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from the lets-MILK-it-for-all-it-is-worth dept.
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 in the new language were four times as fast as those written in existing languages. But the researchers believe that further work will yield even larger gains.
http://phys.org/news/2016-09-language-fourfold-speedups-problems-common.html
[Source]: Faster parallel computing
(Score: 2, Disagree) by JoeMerchant on Tuesday September 13 2016, @11:22PM
The snarky answer is that clearly either: a) the editor did not read the article, or b) they're mentally challenged when it comes to writing appropriate headlines.
I do see from the summary that the meat of the news is regarding sparse table accesses and how it relates to paged memory fetching (poorly, for efficiency).
What I fail to see is why a new language would be necessary or even desired in association with an advanced memory management technique - it would seem to be something better implemented in an API provided for an existing language, or three.
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