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A user-level scheduling along with a specific data alignment for matrix multiplication in cache-coherent Non-Uniform Memory Access (ccNUMA) architectures is presented. Addressing the data locality problem that could occur in such systems potentially alleviates memory bottlenecks. We show experimentally that an agnostic thread scheduler (e.g., OpenMP 3.1) from the data placement on a ccNUMA machine produces a high number of cache-misses. To overcome this memory contention problem, we show how proper memory mapping and scheduling manage to tune an existing matrix multiplication implementation and reduce the number of cache-misses by 67% and consequently, reduce the computation time by up to 22%. Finally, we show a relationship between cache-misses and the gained speedup as a novel figure of merit to measure the quality of the method.

Document database systems store self-describing semi-structured records, such as JSON, "as-is" without requiring the users to pre-define a schema. This provides users with the flexibility to change the structure of incoming records without worrying about taking the system offline or hindering the performance of currently running queries. However, the flexibility of such systems does not free. The large amount of redundancy in the records can introduce an unnecessary storage overhead and impact query performance. Our focus in this paper is to address the storage overhead issue by introducing a tuple compactor framework that infers and extracts the schema from self-describing semi-structured records during the data ingestion. As many prominent document stores, such as MongoDB and Couchbase, adopt Log Structured Merge (LSM) trees in their storage engines, our framework exploits LSM lifecycle events to piggyback the schema inference and extraction operations. We have implemented and empirically evaluated our approach to measure its impact on storage, data ingestion, and query performance in the context of Apache AsterixDB.

No abstract

Large quantities of raw data are being generated by many different sources in different formats. Private and public sectors alike acclaim the valuable information and insights that can be mined from such data to better understand the dynamics of everyday life, such as traffic, worldwide logistics, and social behavior. For this reason, storing, managing, and analyzing "Big Data" at scale is getting a tremendous amount of attention, both in academia and industry. In this paper, we demonstrate the power of a parallel connection that we have built between Apache Spark and Apache AsterixDB (Incubating) to enable complex analytics such as machine learning and graph analysis on data drawn from large semi-structured data collections. The integration of these two systems allows researchers and data scientists to leverage AsterixDB capabilities, including fast ingestion and indexing of semi-structured data and efficient answering of geo-spatial and fuzzy text queries. Complex data analytics can then be performed on the resulting AsterixDB query output in order to obtain additional insights by leveraging the power of Spark's machine learning and graph libraries.