{"id":6299,"date":"2016-05-09T07:00:05","date_gmt":"2016-05-09T15:00:05","guid":{"rendered":"http:\/\/formtek.com\/blog\/?p=6299"},"modified":"2016-03-31T07:40:10","modified_gmt":"2016-03-31T15:40:10","slug":"mapd-and-gpus-fast-data-speeding-up-compute-bound-database-queries","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/mapd-and-gpus-fast-data-speeding-up-compute-bound-database-queries\/","title":{"rendered":"MapD and GPUs: Fast Data: Speeding up Compute-Bound Database Queries"},"content":{"rendered":"

Big Data sets are only possible if there is some Big Processing available to handle the enormous volumes. \u00a0While the amount of data being captured is rising exponentially hardware and software engineers are rushing to develop software algorithms and processing hardware that can keep up with the load.<\/p>\n

Increasingly GPU architectures are being used to solve exceptionally complex problems. \u00a0The misnamed graphical processing unit (GPU) is a\u00a0computer processor that is fine-tuned to process complex mathematical operations, like matrix mathematics and linear algebra very quickly. \u00a0These chips were initially used primarily to work on graphic and image processing problems, but more recently they are finding success<\/a>\u00a0when applied to a variety of\u00a0 scientific, analytics, engineering, consumer, and enterprise applications.<\/p>\n

For example, consider database technology. \u00a0Recently speed gains have been seen by database solutions that are designed to run completely in-memory. \u00a0SAP HANA<\/a> and Spark <\/a>are examples of this. \u00a0In-memory databases are great when the problems you work on are memory bound. \u00a0 But there’s another possible approach. \u00a0The San Francisco-based startup MapD <\/a>has applied the use of GPUs to database processing. \u00a0The MapD solution attempts to use GPUs to speed things up processing for problems that are\u00a0compute-bound<\/a>. \u00a0The speed gains that they’ve seen have been huge, sometimes as much as 100 times the performance of standard CPU-based databases.<\/p>\n

James Curtis<\/a>, 451 Group’s Senior Analyst, said that<\/a> “MapD has an innovative high-performance offering that combines GPU technology with the firm’s own columnar database and visualization frontend, making MapD well suited to help organizations drive real-time decision making.”<\/p>\n

Todd Mostak<\/a>, founder of MapD, told the Wall Street Journal<\/a> that “I would say we\u2019re \u2018fast data.\u2019”<\/p>\n

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Big Data sets are only possible if there is some Big Processing available to handle the enormous volumes. \u00a0While the amount of data being captured is rising exponentially hardware and software engineers are rushing to develop software algorithms and processing…<\/span><\/p>\n

Read more ›<\/a><\/div>\n

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