“The order of operations in these optimized binaries are complicated, which means that they can be hard to disentangle,” says Mendis, a graduate student at CSAIL. “Because stencils do the same computation over and over again, we are able to accumulate enough data to recover the original algorithms.”
From there, the Helium system then replaces the original bit-rotted components with the re-optimized ones. The net result: Helium can improve the performance of certain Photoshop filters by 75 percent, and the performance of less optimized programs such as Microsoft Windows’ IrfanView by 400 to 500 percent.
“We’ve found that Helium can make updates in one day that would take human engineers upwards of three months,” says Amarasinghe. “A system like this can help companies make sure that the next generation of code is faster, and save them the trouble of putting 100 people on these sorts of problems.”
The research was presented in a paper accepted to the Association for Computing Machinery SIGPLAN conference on Programming Language Design and Implementation (PLDI 2015), which took place June 13-17 in Portland, Oregon.
The paper was written by Mendis, fellow graduate students Jeffrey Bosboom and Kevin Wu, research scientist Shoaib Kamil, postdoc Jonathan Ragan-Kelley PhD ’14, Amarasinghe, and researchers from Adobe and Google.
“We are in an era where computer architectures are changing at a dramatic rate, which makes it important to write code that can work on multiple platforms,” says Mary Hall, a professor at the University of Utah’s School of Computing. “Helium is an interesting approach that has the potential to facilitate higher-level descriptions of stencil computations that could then be more easily ported to future architectures.”
One unexpected byproduct of the work is that it lets researchers see the different tricks that programmers used on the old code, such as archaeologists combing through computational fossils.
“We can see the ‘bit hacks’ that engineers use to optimize their algorithms,” says Amarasinghe, “as well as better understand the larger context of how programmers approach different coding challenges.”
References:http://phys.org/