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Forum39-Gabriel-Antoniu PDF

51 Pages·2017·23.6 MB·English
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Storage-Based Convergence Between HPC and Big Data Pierre Matri*, Alexandru Costan✝, Gabriel Antoniu◇, Jesús Montes*, María S. Pérez* * Universidad Politécnica de Madrid, Madrid, Spain — ✝ INSA Rennes / IRISA, Rennes, France — ◇ Inria Rennes Bretagne-Atlantique, Rennes, France ORAP Forum, Paris, March, 2017 Credits: Jack Dongarra 1. Accelerating delivery of a capable exascale computing system that integrates hardware and software capability to deliver approximately 100 times the performance of current 10 petaflop systems across a range of applications representing government needs. 2. Increasing coherence between the technology base used for modeling and simulation and that used for data analytic computing. 3. Establishing, over the next 15 years, a viable path forward for future HPC systems even after the limits of current semiconductor technology are reached (the "post- Moore's Law era"). 4. Increasing the capacity and capability of an enduring national HPC ecosystem by employing a holistic approach that addresses relevant factors such as networking technology, workflow, downward scaling, foundational algorithms and software, accessibility, and workforce development. 5. Developing an enduring public-private collaboration to ensure that the benefits of the research and development advances are, to the greatest extent, shared between the United States Government and industrial and academic sectors. BDEC Workshop, Charleston, SC, USA, April 29-May 1, 2013 BDEC Workshop, Fukuoka, Japan, February 26-28, 2014 BDEC Workshop, Barcelona, Spain, January 28-30, 2015 BDEC Workshop, Frankfurt, June 16-17, 2016 Credits: Jack Dongarra Credits: Dan Reed Stepping Stones: 
 Towards EC/BD Convergence Sharing the same resources •  Resource management methods need to evolve so that BD and EC can share resources Convergence towards « stepping stones » •  Challenges and demonstrations •  Software representing an entire system that can be used for BDEC •  HPC features available in the cloud (HPC) •  Cloud” features available on HPC platforms (availability, predictable response time, etc.) Credits: Kate Keahey A Catalyst for Convergence: Data Science A Catalyst for Convergence: Data Science An Approach:
 The BigStorage H2020 Project

Storage-Based Convergence Between HPC and Big Data. Pierre Matri* . Týr uses a high-performance, sequentially-consistent transaction chain algorithm: WARP [1]. Combined Read-Write performance / Reader-Writer Isolation
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