Automatic parallelisation research promises faster code by non-experts

November 23, 2016 // By Graham Prophet
Researchers at MIT have reported work that they say will allow non-specialists – that is, not specialised in the process of manually coding parallel execution of program flow – to optimize programs that run on, and make best use of, multiprocessor chips.

Dynamic programming – says the MIT information release – is a technique that can yield relatively efficient solutions to computational problems in economics, genomic analysis, and other fields. But adapting it to computer chips with multiple “cores,” or processing units, requires a level of programming expertise that few (for example) economists and biologists have.

 

The MIT statement continues; Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Stony Brook University aim to change that, with a new system that allows users to describe what they want their programs to do in very general terms. It then automatically produces versions of those programs that are optimized to run on multicore chips. It also guarantees that the new versions will yield exactly the same results that the single-core versions would, albeit much faster.

 

In experiments, the researchers used the system to “parallelize” several algorithms that used dynamic programming, splitting them up so that they would run on multicore chips. The resulting programs were between three and 11 times as fast as those produced by earlier techniques for automatic parallelization, and they were generally as efficient as those that were hand-parallelized by computer scientists.

 

The researchers presented their new systemIn October 2016 at the Association for Computing Machinery’s conference on Systems, Programming, Languages and Applications: Software for Humanity.

 

Dynamic programming offers exponential speedups on a certain class of problems because it stores and reuses the results of computations, rather than recomputing them every time they’re required.

 

“But you need more memory, because you store the results of intermediate computations,” says Shachar Itzhaky, first author on the new paper and a postdoc in the group of Armando Solar-Lezama, an associate professor of electrical engineering and computer science at MIT. “When you come to implement it, you realize that you don't get as much speedup as you thought you would, because the memory is slow. When you store