Isum 2017
March 5 - 9  2018
Mérida, Yucatán, México
9th International Supercomputing Conference In Mexico
Creating an Insightful World through Supercomputing


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Magistral 2: Task-based programming with PyCOMPSs: towards the convergence of HPC and Big Data

Dr. Rosa María Badía

Rosa M. Badia holds a PhD on Computer Science (1994) from the Technical University of Catalonia (UPC).  She is the manager of the Workflows and Distributed Computing research group at the Barcelona Supercomputing Center (BSC).  She is also a Scientific Researcher from the Consejo Superior de Investigaciones Cientificas (CSIC). She was involved in teaching and research activities at the UPC from 1989 to 2008, where she was an Associated Professor since year 1997. From 1999 to 2005 she was involved in research and development activities at the European Center of Parallelism of Barcelona (CEPBA). 
Her current research interest are programming models for complex platforms (from multicore, GPUs to Grid/Cloud).  The group lead by Dr. Badia has been developing StarSs programming model for more than 10 years, with a high success in adoption by application developers. Currently the group focuses its efforts in PyCOMPSs/COMPSs, an instance of the programming model for distributed computing including Cloud, and its application to parallelize Big Data and Analytics. 

Dr Badia has published near 200 papers in  international conferences and journals in the topics of her research. She has been very active in projects funded by the European Commission and in contracts with industry (IBM and Intel).  She is currently participating in the following European funded projects: The Human Brain Project, the BioExcel CoE, NEXTGenIO, MUG, EUBra BIGSEA, TANGO, mf2C, the EXPERTISE ITN and it is a member of HiPEAC2 NoE.



Task-based programming models have proven to be a good alternative to traditional programming models for HPC. The generation of a task dependence graph, enables an out of order, asynchronous execution  of the tasks and the exploitation of the parallelism. These category of programming models is now very well accepted by the HPC applications' developers.
Furthermore, we believe that task-based programming models are a very good approach for programming workflows that combine traditional HPC workloads and analytics.  In this talk, we will present the PyCOMPSs/COMPSs programming model, its main programming and runtime features and how can be applied in current practices to enable convergence of HPC and Big Data.