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Proceedings of the 23rd IEEE/ACM International Symposium on distributed Simulation and real time Applications

作者： Sean Kane Sounak Gupta Philip A. Wilsey University of Cincinnati eBay Inc.

ISBN: (纸本)9781728129235

Synthetic workloads are commonly used to exercise simulation tools for performance, performance tuning, and scalability studies. Sometimes these workloads are simple streams of test data following various distributions and in other cases these workloads are generated by more complex, configurable systems. An example of the former is a stream of input events at different arrival rates that might be used to test the performance of an event queue data structure. An example of the latter is the PHOLD simulation model that is often used to contrast the performance implications of different design solutions in a parallel simulation engine. One of the key challenges for synthetic workloads is the question of setting the parameters so that the workload properly reflects the behavior of actual workloads. This paper collects profile data from multiple real-world discrete-event simulation models in multiple configurations and sizes from the ROSS and WARPED2 repositories. A principle focus of this paper is the capture and reporting of profiling data to understand event granularities and event profile data to assist in the configuration of synthetic discrete event model generators.

关键词： parallel discrete event simulation profiling simulation models PHOLD synthetic workload generation discrete event simulation

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15th International Conference on distributed Computing and Artificial Intelligence, DCAI 2018

15th International Conference on Distributed Computing and A...

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15th International Conference on distributed Computing and Artificial Intelligence, DCAI 2018

ISBN: (纸本)9783319946481

The proceedings contain 42 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: Classification of human body smell by learning vector quantization;a multi-objective evolutionary proposal for matching students to supervisors;real-time conditional commitment logic and duration communication interpreted systems;moodsically. Personal music management tool with automatic classification of emotions;arrhythmia detection using convolutional neural models;A novel sentence vector generation method based on autoencoder and Bi-directional LSTM;recognizing the order of four-scene comics by evolutionary deep learning;a big data platform for industrial enterprise asset value enablers;estimating the purpose of discard in mahjong to support learning for beginners;Fire detection using DCNN for assisting visually impaired people in IoT service environment;a Web-based micro-service architecture for comparing parallel implementations of dissimilarity measures;a parallel application of matheuristics in data envelopment analysis;prediction market index by combining financial time-series forecasting and sentiment analysis using soft computing;relaxation method of convolutional neural networks for natural language processing;simple and linear bids in multi-agent daily electricity markets: A preliminary report;Fault tolerance in DisCSPs: Several failures case;computer vision and the internet of things ecosystem in the connected home;distributed System Integration Driven by Tests;coordination platform for a swarm of mobile robots;distributed group analytical hierarchical process by consensus;development of agent predicting werewolf with deep learning;intelligent flight in indoor drones;Privacy preserving Expectation Maximization (EM) clustering construction;domain identification through sentiment analysis;automatic music generation by deep learning.

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15th International Conference on distributed Computing and Artificial Intelligence, DCAI 2018

15th International Conference on Distributed Computing and A...

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15th International Conference on distributed Computing and Artificial Intelligence, DCAI 2018

ISBN: (纸本)9783319996974

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A parallel framework for fuzzy membrane clustering based on P systems and improved PSO

A parallel framework for fuzzy membrane clustering based on ...

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International Conference on Mechatronics and Intelligent Robotics, ICMIR 2018

作者： Zhang, Chengfang Yue, Zhen Jin, Jie Yan, Dan Sichuan Police College LuzhouSichuan646000 China Information Security Center Beijing University of Posts and Telecommunications Beijing100876 China Chengdu Company Space Star Technology Co. LTD. ChengduSichuan610100 China

ISBN: (纸本)9783030002138

The traditional Fuzzy c-means (FCM) clustering algorithm is sensitive to initial seeds and noise. A novel fuzzy clustering algorithm is proposed by Peng Hong, which is named as Fuzzy-MC. However, due to the limitation of the serial architecture of current computer, the parallel and distributed computing characteristics of P system was not able to be exhibited. Therefore, Fuzzy-MC algorithm increases its computing time. To reduce the computing time, this paper proposes a GPU-based parallel Fuzzy-MC algorithm. In the parallel algorithm, block layer in CUDA programming model is used to represent the cells, while threads are used to realize the evolution and communication of the objects. Two artificial data sets and four real-life data sets from the UCI data sets are chosen to compare parallel version and the corresponding serial version. The computing time and clustering performance are used to demonstrate the effectiveness of the proposed algorithm. Compared with the Fuzzy-MC algorithm, parallel Fuzzy-MC algorithm not only ensures the clustering performance but also reduce its computing time. © Springer Nature Switzerland AG 2019.

关键词： Graphics processing unit

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RMalloc() and RpIPE(): A uGNI-based distributed remote memory allocator and access library for one-sided messaging 8

RMalloc() and RpIPE(): A uGNI-based distributed remote memor...

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8th International workshop on Runtime and Operating systems for Supercomputers, ROSS 2018 - In conjunction with HPDC 2018

作者： Wickramasinghe, Udayanga Lumsdaine, Andrew Indiana University School of Informatics and Computing United States Pacific Northwest National Laboratory United States

ISBN: (纸本)9781450358644

Optimizing communication is essential for high-performance computing because synchronization bottlenecks inhibit the overall performance and scalability of parallel applications. Today's cutting-edge computing hardware, as well as networking interfaces like Cray Aries/Gemini, features extremely low latency and high bandwidth remote memory access (RMA) operations for optimized data movement. However for any efficient data movement to occur between two logical processing units, software substrates must be able to properly exploit hardware resources for the underlying fabric. Overheads due to coarse granular synchronization and stalls during irregular access of remote memory regions may hint at two adverse effects of resource under-utilization in time and space. We introduce a uGNI-based distributed remote memory allocator called "rmalloc" which expands RDMA-enabled memory utilization, and a communication substrate called "rpipe" that tries to mitigate synchronization bottlenecks. Our UNIX-inspired RMA programming model is simple to use and equally applicable to both higher-level applications as well as lower-level runtime systems for enabling efficient data movement. Our micro-benchmark results suggest that "rmalloc" default next-fit allocator outperforms MPI-3.0 RMA by 1.5X and up to 6X in most cases, while other variants of "rmalloc" (i.e. best-fit, worst-fit) reduce external fragmentation and perform comparably or better than the default "rmalloc" allocator for irregular RMA. © 2018 ACM.

关键词： Synchronization

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Performance evaluation of full-cloud and edge-cloud architectures for Industrial IoT anomaly detection based on deep learning 2

Performance evaluation of full-cloud and edge-cloud architec...

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2nd IEEE International workshop on Metrology for Industry 4.0 and Internet of Things (IoT) (MetroInd4.0 and IoT)

作者： Ferrari, P. Rinaldi, S. Sisinni, E. Colombo, F. Ghelfi, F. Maffei, D. Malara, M. Univ Brescia Dept Informat Engn Brescia Italy 40Factory Srl Piacenza PC Italy Siemens Spa Milan Italy

ISBN: (纸本)9781728104294

One of the most interesting application of data analysis to industry is the real-time detection of anomalies during production. Industrial IoT paradigm includes all the components to realize predictive systems, like the anomaly detection ones. In this case, the goal is to discover patterns, in a given dataset, that do not resemble the "normal" behavior, to identify faults, malfunctions or the effects of bad maintenance. The use of complex neural networks to implement deep learning algorithm for anomaly detection is very common. The position of the deep learning algorithm is one of the main problem: this kind of algorithm requires both high computational power and data transfer bandwidth, rising serious questions on the system scalability. Data elaboration in the edge domain (i.e. close to the machine) usually reduce data transfer but requires to instantiate expensive physical assets. Cloud computing is usually cheaper but Cloud data transfer is expensive. In this paper a test methodology for the comparison of the two architectures for anomaly detection system is proposed. A real use case is described in order to demonstrate the feasibility. The experimental results show that, by means of the proposed methodology, edge and Cloud solutions implementing deep learning algorithms for industrial applications can be easily evaluated. In details, for the considered use case (with Siemens controller and Microsoft Azure platform) the tradeoff between scalability, communication delay, and bandwidth usage, has been studied. The results show that the full-cloud architecture can outperform the edge-cloud architecture when Cloud computation power is scaled.

关键词： Industry 4.0 distributed measurement systems Automation Networks Cloud computing MQTT

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G-thinker: A distributed Framework for Mining Subgraphs in a Big Graph

G-thinker: A Distributed Framework for Mining Subgraphs in a...

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International Conference on Data Engineering

作者： Da Yan Guimu Guo Md Mashiur Rahman Chowdhury M. Tamer Özsu Wei-Shinn Ku John C. S. Lui University of Alabama at Birmingham University of Waterloo Auburn University The Chinese University of Hong Kong

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Mining from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker that adopts a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph mining algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly-concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the CPU idle time. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://***/gthinker with detailed documentation.

关键词： Task analysis Data mining time complexity Programming Scheduling Throughput Social network services

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Qualitative Software Engineering and parallel Sorting Algorithm for real Numbers

Qualitative Software Engineering and Parallel Sorting Algori...

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作者： Syed, Md. Usman Gani University of Missouri - Kansas City

学位级别：M.S.

The research work consists of two parts. Part one is about qualitative software engineering and Event-B modelling for class and Use case diagrams. Now a days distributed and parallel applications are most popular and are used in applications like telecommunications and aircraft systems with complex computations. It is very important to define the exact properties and features of these systems along with the workflow. UML provides a great opportunity of modelling complex applications but lacks in providing the detailed semantics. In this work, we have provided the importance of implementation of specifications using formal methods like event-B through a simple example and verify its results using ProB. Later, we have defined the UML diagrams like use case and class diagrams in various scenarios and have performed the Event B modeling for these examples. The part one report had been published as a research paper to “The 2018 International Conference on Computational Science and Computational Intelligence 2018, Las Vegas, USA”. The paper was accepted to the conference with Paper Id “CSCI6051”. Part two is on parallel Sorting algorithm on real numbers. There are various best algorithms for sorting integers. The current research work applies the recent important results of serial sorting of real numbers in O(n√logn) time to the design of a parallel algorithm for sorting real numbers in O(log1+ϵn) time and O(nlogn/√loglogn) operations. This is the first NC algorithm known to take o(nlogn) operations for sorting real numbers.

关键词： Computer science

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Energy optimization of security-sensitive mixed-criticality applications for distributed real-time systems

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JOURNAL OF parallel AND distributed COMPUTING 2018年 117卷 115-126页

作者： Zhan, Jinyu Zhang, Xia Jiang, Wei Ma, Yue Jiang, Ke Univ Elect Sci & Technol China Sch Informat & Software Chengdu Sichuan Peoples R China Univ Texas Dallas Dept Comp Sci Richardson TX 75083 USA Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden

Existing studies on mixed-criticality systems are usually safety-oriented, which seriously ignore the security and energy related requirements. In this paper we are interested in the design of security-sensitive mixed-criticality real-time systems. We first establish the system model to capture security-critical applications in mixed-criticality systems. Higher security-criticality protection always results in significant time and energy overhead in mixed-criticality systems. Thus, this paper proposes a system-level design framework for energy optimization of security-sensitive mixed-criticality system with hard real-time constraints. Since the time complexity of finding optimal solutions grows exponentially as problem size grows, a GA (Genetic Algorithm) based on efficient heuristic algorithm is devised to address the system-level optimization problem. Extensive experiments and a real-life case study have been conducted to show the efficiency of the proposed technique, which can obtain balanced minimal energy consumption while satisfying strict security and timing constraints. The proposed approach can save up to 28.9% energy consumption compared with other three candidates. (C) 2018 Elsevier Inc. All rights reserved.

关键词： distributed real-time system Mixed-criticality Security Energy Scheduling System design

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Parana: A parallel Neural Architecture Considering Thermal Problem of 3D Stacked Memory

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IEEE TRANSACTIONS ON parallel AND distributed systems 2019年第1期30卷 146-160页

作者： Yin, Shouyi Tang, Shibin Lin, Xinhan Ouyang, Peng Tu, Fengbin Liu, Leibo Zhao, Jishen Xu, Cong Li, Shuangcheng Xie, Yuan Wei, ShaoJun Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China Univ Calif San Diego Dept Comp Engn La Jolla CA 92093 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA

Recent advances in deep learning (DL) have stimulated increasing interests in neural networks (NN). From the perspective of operation type and network architecture, deep neural networks can be categorized into full convolution-based neural network (ConvNet), recurrent neural network (RNN), and fully-connected neural network (FCNet). Different types of neural networks are usually cascaded and combined as a hybrid neural network (Hybrid-NN) to complete real-life cognitive tasks. Such hybrid-NN implementation is memory-intensive with large number of memory accesses, hence the performance of hybrid-NN is often limited by the insufficient memory bandwidth. A "3D + 2.5D" integration system, which integrates a high-bandwidth 3D stacked DRAM side-by-side with a highly-parallel neural processing unit (NPU) on a silicon interposer, overcomes the bandwidth bottleneck in hybrid-NN acceleration. However, intensive concurrent 3D DRAM accesses produced by the NPU lead to a serious thermal problem in 3D DRAM. In this paper, we propose a neural processor called Parana for hybrid-NN acceleration in consideration of thermal problem of 3D DRAM. Parana solves the thermal problem of 3D memory by optimizing both the total number of memory accesses and memory accessing behaviors. For memory accessing behaviors, Parana balances the memory bandwidth by spatial division mapping hybrid-NN onto computing resources, which efficiently avoids that masses of memory accesses are issued in a short time period. To reduce the total number of memory accesses, we design a new NPU architecture and propose a memory-oriented tiling and scheduling mechanism to exploit the maximum utilization of on-chip buffer. Experimental results show that Parana reduces the peak temperature by up to 54.72 degrees C and the steady temperature by up to 32.27 degrees C over state-of-the-art accelerators with 3D memory without performance degradation.

关键词： Neural processor hybrid neural networks thermal problem 3D memory

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