Jun, Liu, Xu: Cutting cost and power consumption for big data


July 23, 2015

SavingCostBigData

Random-access memory, or RAM, is where computers like to store the data they’re working on. A processor can retrieve data from RAM tens of thousands of times more rapidly than it can from the computer’s disk drive. But in the age of big data, data sets are often much too large to fit in a single computer’s RAM. Sequencing data describing a single large genome could take up the RAM of somewhere between 40 and 100 typical computers.

Flash memory — the type of memory used by most portable devices — could provide an alternative to conventional RAM for big-data applications. It’s about a tenth as expensive, and it consumes about a tenth as much power. The problem is that it’s also a tenth as fast. But at the International Symposium on Computer Architecture in June, MIT researchers presented a new system that, for several common big-data applications, should make servers using flash memory as efficient as those using conventional RAM, while preserving their power and cost savings.

Joining Arvind on the new paper are Sang Woo Jun and Ming Liu, MIT graduate students in computer science and engineering and joint first authors; their fellow grad student Shuotao Xu; Sungjin Lee, a postdoc in Arvind’s group; Myron King and Jamey Hicks, who did their PhDs with Arvind and were researchers at Quanta Computer when the new system was developed; and one of their colleagues from Quanta, John Ankcorn — who is also an MIT alumnus. Read the full article at MIT NEWS

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