یک روش توأم زمان‌بندی و نگاشت وظایف در سیستم‌های چندپردازنده‌ای روی تراشه با هدف بهبود چالش‌های طراحی
محورهای موضوعی : مهندسی برق و کامپیوتر
آتنا عبدی
1
(دانشگاه امیرکبیر)
حمیدرضا زرندی
2
(دانشگاه صنعتی امیرکبیر)
شاهرخ جلیلیان
3
(پژوهشگاه فضایی ایران)
کلید واژه: سیستم چندپردازنده‌ایزمان‌بندیقابلت اطمینانتوان مصرفیدمابهینه‌سازی چندهدفیپوسته Pareto,
چکیده مقاله :
در این مقاله روش مکاشفه‌ای زمان‌بندی و نگاشت وظایف ایستا به منظور بهینه‌سازی زمان اجرا، قابلیت اطمینان، توان مصرفی و دما به عنوان اساسی‌ترین چالش‌های طراحی سیستم‌های چندپردازنده‌ای ارائه شده است. روش ارائه‌شده بر پایه زمان‌بندی لیستی بوده و تکرار وظایف، مقیاس پویای ولتاژ و فرکانس و افزودن زمان‌های خالی با هدف بهبود قابلیت اطمینان، توان مصرفی و دمای سیستم و گسترده‌کردن فضای جواب با هدف جستجوی مؤثرتر در آن در نظر گرفته شده است. به دلیل رابطه متخاصم و ناهمسوی مابین پارامترهای ذکرشده، فرایند بهینه‌سازی چندهدفی بسیار پیچیده بوده و در روش پیشنهادی از راهکار استخراج پوسته Pareto استفاده شده است. همچنین در این روش، مدل‌سازی جامعی از تمامی اهداف صورت گرفته و وابستگی‌های آنها لحاظ شده است. آزمایش‌های متعدی به منظور بررسی کارایی و قابلیت‌های روش پیشنهادی در بهینه‌سازی هم‌زمان اهداف مسئله و تولید جواب‌های درست انجام گرفته است. بررسی‌ها و مقایسه روش پیشنهادی با یک روش مکاشفه‌ای مؤثر پیشین بهبود میانگین 19% در پارامترهای طراحی مورد هدف مسئله را نشان می‌دهد.
In this paper, a static task scheduling and mapping heuristic approach to optimize execution time, reliability, power and temperature of multiprocessor systems on chip is presented. This method is proposed based on the list scheduling approach and utilized task replication, dynamic voltage and frequency scaling, and adding cooling slacks to improve reliability, power consumption and temperature to expand the design space and explore the solution set more efficiently. Due to the existing trade-offs among the considered parameters and their optimization, the optimization process is complicated and our proposed method is used the Pareto front generation technique. Moreover, our proposed method, models the objectives comprehensively to consider their dependency. Several experiments are performed to demonstrate the performance and capability of the proposed method in joint optimization of the parameters and extracting the proper solution set. Compared to the previous research, our proposed method outperforms them in optimizing the considered design parameters and its results is 19% better averagely than an efficient studied heuristic method.
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