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Pipelining Vector-Based Statistical Functions for In-Memory Analytics

Steven Graves

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Main memory (DRAM) is the fastest storage medium for a database management system. But once you have a highly efficient in-memory database system, how do you reduce latency further?

Our new white paper, "Pipelining Vector-Based Statistical Functions for In-Memory Analytics", available for free download, explains McObject’s focus on optimizing CPU L1/L2 cache use in eXtremeDB Financial Edition.

The product’s two key features in this area are its support for columnar data handling (via its “sequence” data type), and the programming technique of pipelining using eXtremeDB Financial Edition’s library of vector-based statistical functions.

Columnar handling maximizes the proportion of relevant data brought into L1/L2 cache with every fetch.

Pipelining causes interim results to remain in L1/L2 cache during processing, rather than being output as temporary tables in main memory. Both columnar data handling and pipelining eliminate latency by minimizing transfers between main memory and L1/L2 cache.



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