MIT researchers develop new programming language for multicore image processing

August 06, 2012 // By Nick Flaherty
Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new programming language for multicore image processing algorithms called Halide.

Not only are Halide programs easier to read, write and revise than image-processing programs written in a conventional language, but because Halide automates code-optimization procedures that would ordinarily take hours to perform by hand, they're also significantly faster says the team.
In tests, the MIT researchers used Halide to rewrite several common image-processing algorithms whose performance had already been optimized by seasoned programmers. The Halide versions were typically about one-third as long but offered significant performance gains — two-, three-, or even six-fold speedups. In one instance, the Halide program was actually longer than the original — but the speedup was 70-fold.
However the developmentis currently separate to the OpenCL multicore programming specification.
Jonathan Ragan-Kelley, a graduate student in the Department of Electrical Engineering and Computer Science (EECS), and Andrew Adams, a CSAIL postdoc, led the development of Halide, and they've released the code online.
Halide doesn't spare the programmer from thinking about how to parallelize efficiently on particular machines, but it splits that problem off from the description of the image-processing algorithms. A Halide program has two sections: one for the algorithms, and one for the processing "schedule." The schedule can specify the size and shape of the image chunks that each core needs to process at each step in the pipeline, and it can specify data dependencies — for instance, that steps being executed on particular cores will need access to the results of previous steps on different cores. Once the schedule is drawn up, however, Halide handles all the accounting automatically.
A programmer who wants to export a program to a different machine just changes the schedule, not the algorithm description. A programmer who wants to add a new processing step to the pipeline just plugs in a description of the new procedure, without having to modify the existing ones. (A new step in the pipeline will require a corresponding specification in the schedule, however.)
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