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تسجيل مستخدم جديدFundamental Resource Trade-offs for Encoded Distributed Optimization
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نشر من قبل
Seyed Mohammadreza Mousavi Kalan
تاريخ النشر
2018
مجال البحث
الهندسة المعلوماتية
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English
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Dealing with the shear size and complexity of todays massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a.~stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. A recent computational framework, called encoded optimization, creates redundancy in the data to mitigate the effect of stragglers. In this paper we develop novel mathematical understanding for this framework demonstrating its effectiveness in much broader settings than was previously understood. We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy), and straggler toleration in this framework.
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