Survey Optimize Multicore with Machine Learning
سال انتشار: 1396
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 666
فایل این مقاله در 12 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
این مقاله در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ITCT04_019
تاریخ نمایه سازی: 17 آبان 1396
چکیده مقاله:
Multicore architectures have become so complex and diverse that there is no obvious path to achieving good performance. Hundreds of code transformations, compiler flags, architectural features and optimization parameters result in a search space that can take many machine-months to explore exhaustively. Inspired by successes in the systems community, we apply state-of-the-art machine learning techniques to explore this space more intelligently. On 7-point and 27-point stencil code, our technique takes about two hours to discover a configuration whose performance is within 1% of and up to 18% better than that achieved by a human expert. This factor of 2000 speedup over manual exploration of the auto-tuning parameter space enables us to explore optimizations that were previously off-limits. We believe the opportunity for using machine learning in multicore auto tuning is even more promising than the successes to date in the systems literature.
کلیدواژه ها:
نویسندگان