A Scalable, Multi-Access Flash Store for Big Data Analytics

Sang Woo Jun, Ming Liu and Arvind, MIT CSAIL Modern data-driven analytics often deal with datasets that do not fit in the main memory of the processing system. In such a scenario, the limiting factor in performance is usually the transportation … Continue reading

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Accelerated Linear Algebra on Big Data

By Jack Dongarra, University of Tennessee Knoxville and Innovative Computing Laboratory Often with Big Data come massive amounts of computations. For example, gene correlations may be analyzed with the Singular Value Decomposition as it is done in the GenMark benchmark. The SVD algorithm is a robust method that … Continue reading

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MapD: A Way to Map Big Data Faster

How a Fortuitous “Hack” Led to a Database Breakthrough “Necessity is the mother of invention” may be an old saying, but it’s so often true – even today. Which is why creative ideas for new technologies are coming not just … Continue reading

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Fast Data Access in the Execution Migration Machine

By Jingjing Wang, University of Washington The Execution Migration Machine (EM2) project, by Srini Devadas and his team from MIT CSAIL, proposes a novel solution for caching and data consistency when there are a huge number of processors.  A real … Continue reading

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BlueSSD: A Multi-Access, Multi-SSD Reconfigurable Platform for Big Data

By Jingjing Wang, University of Washington At the Intel Science and  Technology Center for Big Data Research Retreat earlier this year, Arvind of MIT CSAIL introduced a new storage architecture specially designed for large-scale, distributed data management tasks. Usually, when … Continue reading

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Anti-Caching and Non-Volatile Memory for Transactional DBMS

By Andy Pavlo of Brown University and Mike Stonebraker of MIT CSAIL The traditional wisdom for building disk-based database management systems (DBMSs) is to organize blocks of data on disk, with a main memory block cache. In order to improve performance given high disk … Continue reading

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High-Performance Computing Meets Databases

By Bill Howe, Ph.D., University of Washington eScience Institute The large-scale data systems associated with high performance computing (HPC) applications ― e.g., parallel file systems ― are becoming increasingly “smart,” augmenting basic open(), seek() and read() interfaces with sophisticated indexing … Continue reading

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