt more conservative approaches in order to retain their focus on scientific findings and as such traditional HPC environme...
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