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Register a new userUsing data flow graph to certificate parallel applications
استخدام مخطط تدفق البيانات للتحقق من صحة تنفيذ التطبيقات المتوازية
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Aِl-Baath University ورقة بحثية
Publication date
2017
fields
Mathematics
and research's language is
العربية
Created by
Shamra Editor
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سنقدم في هذا البحث طريقتين احتماليتين للتحقق من صحة تنفيذ التطبيقات المتوازية على بنى تفرعية بفرض عدم وجود عقد موثوقة (Oracle) نعتمد عليها في التحقق، بالإضافة إلى تقديم نموذج الكلفة للآليتين و المقارنة بينهما. سنهتم في هذا البحث بالتطبيقات المتوازية الممثلة بمخطط تدفق البيانات المبني ديناميكياً خلال التنفيذ و المنفذة في بيئة تفرعية واسعة غير متجانسة و ديناميكية، تعتمد مبدأ سرقة العمل لتوزيع المهام بين المعالجات.
In this research, We introduce two probabilistic mechanisms to certificate parallel applications on distribute architecture supposing that there are no oracles on which we depend on certification, in addition to introducing cost model of two mechanisms and compare them. In this research, we are interested in parallel applications, which are represented by data-flow graph that is built dynamically during the execution and which are executed in a wide distributed heterogeneous and dynamic environment and these applications use the principle of work stealing to distribute the tasks among the processors.
References used
DABROWSKI Ch, 2009- Reliability in grid computing systems, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8970 Gaithersburg, MD 20899-8970, U.S.A, Vol.21.8, Pages 927-959
GAUTIER T, JAFAR S, KRINGS AW, & ROCH J-L, 2005- Theft-Induced Checkpointing for Reconfigurable Dataflow Applications, IEEE, vol 16, 6pp.-6
GHAZAL R, & JAFAR S, 2016- A checkpoint/ recovery Model based on work stealing for grid application, Albaath Magazin, vol 38, pp 71-86
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In this paper we present a study on the time cost
added to the grid computing as a result of the use of a
coordinated checkpoint / recovery fault tolerance protocol, we aim
to find a mathematical model which determined the suitable time
to save t
he checkpoints for application, to achieve a minimum
finish time of parallel application in grid computing with faults and
fault tolerance protocols, we have find this model by serial
modeling to the goal errors, execution environment and the
chosen fault tolerance protocol all that by Kolmogorov differential
equations.
In this paper, we introduce a continuous mathematical model to
optimize the compromise between the overhead of fault tolerance
mechanism and the faults impacts in the environment of
execution. The fault tolerance mechanism considered in this
rese
arch is a coordinated checkpoint/recovery mechanism and the
study based on stochastic model of different performance critics of
parallel application on parallel and distributed environment.
We introduce an auto adaptive strategy enables to write a parallel
algorithm adapts to the number of available resources at allocated
parallel environment to execute the parallel program. The parallel
applications we are studying which are represe
nted by data-flow
graph which built dynamically during the execution. The new
suggested strategy is based on coupling of a sequential algorithm
and a parallel one and relies on the principle of work stealing in
the tasks scheduling. We offer a study of the complexity of the
adaptive algorithm and analyze its performance on processors
and compare it with a performance of a classic parallel algorithm.
The objective of this paper is presenting an educational data
mining case study, by applying data mining techniques.
Data Mining
التنقيب في البيانات
التنقيب في بيانات المؤسسات التعليمية
برنامج ذكاء الأعمال الخاص بشركة مايكروسوفت
خوارزمية مايكروسوفت لأشجار القرار
خوارزمية مايكروسوفت للتجميع و الكشف عن الحالات الشاذة
خوارزمية مايكروسوفت لقواعد الارتباط
Educational Data Mining
SQL Server Business Intelligence Development Studio
Microsoft Decision Trees
Microsoft Clustering
Outlier Detection
Microsoft Association Rules
المزيد..
While neural networks produce state-of-the-art performance in several NLP tasks, they generally depend heavily on lexicalized information, which transfer poorly between domains. We present a combination of two strategies to mitigate this dependence o
n lexicalized information in fact verification tasks. We present a data distillation technique for delexicalization, which we then combine with a model distillation method to prevent aggressive data distillation. We show that by using our solution, not only does the performance of an existing state-of-the-art model remain at par with that of the model trained on a fully lexicalized data, but it also performs better than it when tested out of domain. We show that the technique we present encourages models to extract transferable facts from a given fact verification dataset.
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