The Role of Middleware in Optimizing Vector Processing
Big data brings big benefits, as well as big challenges, for existing volume server compute architectures. That is because AI/ML data sources contain mostly unstructured data, which is challenging to process using traditional system architectures. Revealing the hidden benefits and insights within unstructured data efficiently requires new explainable software applications, processors and memory configurations, using industry standard coding languages, versus proprietary, “black box” software packages.
This white paper delves into the world of unstructured data and describes some of the technologies—especially vector processors and their optimization software—that play key roles in solving enterprise problems that present themselves as a result of increasing amounts of globally generated data.
Additionally, this white paper discusses how NEC’s SX-Aurora TSUBASA vector engine’s compute architecture combined with NEC’s opensource Frovedis framework address these challenges at far less cost, and with higher performance, than an approach solely using scalar processors. You will learn how vector processing with SX-Aurora TSUBASA’s vector engine, combined with Frovedis, can change the way big data is handled, while removing the barriers to achieving even higher performance in the future.