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Panel: Beyond Exascale Computing

Panel: Beyond Exascale Computing. INTERNATIONAL ADVANCED RESEARCH WORKSHOP ON HIGH PERFORMANCE COMPUTING From Clouds and Big Data to Exascale and Beyond Cetraro (Italy) July 9 2014. Geoffrey Fox gcf@indiana.edu http://www.infomall.org School of Informatics and Computing

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Panel: Beyond Exascale Computing

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  1. Panel: Beyond Exascale Computing INTERNATIONAL ADVANCED RESEARCH WORKSHOPON HIGH PERFORMANCE COMPUTINGFrom Clouds and Big Data to Exascale and Beyond Cetraro (Italy) July 9 2014 Geoffrey Fox gcf@indiana.edu http://www.infomall.org School of Informatics and Computing Digital Science Center Indiana University Bloomington

  2. Comments I • Main comment: Machines are changing but not so clear problems are. Do we know what post exascale age problems are? • What are Big Data Problems: There is big data but current data analytics (machine learning) algorithms are classic parallel algorithms. adjective current maybe important -- may change • Note two interesting aspects of Google or Amazon or Facebook parallelisms • Back end data and users. both in billions or higher • Science is dominated by back end data • So I do expect differences between post exascale architectures for extreme scale science and commercial

  3. Comments II • Famous analytics libraries are R and Mahout. Not optimized for scalable processing. Need to change this • Surely this will develop a new set of algorithms and architecture challenges. Claim is algorithm change gave as much performance increase as hardware in simulations. Will this happen in analytics. what will be the needs of these complex new algorithms • Current analytics are pretty homogeneous e.g. "deep learning" is extreme. 11 billion highly uniform parameters which are weights of artificial neural network like the initial finite difference algorithms we started with in parallel simulation area. Simulations have introduced heterogeneous algorithms with adaptive meshes etc; this likely to occur in analytics and lead to change

  4. Comments III • Abstractions. Not so clear problems have structure proposed for post exascale machine architectures.We can reformulate them but is that because they want to be reformulated or because efficient use of hardware demands reformulation? • When Mother Nature evolves a blade of grass or a star, it is not put on a dataflow stack. it is updated in place • I personally find it painful to map problems into multiple programming models -- cudampi threads ....

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