Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Cloud computing offers massive scalability and elasticity required by many scientific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new oppor-tunities for application developers. This paper investigates how workflow sys-tems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to integrate Big Data processing with workflows and science gateways work well in different cloud infrastructures and application scenarios, and can be used to create massively parallel applications for scientific analysis of Big Data

Triple Indexing: An Efficient Technique for Fast Phrase Query Evaluation

Phrase query evaluation is an important task of every search engine. Optimizing the query evaluation time for phrase queries is the biggest threat for the current search engine. Usually, phrase queries are a hassle for standard indexing techniques. This is generally because, merging the posting lists and checking the word ordering takes a lot of time. This paper proposes a new technique called Triple Indexing to index web documents which optimizes query evaluation time for phrase queries by reducing the time for merging the posting lists and checking the word ordering. In addition, a proper procedure has been put forward for document ranking using an extended vector space model. The 4 Universities dataset and Industry Sector dataset of Carnegie Mellon University has been used for experimental purpose and it has been found that using the proposed method with a modern machine, the query time for phrase queries is reduced by almost 50 percent, compared to a standard inverted index

MPI-LiFE: Designing High-Performance Linear Fascicle Evaluation of Brain Connectome with MPI

In this paper, we combine high-performance com- puting science with computational neuroscience methods to show how to speed-up cutting edge methods for mapping and evaluation of the large-scale network of brain connections. More specifically, we use a recent factorization method of the Linear Fascicle Evaluation model (i.e., LiFE [1], [2]) that allows for statistical evaluation of brain connectomes. The method called ENCODE [3], [4] uses a Sparse Tucker Decomposition approach to represent the LiFE model. We show that we can implement the optimization step of the ENCODE method using MPI and OpenMP programming paradigms. Our approach involves the parallelization of the multiplication step of the ENCODE method. We model our design theoretically and demonstrate empirically that the design can be used to identify optimal configurations for the LiFE model optimization via ENCODE method on different hardware platforms. In addition, we co-design the MPI runtime with the LiFE model to achieve profound speed-ups. Extensive evaluation of our designs on multiple clusters corroborate our theoretical model. We show that on a single node on TACC Stampede2, we can achieve speed-ups of up to 8.7x as compared to the original approach.Fil: Gugnani, Shashank. Ohio State University; Estados UnidosFil: Lu, Xiaoyi. Ohio State University; Estados UnidosFil: Pestilli, Franco. Indiana University; Estados UnidosFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Panda, Dhabaleswar K.. Ohio State University; Estados UnidosIEEE 24th International Conference on High Performance ComputingJaipurIndiaInstitute of Electrical and Electronics Engineer