by Larry Hardesty, MIT News Office
Computer chips’ clocks have stopped getting faster. To maintain the regular doubling of computer power that we now take for granted, chip makers have been giving chips more “cores,” or processing units. But how to distribute computations across multiple cores is a hard problem, and this five-part series of articles examines the different levels at which MIT researchers are tackling it, from hardware design up to the development of new programming languages.

t its most fundamental, computer science is about the search for better algorithms — more efficient ways for computers to do things like sort data or filter noise out of digital signals. But most new algorithms are designed to run on serial computers, which process instructions one after another. Retooling them to run on parallel processors is rarely simple.

As head of MIT’s Supertech Research Group, Professor of Computer Science and Engineering Charles Leiserson is an old hand at parallelizing algorithms. Often, he explains, the best approach is to use a technique known as divide-and-conquer. Divide-and-conquer is a recursive technique, meaning that it uses some method to split a problem in half, then uses the same method to split those halves in half, and so on. The advantage of divide-and-conquer, Leiserson explains, is that it enables a computer to tailor an algorithm’s execution to the resources available.

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