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12th international Conference on Hybrid Artificial Intelligent systems, HAIS 2017

12th International Conference on Hybrid Artificial Intellige...

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12th international Conference on Hybrid Artificial Intelligent systems, HAIS 2017

ISBN: (纸本)9783319596495

The proceedings contain 60 papers. The special focus in this conference is on Hybrid Artificial Intelligent systems. The topics include: Word embedding based event detection on social media;sentiment classification from multi-class imbalanced twitter data using binarization;an ontology for generalized disease incidence detection on twitter;concept discovery in graph databases;leveraging distributed representations of elements in triples for predicate linking;a review of distributed data models for learning;incorporating more scaled differences to differential evolution;topological evolution of financial network;optimization of joint sales potential using genetic algorithm;evolutionary multi-objective scheduling for anti-spam filtering throughput optimization;a hybrid diploid genetic based algorithm for solving the generalized traveling salesman problem;a novel hybrid nature-inspired scheme for solving a financial optimization problem;PSO with partial population restart based on complex network analysis;kernel density-based pattern classification in blind fasteners installation;training set fuzzification towards prediction improvement;on the impact of imbalanced data in convolutional neural networks performance;parallel implementation of a simplified semi-physical wildland fire spread model using openmp;a study on the noise label influence in boosting algorithms;radial-based approach to imbalanced data oversampling;simulation of a directional process by means of an anisotropic buffer operator;visual clustering approach for docking results from vina and autodock;an enhanced hierarchical traitor tracing scheme based on clustering algorithms and extraction of outliers from imbalanced sets.

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Scale-Out Acceleration for Machine Learning 17

Scale-Out Acceleration for Machine Learning

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50th Annual IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Park, Jongse Sharma, Hardik Mahajan, Divya Kim, Joon Kyung Olds, Preston Esmaeilzadeh, Hadi Georgia Inst Technol Alternat Comp Technol ACT Lab Atlanta GA 30332 USA Univ Calif San Diego Alternat Comp Technol ACT Lab La Jolla CA 92093 USA

ISBN: (纸本)9781450349529

The growing scale and complexity of Machine Learning (ML) algorithms has resulted in prevalent use of distributed general-purpose systems. In a rather disjoint effort, the community is focusing mostly on high performance single-node accelerators for learning. This work bridges these two paradigms and offers CoSMIC, a full computing stack constituting language, compiler, system software, template architecture, and circuit generators, that enable programmable acceleration of learning at scale. CoSMIC enables programmers to exploit scale-out acceleration using FPGAs and Programmable ASICs (P-ASICs) from a high-level and mathematical Domain-Specific Language (DSL). Nonetheless, CoSMIC does not require programmers to delve into the onerous task of system software development or hardware design. CoSMIC achieves three conflicting objectives of efficiency, automation, and programmability, by integrating a novel multi-threaded template accelerator architecture and a cohesive stack that generates the hardware and software code from its high-level DSL. CoSMIC can accelerate a wide range of learning algorithms that are most commonly trained using parallel variants of gradient descent. The key is to distribute partial gradient calculations of the learning algorithms across the accelerator-augmented nodes of the scale-out system. Additionally, CoSMIC leverages the parallelizability of the algorithms to offer multi-threaded acceleration within each node. Multi-threading allows CoSMIC to efficiently exploit the numerous resources that are becoming available on modern FPGAs/P-ASICs by striking a balance between multi-threaded parallelism and single-threaded performance. CoSMIC takes advantage of algorithmic properties of ML to offer a specialized system software that optimizes task allocation, role-assignment, thread management, and internode communication. We evaluate the versatility and efficiency of CoSMIC for 10 different machine learning applications from various domains. On av

关键词： Accelerator scale-out distributed cloud machine learning

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22nd ieee workshop on dependable parallel, distributed and network-centric systems (DPDNS 2017)

Proceedings - 2017 IEEE 31st International Parallel and Dist...

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proceedings - 2017 IEEE 31st international parallel and distributed Processing symposium Workshops, IPDPSW 2017 2017年 1237-1237页

作者： Avresky, Dimiter Maehle, Erik IRIANC Boston United States Munich Germany University of Luebeck Germany

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Brief announcement: On the parallel undecided-state dynamics with two colors 31

Brief announcement: On the parallel undecided-state dynamics...

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31st international symposium on distributed Computing, DISC 2017

作者： Clementi, Andrea Gualà, Luciano Pasquale, Francesco Scornavacca, Giacomo Università Tor Vergata di Roma Rome Italy Università degli Studi Dell'Aquila L'Aquila Italy

ISBN: (纸本)9783959770538

The Undecided-State Dynamics is a well-known protocol that achieves Consensus in distributed systems formed by a set of n anonymous nodes interacting via a communication network. We consider this dynamics in the parallel PULL communication model on the complete graph for the binary case, i.e., when every node can either support one of two possible colors or stay in the undecided state. Previous work in this setting only considers initial color configurations with no undecided nodes and a large bias (i.e.,(n)) towards the majority color. A interesting open question here is whether this dynamics reaches consensus quickly, i.e. within a polylogarithmic number of rounds. In this paper we present an unconditional analysis of the Undecided-State Dynamics which answers to the above question in the affirmative. Our analysis shows that, starting from any initial configuration, the Undecided-State Dynamics reaches a monochromatic configuration within O(log2 n) rounds, with high probability (w.h.p.). Moreover, we prove that if the initial configuration has bias Ω (√n log n), then the dynamics converges toward the initial majority color within O(log n) round, w.h.p. At the heart of our approach there is a new analysis of the symmetry-breaking phase that the process must perform in order to escape from (almost-)unbiased configurations. Previous symmetry-breaking analysis of consensus dynamics essentially concern sequential communication models (such as Population Protocols) and/or symmetric updated rules (such as majority rules). © Andrea Clementi, Luciano Gualà, Francesco Pasquale, and Giacomo Scornavacca;.

关键词： Dynamics

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An Analysis Tool to Methodically Scan Enterprise Scale Email systems: What's in Your Email System?

An Analysis Tool to Methodically Scan Enterprise Scale Email...

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international symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Olujuwon Dare Craig C. Douglas Level 3 Communications Broomfield CO USA University of Wyoming School of Energy Resources and Mathematics Laramie WY USA

ISBN: (纸本)9781538621639

Criminal activities, socially unacceptable behavior occurs in large enterprises (e.g., governments and global corporations) all too often undetected for years. In this paper we describe an easy to install and use web based intelligent parallel system to quickly search enterprise level email systems. The system is tailored for English, but we outline how the system can be adapted for other languages. We demonstrate the system's usefulness on the publically available Enron email data set.

关键词： Electronic mail Tools databases Receivers Software Government Standards organizations

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PaPar: A parallel Data Partitioning Framework for Big Data Applications

PaPar: A Parallel Data Partitioning Framework for Big Data A...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Hao Wang Jing Zhang Da Zhang Sarunya Pumma Wu-chun Feng Department of Computer Science Virginia Tech Blacksburg VA USA

Today, big data applications can generate large-scale data sets at an unprecedented rate; and scientists have turned to parallel and distributed systems for data analysis. Although many big data processing systems provide advanced mechanisms to partition data and tackle the computational skew, it is difficult to efficiently implement skew-resistant mechanisms, because the runtime of different partitions not only depends on input data size but also algorithms that will be applied on data. As a result, many research efforts have been undertaken to explore user-defined partitioning methods for different types of applications and algorithms. However, manually writing application-specific partitioning methods requires significant coding effort, and finding the optimal data partitioning strategy is particularly challenging even for developers that have mastered sufficient application knowledge. In this paper, we propose PaPar, a parallel data Partitioning framework for big data applications, to simplify the implementations of data partitioning algorithms. PaPar provides a set of computational operators and distribution strategies for programmers to describe desired data partitioning methods. Taking an input data configuration file and a workflow configuration file as the input, PaPar can automatically generate the parallel partitioning codes by formalizing the user-defined workflow as a sequence of key-value operations and matrix-vector multiplications, and efficiently mapping to the parallel implementations with MPI and MapReduce. We apply our approach on two applications: muBLAST, a MPI implementation of BLAST algorithms for biological sequence search; and PowerLyra, a computation and partitioning method for skewed graphs. The experimental results show that compared to the partitioning methods of applications, the codes generated by PaPar can produce the same data partitions with comparable or less partitioning time.

关键词： Partitioning algorithms distributed databases Runtime Indexes Algorithm design and analysis Data structures Big Data

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A parallel FastTrack Data Race Detector on Multi-core systems

A Parallel FastTrack Data Race Detector on Multi-core System...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Young Wn Song Yann-Hang Lee Western Digital Corp. Chandler Chandler AZ USA Computer Science and Engineering Arizona State University Tempe AZ

Detecting data races in multithreaded programs is critical to ensure the correctness of the programs. To discover data races precisely without false alarms, dynamic detection approaches are often applied. However, the overhead of the existing dynamic detection approaches, even with recent innovations, is still substantially high. In this paper, we present a simple but efficient approach to parallelize data race detection in multicore SMP (Symmetric Multiprocessing) machines. In our approach, data access information needed for dynamic detection is collected at application threads and passed to de-tection threads. The access information is distributed in a way that the operation performed by each detection thread is inde-pendent of that of other detection threads. As a consequence, the overhead caused by locking operations in data race detection can be alleviated and multiple cores can be fully utilized to speed up and scale up the detection. Furthermore, each detection thread deals with only its own assigned memory access region rather than the whole address space. The executions of detection threads can exploit the spatial locality of accesses leading to an improved cache performance. We have applied our parallel approach on the FastTrack algorithm and demon-strated the validity of our approach on an Intel Xeon machine. Our experimental results show that the parallel FastTrack detector, on average, runs 2.2 times faster than the original FastTrack detector on the 8 core machine.

关键词： Instruction sets Clocks Detectors Multicore processing Data structures Concurrent computing distributed databases

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HDSVM: A High Efficiency distributed SVM Framework over Data Stream

HDSVM: A High Efficiency Distributed SVM Framework over Data...

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IEEE international symposium on parallel and distributed Processing with Applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yan Hou Yijie Wang Xingkong Ma Li Cheng College of Computer National University of Defense Technology Changsha National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9781538637913

The application of Support Vector Machine (SVM) over data stream is growing with the increasing real-time processing requirements in classification field, like anomaly detection and real-time image processing. However, the dynamic live data with high volume and fast arrival rate in data streams make it challenging to apply SVM in data stream processing. Existing SVM implementations are mostly designed for batch processing and hardly satisfy the efficiency requirement of stream processing for its inherent complexity. To address the challenges, we propose a high efficiency distributed SVM framework over data stream (HDSVM), which consists of two main algorithms, incremental learning algorithm and distributed algorithm. Firstly, we propose a partial support vectors reserving incremental learning algorithm (PSVIL). By selecting a subset of support vectors based on their distances to classification hyperplane instead of the universal set to update SVM, the algorithm achieves lower time overhead while ensuring accuracy. Secondly, we propose a distribution remaining partition and fast aggregation distributed algorithm (DRPFA) for SVM. The real-time data is partitioned based on the original distribution with clustering instead of random partition, and historical support vectors are partitioned based on their distances to the classification hyperplane. The global hyperplane can be obtained by averaging the parameters of local hyperplanes due to the above partition strategy. Extensive experiments on Apache Storm show that the proposed HDSVM achieve lower time overhead and similar accuracy compared with the state-of-art. Speed-up ratio is increased by 2-8 times within 1% accuracy deviation.

关键词： Support vector machines Training distributed databases distributed algorithms Real-time systems Partitioning algorithms Streaming media

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A Lightweight Privacy Aware Friend Locator in Mobile Social Networks

A Lightweight Privacy Aware Friend Locator in Mobile Social ...

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IEEE international symposium on parallel and distributed Processing with Applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Tao Peng Qin Liu Guojun Wang Jianer Chen School of Computer Science and Educational Software Guangzhou University Guangzhou Guangdong Province P. R. China

ISBN: (纸本)9781538637913

Friend Locator is a popular application in Location Based Service (LBS) which notifies the mobile user if his friend is located in the nearby area. While users enjoy the services, location privacy disclosure has been a focus of security concern. Some privacy preserving schemes were proposed to protect user privacy. However, existing proposals have several limitations, such as incurring high communication overheads, providing limited privacy or requiring a fully-trusted third party (TTP). In this paper, we proposed a lightweight Privacy Aware Friend Locator (PAFL) scheme in Location-Based Social Networks (LBSNs) environment. The PAFL scheme preserves user location privacy utilizing the technology of Order Preserving Encryption (OPE), in which TTP is not required. The server performs proximity detection by comparison directly applied to the encrypted data, having no knowledge about the user's real location. The simulation results show that compared with some existing schemes the proposed PAFL scheme can greatly save the overheads of users and improve the efficiency of the system, while preserving users' privacy.

关键词： Privacy Encryption databases Social network services systems architecture

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SWhybrid: A Hybrid-parallel Framework for Large-Scale Protein Sequence Database Search

SWhybrid: A Hybrid-Parallel Framework for Large-Scale Protei...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Haidong Lan Weiguo Liu Yongchao Liu Bertil Schmidt Shandong University Jinan China Georgia Tech Atlanta USA Johannes Gutenberg University Mainz Germany

Computer architectures continue to develop rapidly towards massively parallel and heterogeneous systems. Thus, easily extensible yet highly efficient parallelization approaches for a variety of platforms are urgently needed. In this paper, we present SWhybrid, a hybrid computing framework for large-scale biological sequence database search on heterogeneous computing environments with multi-core or many-core processing units (PUs) based on the Smith- Waterman (SW) algorithm. To incorporate a diverse set of PUs such as combinations of CPUs, GPUs and Xeon Phis, we abstract them as SIMD vector execution units with different number of lanes. We propose a machine model, associated with a unified programming interface implemented in C++, to abstract underlying architectural differences. Performance evaluation reveals that SWhybrid (i) outperforms all other tested state-of-the-art tools on both homogeneous and heterogeneous computing platforms, (ii) achieves an efficiency of over 80% on all tested CPUs and GPUs and over 70% on Xeon Phis, and (iii) achieves utlization rates of over 80% on all tested heterogeneous platforms. Our results demonstrate that there is enough commonality between vector-like instructions across CPUs and GPUs that one can develop higher-level abstractions and still specialize with close-to-peak performance. SWhybrid is open-source software and freely available at https://***/turbo0628/swhybrid.

关键词： databases Computer architecture Performance evaluation Computational modeling Protein sequence Graphics processing units

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