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Machine-Learning-Based Identification of Defect Patterns in Semiconductor Wafer Maps: An Overview and Proposal 28

Machine-Learning-Based Identification of Defect Patterns in ...

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28th IEEE international parallel & distributed processing Symposium Workshops (IPDPSW)

作者： Adly, Fatima Yoo, Paul D. Muhaidat, Sami Al-Hammadi, Yousof Khalifa Univ Dept ECE Abu Dhabi U Arab Emirates Univ Sydney Ctr Distributed & High Performance Comp J12 Sydney NSW 2006 Australia

ISBN: (纸本)9781479941162

Wafers are formed from very thin layers of a semiconductor material;hence, they are highly susceptible to various kinds of defects. The defects are most likely to occur during the lengthy and complex fabrication process, which can include hundreds of steps. Wafer defects are generally caused by machine inaccuracy, chemical stains, physical damages, human mistakes, and atmospheric conditions. The defective chips tend to have several unique spatial patterns across the wafer, namely ring, spot, repetitive and cluster patterns. To locate such defect patterns, wafer maps are used to visualize and ultimately lead to better understanding of what happened during the process failure. To identify the unique patterns of defects and to find the point of manufacturing process that causes such defects accurately, nature-inspired model-free machine-learning techniques have been well accepted. This paper thus reviews the theoretical and experimental literature of such models with a focus on model learnability and efficiency-related issues involving data reduction and transformation techniques, which could be seen as the key model properties to deal with big data applications.

关键词： wafer defect patterns wafer map nature-inspired machine-learning and classification

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Portfolio-based Selection of Robust Dynamic Loop Scheduling Algorithms Using Machine Learning 28

Portfolio-based Selection of Robust Dynamic Loop Scheduling ...

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28th IEEE international parallel & distributed processing Symposium Workshops (IPDPSW)

作者： Sukhija, Nitin Malone, Brandon Srivastava, Srishti Banicescu, Ioana Ciorba, Florina M. Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA Univ Helsinki Helsinki Inst Informat Technol Dept Comp Sci Helsinki Finland Tech Univ Dresden Ctr Informat Serv & High Performance Comp D-01062 Dresden Germany

ISBN: (纸本)9781479941162

The execution of computationally intensive parallel applications in heterogeneous environments, where the quality and quantity of computing resources available to a single user continuously change, often leads to irregular behavior, in general due to variations of algorithmic and systemic nature. To improve the performance of scientific applications, loop scheduling algorithms are often employed for load balancing of their parallel loops. However, it is a challenge to select the most robust scheduling algorithms for guaranteeing optimized performance of scientific applications on large-scale computing systems that comprise resources which are widely distributed, highly heterogeneous, often shared among multiple users, and have computing availabilities that cannot always be guaranteed or predicted. To address this challenge, in this work we focus on a portfolio-based approach to enable the dynamic selection and use of the most robust dynamic loop scheduling (DLS) algorithm from a portfolio of DLS algorithms, depending on the given application and current system characteristics including workload conditions. Thus, in this paper we provide a solution to the algorithm selection problem and experimentally evaluate its quality. We propose the use of supervised machine learning techniques to build empirical robustness prediction models that are used to predict DLS algorithm's robustness for given scientific application characteristics and system availabilities. Using simulated scientific applications characteristics and system availabilities, along with empirical robustness prediction models, we show that the proposed portfolio-based approach enables the selection of the most robust DLS algorithm that satisfies a user-specified tolerance on the given application's performance obtained in the particular computing system with a certain variable availability. We also show that the portfolio-based approach offers higher guarantees regarding the robust performance of the applic

关键词： Dynamic loop scheduling robustness algorithm selection empirical robustness prediction models machine learning techniques variable system availability

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Locality-Sensitive Operators for parallel Main-Memory Database Clusters

Locality-Sensitive Operators for Parallel Main-Memory Databa...

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IEEE 30th international conference on Data Engineering (ICDE)

作者： Roediger, Wolf Muehlbauer, Tobias Unterbrunner, Philipp Reiser, Angelika Kemper, Alfons Neumann, Thomas Tech Univ Munich D-80290 Munich Germany Snowflake Comp Inc San Mateo CA USA

ISBN: (纸本)9781479934805

The growth in compute speed has outpaced the growth in network bandwidth over the last decades. This has led to an increasing performance gap between local and distributed processing. A parallel database cluster thus has to maximize the locality of query processing. A common technique to this end is to co-partition relations to avoid expensive data shuffling across the network. However, this is limited to one attribute per relation and is expensive to maintain in the face of updates. Other attributes often exhibit a fuzzy co-location due to correlations with the distribution key but current approaches do not leverage this. In this paper, we introduce locality-sensitive data shuffling, which can dramatically reduce the amount of network communication for distributed operators such as join and aggregation. We present four novel techniques: (i) optimal partition assignment exploits locality to reduce the network phase duration;(ii) communication scheduling avoids bandwidth underutilization due to cross traffic;(iii) adaptive radix partitioning retains locality during data repartitioning and handles value skew gracefully;and (iv) selective broadcast reduces network communication in the presence of extreme value skew or large numbers of duplicates. We present comprehensive experimental results, which show that our techniques can improve performance by up to factor of 5 for fuzzy co-location and a factor of 3 for inputs with value skew.

关键词： Algorithm design and analysis Bandwidth Correlation Database systems distributed databases Partitioning algorithms

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Meeting Predictable Buffer Limits in the parallel Execution of Event processing Operators 2

Meeting Predictable Buffer Limits in the Parallel Execution ...

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IEEE international conference on Big Data

作者： Mayer, Ruben Koldehofe, Boris Rothermel, Kurt Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781479956661

Complex Event processing (CEP) systems enable applications to react to live-situations by detecting event patterns (complex events) in data streams. With the increasing number of data sources and the increasing volume at which data is produced, parallelization of event detection is becoming of tremendous importance to limit the time events need to be buffered before they actually can be processed by an event detector-named event processing operator. In this paper, we propose a pattern-sensitive partitioning model for data streams that is capable of achieving a high degree of parallelism for event patterns which formerly could only be consistently detected in a sequential manner or at a low parallelization degree. Moreover, we propose methods to dynamically adapt the parallelization degree to limit the buffering imposed on event detection in the presence of dynamic changes to the workload. Extensive evaluations of the system behavior show that the proposed partitioning model allows for a high degree of parallelism and that the proposed adaptation methods are able to meet the buffering level for event detection under high and dynamic workloads.

关键词： buffer storage data handling parallel processing CEP systems buffering complex event processing systems data sources data streams data volume dynamic workloads event detection parallelization event patterns detection event processing operators parallel execution parallelization degree pattern-sensitive partitioning model predictable buffer limits system behavior time events Adaptation models Corporate acquisitions Correlation Delays Event detection Middleware Monitoring Complex Event processing Data parallelization Quality of Service Self-Adaptation Stream processing

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Proceedings - 2014 22nd Euromicro international conference on parallel, distributed, and Network-Based processing, PDP 2014

Proceedings - 2014 22nd Euromicro International Conference o...

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2014 22nd Euromicro international conference on parallel, distributed, and Network-Based processing, PDP 2014

ISBN: (纸本)9781479927289

The proceedings contain 111 papers. The topics discussed include: on partitioning two dimensional finite difference meshes for distributed memory parallel computers;coordinated cooperative work using undependable processors with unreliable broadcast;agricultural reform: more efficient farming using advanced parallel refactoring tools;. the multi-level communication: minimal deadlock-free and storage efficient routing for Torus networks;dynamic feature selection for machine-learning based concurrency regulation in STM;an efficient barrier implementation for OpenMP-like parallelism on the Intel SCC;evaluating the impact of transactional characteristics on the performance of transactional memory applications;scalable parallel I/O on a blue gene/Q supercomputer using compression, topology-aware data aggregation, and subfiling;and EagleMacaw: a dual-tree replication protocol for efficient and reliable P2P media streaming.

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Agent-Based Fault-Tolerance Mechanism for distributed Key-Value Database

Agent-Based Fault-Tolerance Mechanism for Distributed Key-Va...

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5th international conference on Digital Home (ICDH)

作者： Wu Hui-Jun Lu Kai Li Gen Jiang Jin-Fei Wang Shuang-Xi Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China

ISBN: (纸本)9781479942848

distributed key-value database is widely used in Web 2.0 applications and cloud computing environments. It overcomes the weak performance and bad scalability of traditional relational database. But fault in distributed system will lead to errors, then the high performance is useless. So we should build a fault tolerance mechanism. On the other hand, in many application scenarios, transactional operations are inevitable. Some existing key-value databases utilize two-phase commit protocol or optimistic concurrency control in transaction processing. But the problems are sing-node failure and high overhead in protocol processing. Meanwhile, users' programming becomes more error-prone. This paper designs a fault tolerance and recovery mechanism on DStageDB, which is a distributed key-value database. We design an agent-based transaction processing mechanism. The transaction processing speed is improved and less user intervention is needed.

关键词： fault-tolerance key-value database agent transaction

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Tracing Flow Information for Tighter WCET Estimation: Application to Vectorization

Tracing Flow Information for Tighter WCET Estimation: Applic...

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IEEE international conference on Embedded and Real-Time Computing Systems and applications (RTCSA)

作者： Hanbing Li Isabelle Puaut Erven Rohou Inrial/RISA Rennes FRANCE University of Rennes l/IRISA Rennes FRANCE Inria/IRISA Rennes FRANCE

Real-time systems have become ubiquitous, and many play an important role in our everyday life. For hard real-time systems, computing correct results is not the only requirement. In addition, these results must be produced within pre-determined deadlines. Designers must compute the worst-case execution times (WCET) of the tasks composing the system, and guarantee that they meet the required timing constraints. Standard static WCET estimation techniques establish a WCET bound from an analysis of the machine code, taking into account additional flow information provided at source code level, either by the programmer or from static code analysis. Precise flow information helps produce tighter WCET bounds, hence limiting over-provisioning the system. However, flow information is difficult to maintain consistent through the optimizations applied by a compiler, and the majority of real-time systems simply do not apply any optimization. Vectorization is a powerful optimization that exploits data-level parallelism present in many applications, using the SIMD (single instruction multiple data) extensions of processor instruction sets. Vectorization is a mature optimization, and it is key to the performance of many systems. Unfortunately, it strongly impacts the control flow structure of functions and loops, and makes it more difficult to trace flow information from high-level down to machine code. For this reason, as many other optimizations, it is overlooked in real-time systems. In this paper, we propose a method to trace and maintain flow information from source code to machine code when vectorization optimization is applied. WCET estimation can benefit from this traceability. We implemented our approach in the LLVM compiler. In addition, we show through measurements on single-path programs that vectorization improves not only average-case performance but also WCETs. The WCET improvement ratio ranges from 1.18x to 1.41x depending on the target architecture on a benchmark

关键词： Optimization Real-time systems Estimation parallel processing Transforms Yttrium Timing

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SEPS 2014: First international workshop on Software Engineering for parallel Systems (co-located with SPLASH 2014)

SEPS 2014: First international workshop on Software Engineer...

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2014 ACM SIGPLAN conference on Systems, Programming, and applications: Software for Humanity, SPLASH 2014

作者： Jannesari, Ali Wolf, Felix Tichy, Walter F. German Research School for Simulation Sciences RWTH Aachen University Germany Germany

ISBN: (纸本)9781450332088

The first international workshop on Software Engineering for parallel Systems (SEPS) will be held in Portland, Oregon, USA on October 21, 2014 and co-located with the ACM SIGPLAN conference on Systems, Programming, Languages and applications: Software for Humanity (SPLASH 2014). The purpose of this workshop is to provide a stable forum for researchers and practitioners dealing with compelling challenges of the software development life cycle on modern parallel platforms. The increased complexity of parallel applications on modern parallel platforms (e.g. multicore, manycore, distributed or hybrid) requires more insight into development processes, and necessitates the use of advanced methods and techniques supporting developers in creating parallel applications or parallelizing and reengineering sequential legacy applications. We aim to advance the state of the art in different phases of parallel software development, covering software engineering aspects such as requirements engineering and software specification;design and implementation;program analysis, profiling and tuning;testing and debugging. Copyright is held by the owner/author(s).

关键词： parallel programming

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Comparison of parallel Discrete Fourier Transform Implementations for Modelling of Optical Communication Systems 12

Comparison of Parallel Discrete Fourier Transform Implementa...

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12th IEEE international conference on Emerging eLearning Technologies and applications (ICETA)

作者： Grondzak, K. Kortis, P. Univ Zilina Fac Management Sci & Informat Dept Informat Zilina Slovakia Univ Zilina Fac Elect Engn Dept Telecommun & Multimedia Zilina Slovakia

ISBN: (纸本)9781479977406

Discrete Fourier Transform (DFT) is a well-known mathematical tool used for scientific and engineering purposes. One of the areas, where DFT is frequently used is digital signal processing (DSP). The application of DSP techniques is used in many diverse areas, from telecommunication sector through medical data processing up to modern home entertainment systems. Many of the DSP applications require real time data processing. In the scientific area the DFT is frequently used for modelling of electrical circuits, communication systems and other areas, where the direct (from time domain to frequency domain) and inverse transformations are performed. This contribution presents two available libraries to accelerate DFT using parallelization. Presented libraries are implemented for use on recent computer processors (Central processing Unit, CPU), as well as for the calculation of the graphics cards (Graphic processing Unit, GPU). We briefly introduce the libraries and then present the comparison of their performance.

关键词： discrete Fourier transform Modeling Digital signal processing telecommunications sector COMMUNICATION SYSTEMS Libraries OPTICAL COMMUNICATION SYSTEMS Desmoplakin Gene graphic images central processing units Optical communication TIME-DOMAIN Frequency-domain analysis PROCESSOR

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Priority-based Resource Scheduling in distributed Stream processing Systems for Big Data applications 14

Priority-based Resource Scheduling in Distributed Stream Pro...

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IEEE/ACM 7th international conference on Utility and Cloud Computing (UCC)

作者： Bellavista, Paolo Corradi, Antonio Reale, Andrea Ticca, Nicola Univ Bologna Dept Comp Sci & Engn Bologna Italy

ISBN: (纸本)9781479978816

distributed Stream processing Systems (DSPSs) are attracting increasing industrial and academic interest as flexible tools to implement scalable and cost-effective on-line analytics applications over Big Data streams. Often hosted in private/public cloud deployment environments, DSPSs offer datastream processing services that transparently exploit the distributed computing resources made available to them at runtime. Given the volume of data of interest, possible (hard/soft) real-time processing requirements, and the time-variable characteristics of input datastreams, it is very important for DSPSs to use smart and innovative scheduling techniques that allocate computing resources properly and avoid static over-provisioning. In this paper, we originally investigate the suitability of exploiting application-level indications about differentiated priorities of different stream processing tasks to enable application-specific DSPS resource scheduling, e.g., capable of re-shaping processing resources in order to dynamically follow input data peaks of prioritized tasks, with no static over-provisioning. We originally propose a general and simple technique to design and implement priority-based resource scheduling in flow-graph-based DSPSs, by allowing application developers to augment DSPS graphs with priority metadata and by introducing an extensible set of priority schemas to be automatically handled by the extended DSPS. In addition, we show the effectiveness of our approach via its implementation and integration in our Quasit DSPS and through experimental evaluation of this prototype on a real-world stream processing application of Big Data vehicular traffic analysis.

关键词： distributed Stream processing Big Data Priority-based Resource Scheduling Application-level and Application-specific Scheduling Cloud Computing Optimization Vehicular Traffic Analysis

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