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  • Divide and conquer

     Japo_Japo updated 1 month, 1 week ago 1 Member · 1 Post
  • Japo_Japo

    Member
    September 10, 2021 at 8:17 pm

    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.)

    “When you have the idea that you might want to parallelize something a certain way or use stages a certain way, when writing that manually, it’s really hard to express that idea correctly,” Ragan-Kelley says. “If you have a new optimization idea that you want to apply, chances are you’re going to spend three days debugging it because you’ve broken it in the process. With this, you change one line that expresses that idea, and it synthesizes the correct thing.”

    Although Halide programs are simpler to write and to read than ordinary image-processing programs, because the scheduling is handled automatically, they still frequently offer performance gains over even the most carefully hand-engineered code. Moreover, Halide code is so easy to modify that programmers could simply experiment with half-baked ideas to see if they improve performance.

    “You can just flail around and try different things at random, and you’ll often find something really good,” Adams says. “Only much later, when you’ve thought about it very hard, will you figure out why it’s good.”

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