GPU Accelerators Radically Boost SQL Queries
In-memory databases are all the rage for very fast query processing, but you have to have the right balance of compute and memory for queries against in-memory databases to really scream. Sometimes, a large number of relatively small cores and a reasonably beefy chunk of RAM can radically outperform clusters with hundreds or thousands of server nodes running parallel analytics platforms based on Hadoop or in-memory architectures. This is apparently the case with a forthcoming product called MapD, which is a massively parallel database and visualization system with an interesting history.
MapD was founded by Todd Mostak in September 2013 after he had seen some impressive performance results with a GPU-accelerated database he created as much out of frustration as curiosity to help him do research for his master’s thesis. As part of his thesis, Mostak was trying to correlate some 40 million Tweets relating to the Arab Spring revolution in Egypt to voting districts in the country. The idea was to map the tweets and test the idea that people were more radicalized in poorer areas in Egypt; sentiment analysis for ranking the Tweets came from comparing them to forums and message boards.
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