کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4951611 1441476 2017 41 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Machine learning-based thread-parallelism regulation in software transactional memory
ترجمه فارسی عنوان
تنظیم ماشین حساب مبتنی بر یادگیری ماشین در حافظه کاربردی نرم افزار
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Transactional Memory (TM) stands as a powerful paradigm for manipulating shared data in concurrent applications. It avoids the drawbacks of coarse grain locking schemes, namely the potentially excessive limitation of concurrency, while jointly providing support for synchronization transparency to the programmers, which is achieved by embedding code-blocks accessing shared data within transactions. On the downside, excessive transaction aborts may arise in scenarios with non-negligible volumes of conflicting data accesses, which might significantly impair performance. TM needs therefore to resort to methods enabling applications to run with the maximum degree of transaction concurrency that still avoids thrashing. In this article, we focus on Software TM (STM) implementations and present a machine learning-based approach that enables the dynamic selection of the best suited number of threads to be kept alive along specific phases of the execution of STM applications, depending on (variations of) the shared data access pattern. Two key contributions are provided with our approach: (i) the identification of the well suited set of features allowing the instantiation of a reliable neural network-based performance model and (ii) the introduction of mechanisms enabling the reduction of the run-time overhead for sampling these features. We integrated a real implementation of our machine learning-based thread-parallelism regulation approach within the TinySTM open source package and present experimental data, based on the STAMP benchmark suite, which show the effectiveness of the presented thread-parallelism regulation policy in optimizing transaction throughput.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Parallel and Distributed Computing - Volume 109, November 2017, Pages 208-229
نویسندگان
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