Soft errors caused by energetic particle strikes in on-chip cache memories have become a critical challenge for microprocessor design. Architectural vulnerability factor (AVF), which is defined as the probability that...
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Soft errors caused by energetic particle strikes in on-chip cache memories have become a critical challenge for microprocessor design. Architectural vulnerability factor (AVF), which is defined as the probability that a transient fault in the structure would result in a visible error in the final output of a program, has been widely employed for accurate soft error rate estimation. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures (i.e., issue queue, reorder buffer). Considering large on-chip L2 cache, redundancy-based protection techniques (such as ECC) have been widely employed for L2 cache data integrity with high costs. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to the over-protection, thus incurring high overheads. Therefore, designing AVF-aware protection techniques would be attractive for designers to achieve a cost-efficient protection for caches, especially at early design stage. In this paper, we propose an improved AVF estimation framework for conducing comprehensive characterization of dynamic behavior and predictability of L2 cache vulnerability. We propose to employ Bayesian Additive Regression Trees (BART) method to accurately model the variation of L2 cache AVF and to quantitatively explain the important effects of several key performance metrics on L2 cache AVF. Then we employ bump hunting technique to extract some simple selecting rules based on several key performance metrics for a simplified and fast estimation of L2 cache AVF. Using the simplified L2 cache AVF estimator, we develop an AVF-aware ECC technique as an example to demonstrate the cost-efficient advantages of the AVF prediction based dynamic fault tolerant techniques. Experimental results show that compared with traditional full ECC technique, AVF-aware ECC technique reduces the L2 cache acc
Chip Multi-Processors (CMPs) emerge as a mainstream architectural design alternative for high performance parallel and distributed computing. Last Level Cache (LLC) management is critical to CMPs because off-chip acce...
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To provide timely results for ‘Big Data Analytics’, it is crucial to satisfy deadline requirements for MapReduce jobs in production environments. In this paper, we propose a deadline-oriented task scheduling approac...
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The Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of...
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ISBN:
(纸本)9781450323697
The Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of heterogeneous software resources have not been organized in a reasonable and efficient way. Software feature is an ideal material to characterize software resources. The effectiveness of feature- related tasks will be greatly improved, if a multi-grained feature repository is available. In this paper, we propose a novel approach for organizing, analyzing and recommend- ing software features. Firstly, we construct a Hierarchical rEpository of Software feAture (HESA). Then, we mine the hidden affnities among the features and recommend relevant and high-quality features to stakeholders based on HESA. Finally, we conduct a user study to evaluate our approach quantitatively. The results show that HESA can organize software features in a more reasonable way compared to the traditional and the state-of-the-art approaches. The result of feature recommendation is effective and interesting. Categories and Subject Descriptors D.2.9 [Software Engineering]: Mining Software Reposi- tory;H.3.3 [Information Storage and retrieval]: Fea- ture Model, Clustering, Query formulation General Terms Algorithms, Human Factors.
Nowadays, the demand for software resources on different granularity is becoming prominent in software engineering field. However, a large quantity of heterogeneous software resources have not been organized in a reas...
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Multi-island single electron transistor is an important kind of the single electron transistor, which is convenient to realize the controllable room temperature operation. A novel semi-empirical compact model for the ...
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ISBN:
(纸本)9781424435449
Multi-island single electron transistor is an important kind of the single electron transistor, which is convenient to realize the controllable room temperature operation. A novel semi-empirical compact model for the Multi-island single electron transistor is proposed. The new approach combines the orthodox theory of the single electron tunneling through single coulomb island and a novel empirical analysis procedure for the chain of multi coulomb islands to solve the current of the whole multi-island single electron transistor. The tunneling rates are calculated based on the orthodox theory for the single electron tunneling. The tunneling currents representing the first splitted peaks in the coulomb oscillation curves are calculated according to the assumption that the currents through all the coulomb islands are equal to each other at the stable states, while the currents representing the other splitted peaks are constructed and merged together according to the empirical analysis. The model is verified by the traditional SET simulator SIMON and shows much faster calculation speed than SIMON. Therefore, the novel compact model is suitable for the large scale MISET circuit simulation.
Stragglers can temporize jobs and reduce cluster efficiency seriously. Many researches have been contributed to the solution, such as Blacklist[8], speculative execution[1, 6], Dolly[8]. In this paper, we put forward ...
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Wireless sensor networks (WSN) is a critical technology for information gathering covering many areas, including health-care, transportation, air traffic control and environment monitoring. Despite wide use, the fast ...
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Semantic Web service matchmaking,as one of the most challenging problems in Semantic Web services (SWS),aims to filter and rank a set of services with respect to a service query by using a certain matching *** this pa...
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Semantic Web service matchmaking,as one of the most challenging problems in Semantic Web services (SWS),aims to filter and rank a set of services with respect to a service query by using a certain matching *** this paper,we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration (e.g.,the weighted sum) for SWS *** logistic regression model is trained on training data derived from binary relevance assessments of existing test collections,and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching *** are then ranked according to the probabilities of relevance with respect to each *** method is evaluated on two main test collections,SAWSDL-TC2 and Jena Geography Dataset(JGD).Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service,and hence can improve the effectiveness of service matchmaking.
As the rapid growth of open source software, how to choose software from many alternatives becomes a great challenge. Traditional ranking approaches mainly focus on the characteristics of the software themselves, such...
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