Faster parallel computing

Monday, September 12, 2016 - 23:11 in Mathematics & Economics

In today’s computer chips, memory management is based on what computer scientists call the principle of locality: If a program needs a chunk of data stored at some memory location, it probably needs the neighboring chunks as well. But that assumption breaks down in the age of big data, now that computer programs more frequently act on just a few data items scattered arbitrarily across huge data sets. Since fetching data from their main memory banks is the major performance bottleneck in today’s chips, having to fetch it more frequently can dramatically slow program execution. This week, at the International Conference on Parallel Architectures and Compilation Techniques, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are presenting a new programming language, called Milk, that lets application developers manage memory more efficiently in programs that deal with scattered data points in large data sets. In tests on several common algorithms, programs written...

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