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Adaptive Nature-inspired Fog Architecture 2

Adaptive Nature-inspired Fog Architecture

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2nd IEEE international Conference on Fog and Edge Computing (ICFEC)

作者： Kimovski, Dragi Ijaz, Humaira Saurabh, Nishant Prodan, Radu Univ Klagenfurt Distributed Multimedia Syst Grp Klagenfurt Austria Univ Innsbruck Distributed & Parallel Syst Grp Innsbruck Austria Univ Sargodha Sargodha Pakistan

ISBN: (纸本)9781538664889

During the last decade, Cloud computing has efficiently exploited the economy of scale by providing low cost computational and storage resources over the Internet, eventually leading to consolidation of computing resources into large data centers. However, the nascent of the highly decentralized Internet of Things (IoT) technologies that cannot effectively utilize the centralized Cloud infrastructures pushes computing towards resource dispersion. Fog computing extends the Cloud paradigm by enabling dispersion of the computational and storage resources at the edge of the network in a close proximity to where the data is generated. In its essence, Fog computing facilitates the operation of the limited compute, storage and networking resources physically located close to the edge devices. However, the shared complexity of the Fog and the influence of the recent IoT trends moving towards deploying and interconnecting extremely large sets of pervasive devices and sensors, requires exploration of adaptive Fog architectural approaches capable of adapting and scaling in response to the unpredictable load patterns of the distributed IoT applications. In this paper we introduce a promising new nature-inspired Fog architecture, named SmartFog, capable of providing low decision making latency and adaptive resource management. By utilizing novel algorithms and techniques from the fields of multi-criteria decision making, graph theory and machine learning we model the Fog as a distributed intelligent processing system, therefore emulating the function of the human brain.

关键词： Computer architecture Cloud computing Optimization Decision making Edge computing Adaptation models Electronic mail

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Invited Paper: How Future Buildings Could Redefine distributed Computing 32

Invited Paper: How Future Buildings Could Redefine Distribut...

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32nd IEEE international parallel and distributed processing symposium Workshops, IPDPSW 2018

作者： Ngoko, Yanik Saintherant, Nicolas Cerin, Christophe Trystram, Denis Qarnot Computing Montrouge France University of Paris 13 Paris France University of Grenoble Alpes Grenoble France

ISBN: (纸本)9781538655559

One of the most important challenges for the Internet of Things (IoT) is the implementation of edge computing platforms. In order to improve the response time, the confidentiality, or the energy consumption of IoT applications, part of IoT services must be operated on servers, deployed close to connected devices. This said, much remains to be done for the practical realization of this vision. What are edge servers? Where will they be deployed? These are some questions that are poorly addressed in the literature. In this paper, we propose to build an edge computing framework around new datacenter models in which computing servers are seamlessly integrated in buildings. The datacenter models we are considering have been developed around the concept of data furnace. This concept has been implemented in European cities for district heating. Our paper introduces a new processing model in which edge and distributed cloud computing are operated with the same data furnace servers. We then discuss the challenges in the utilization of this model for edge computing. Our discussion deals with urban integration, service computing frameworks and performance issues. Finally, we end with an impact analysis of our proposition on the future of cloud computing. © 2018 IEEE.

关键词： distributed cloud

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Proceedings - 2018 IEEE 32nd international parallel and distributed processing symposium Workshops, IPDPSW 2018

Proceedings - 2018 IEEE 32nd International Parallel and Dist...

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32nd IEEE international parallel and distributed processing symposium Workshops, IPDPSW 2018

ISBN: (纸本)9781538655559

The proceedings contain 145 papers. The topics discussed include: user-transparent translation of machine instructions to programmable hardware;approximation algorithm for scheduling applications on hybrid multi-core machines with communications delays;large scale data centers simulation based on baseline test model;application performance on a cluster-booster system;transport-triggered soft cores;robustness of surface EMG classifiers with fixed-point decomposition on reconfigurable architecture;streaming architecture for large-scale quantized neural networks on an FPGA-based dataflow platform;high-level reliability evaluation of reconfiguration-based fault tolerance techniques;dynamic reconfiguration for real-time automotive embedded systems in fail-operational context;and rerooting trees increases opportunities for concurrent computation and results in markedly improved performance for phylogenetic inference.

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Rerooting Trees Increases Opportunities for Concurrent Computation and Results in Markedly Improved Performance for Phylogenetic Inference 32

Rerooting Trees Increases Opportunities for Concurrent Compu...

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32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Ayres, Daniel L. Cummings, Michael P. Univ Maryland Ctr Bioinformat & Computat Biol College Pk MD 20742 USA

ISBN: (纸本)9781538655559

parallel computing algorithms benefit from increases in concurrency when the hardware capacity is being under utilized. For likelihood-based molecular evolution inferences this can be due to small problem sizes, or because hardware capacity has increased beyond dataset sizes. A central concept in this domain is a bifurcating tree, which represents evolutionary relationships. The topology of the tree being evaluated directly affects the degree of parallelism that can be exploited by likelihood-based algorithms. For time-reversible evolutionary models we can reroot an unbalanced tree in order to make it more symmetric, without affecting the likelihood. Based on the reduction in number of concurrent operation sets, we define a best-case theoretical expectations, based on tree size and topology, for speedup due to rerooting which approaches 2-fold as the number of tip nodes increases for pectinate trees, and much higher values for some random topologies as the number of tip nodes increases. Empirical results using the NVIDIA CUDA implementation of the BEAGLE library confirm the merit of this approach. For pectinate trees we observe speedups of up to 1.93-fold due to rerooting and even larger speedups for random trees for the core likelihood-evaluation function in BEAGLE.

关键词： Bayes methods Biology computing Evolution (biology) Phylogeny Maximum likelihood estimation Multicore processing parallel programming

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Lyapunov-based online parameter estimation in continuous fluidized bed spray agglomeration processes 12

Lyapunov-based online parameter estimation in continuous flu...

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12th international-Federation-of-Automatic-Control (IFAC) symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS)

作者： Golovin, Ievgen Otto, Eric Duerr, Robert Palis, Stefan Kienle, Achim Otto von Guericke Univ D-39106 Magdeburg Germany Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Natl Res Univ Moscow Power Engn Inst Moscow Russia

Fluidized bed spray agglomeration is a particle formation process in many industrial applications, e.g. pharmaceutical and food processing. The properties of the formed agglomerates, like characteristic volume, significantly affect the product quality and can be affected by variation of certain operating parameters. Mathematical modeling not only provides an abstract characterization of the effects of those on the product properties but also supports thorough understanding of the underlying physical and chemical mechanisms. Moreover, it enables application of advanced process analysis, control and intensification schemes. As characteristic properties underlie variations within the ensemble of agglomerates the process can be described as a distributed parameter system, where the resulting model equations are partial differential equations. Adaption to experimental data requires the solution of inverse problems, which tend to be ill-conditioned. As an alternative approach, in this contribution an adaptive identification procedure is presented. Therefore, a modified plant model is run in parallel to the process and adaption rates are chosen based on a Lyapunov-function. The approach is validated in a parametric study for two scenarios: In the first, it is assumed that the structure of the dynamics is fully known, while in the second, this assumption does not hold. It is shown that the proposed approach allows to reconstruct unknown kinetic information of the process dynamics. (C) 2019, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Adaptive identification distributed parameter systems population balance equations partial differential equations

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distributed Symmetry Breaking in Graphs with Bounded Diversity 32

Distributed Symmetry Breaking in Graphs with Bounded Diversi...

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32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Barenboim, Leonid Maimon, Tzalik Open Univ Israel Comp Sci Raanana Israel

ISBN: (纸本)9781538643686

We consider the distributed synchronous message passing model, also known as the LOCAL model. In this model the input graph G represents a network, where each vertex in the graph is a processor and each edge is a communication line between two processors. Symmetry-breaking problems are among the most studied problems in this model [4], [7], [9]-[11], [13], [17], [18], [23], [24], [26]. In this paper we devise a general method for solving symmetry-breaking problems in graphs with bounded diversity. Roughly speaking, the diversity of a graph is the maximum number of maximal cliques a vertex belongs to. This general method uses a new approach which utilizes a structure called a connector. We build a series of such connectors, each of which simplifies the previous one by decreasing maximum clique size. Eventually, cliques becomes sufficiently small and have some additional properties that allow us to bound the maximum degree of the connectors. Then it becomes possible to employ efficient symmetry-breaking algorithms for bounded-degree graphs and extend the results to all the connectors in the series in backward order, until we reach a solution for the original graph. We use the ideas of this general method to achieve the following results. First, we devise an improved algorithm for maximal matching with running time of O(log(S)(D(G) + log*n)), where D(G) is the diversity of G and S(G) is the maximum clique size. The best currently-known deterministic result for general graphs is O(log(2) Delta log n) [13]. This result is also the best currently-known for graphs with bounded diversity, hence our result constitutes an improvement for graphs with D(G) = o(log Delta log n). Another algorithm of ours for the same problem has a running time of O(D(G)(2) + log*n). For graphs with D(G) = O(1) this shows a separation of complexities between the maximal matching problem and the maximal independent set problem. Indeed, in such graphs our algorithm computes a maximal matching withi

关键词： Maximal Matching Rulings Sets Message-Passing Model Clique Decomposition Connectors

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TensorFlow Doing HPC

TensorFlow Doing HPC

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Steven W. D. Chien Stefano Markidis Vyacheslav Olshevsky Yaroslav Bulatov Erwin Laure Jeffrey Vetter KTH Royal Institute of Technology Stockholm Sweden South Park Commons San Francisco USA Oak Ridge National Laboratory Oak Ridge USA

ISBN: (纸本)9781728135113

TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware. While TensorFlow has been initially designed for developing Machine Learning (ML) applications, in fact TensorFlow aims at supporting the development of a much broader range of application kinds that are outside the ML domain and can possibly include HPC applications. However, very few experiments have been conducted to evaluate TensorFlow performance when running HPC workloads on supercomputers. This work addresses this lack by designing four traditional HPC benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG) solver and Fast Fourier Transform (FFT). We analyze their performance on two supercomputers with accelerators and evaluate the potential of TensorFlow for developing HPC applications. Our tests show that TensorFlow can fully take advantage of high performance networks and accelerators on supercomputers. Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical communication bandwidth on our testing platform. We find an approximately 2×, 1.7× and 1.8× performance improvement when increasing the number of GPUs from two to four in the matrix-matrix multiply, CG and FFT applications respectively. All our performance results demonstrate that TensorFlow has high potential of emerging also as HPC programming framework for heterogeneous supercomputers.

关键词： Graphics processing units Task analysis Supercomputers Servers Programming Kernel

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Threaded Accurate Matrix-Matrix Multiplications with Sparse Matrix-Vector Multiplications 32

Threaded Accurate Matrix-Matrix Multiplications with Sparse ...

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32nd IEEE international parallel and distributed processing symposium (IPDPS)

作者： Ichimura, Shuntaro Ogita, Takeshi Katagiri, Takahiro Nagai, Toru Ozaki, Katsuhisa Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi Japan Tokyo Womans Christian Univ Div Math Sci Suginami Ku 2-6-1 Zempukuji Tokyo Japan Nagoya Univ Informat Technol Ctr Chikusa Ku Furo Cho Nagoya Aichi Japan Shibaura Inst Technol Coll Syst Engn & Sci Minuma Ku 307 Fukasaku Saitama Saitama Japan

ISBN: (纸本)9781538655559

Basic Linear Algebra Subprograms (BLAS) is a frequently used numerical library for linear algebra computations. However, it places little emphasis on computational accuracy, especially with respect to the accuracy assurance of the results. Although some algorithms for ensuring the computational accuracy of BLAS operations have been studied, there is a need for performance evaluation in advanced computer architectures. In this study, we parallelize high-precision matrix-matrix multiplication using thread-level parallelism. In addition, we conduct a performance evaluation from the viewpoints of execution speed and accuracy. We implement a method to convert dense matrices into sparse matrices by exploiting the nature of the target algorithm and adapting sparse-vector multiplication. Results obtained using the FX100 supercomputer system at Nagoya University indicate that (1) implementation with the ELL format achieves 1.43x speedup and (2) a maximum of 38x speedup compared to conventional implementation for dense matrix operations with dgemm.

关键词： component Accuracy assurance High-precision matrix-matrix multiplication Thread parallelism Error-free transformation Sparse matrix-vector multiplication

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Cymbalo: An Efficient Graph processing Framework for Machine Learning 16

Cymbalo: An Efficient Graph Processing Framework for Machine...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Tian, Xinhui Xie, Biwei Zhan, Jianfeng Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728111414

Due to the growth of data scale, distributed machine learning has become more important than ever. Some recent work, like TuX(2), show promising prospect in dealing with distributed machine learning by leveraging the power of graph computation, but still leave some key problems unsolved. In this paper, we propose Cymbalo, a new distributed graph processing framework for large-scale machine learning algorithms. To satisfy the specific characteristics of machine learning, Cymbalo employs a heterogeneity-aware data model, a hybrid computing model and a vector-aware programming model, to ensure small memory footprint, good computation efficiency and expressiveness. The experiment results show that Cymbalo outperforms Spark by 2.4x-3.2x, and PowerGraph by up to 5.8x. Moreover, Cymbalo can also outperform Angel, a recent parameter server system, by 1.6x-2.1x.

关键词： Large-scale machine learning Graph computation distributed processing Spark

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A Revised DNA Computing Model of Inversion and Division over Finite Field GF(2ⁿ) 16

A Revised DNA Computing Model of Inversion and Division over...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Li, Yongnan Peoples Publ Secur Univ China Sch Criminal Invest & Counter Terrorism Beijing 100038 Peoples R China

ISBN: (纸本)9781728111414

This paper proposes a DNA computing model to calculate both inversion and division over finite field GF(2(n)). It is designed based on the previous works by combining modular-multiplication into the process of computing inversion. It adds the function of computing division comparing with the previous model of inversion. The computation tiles performing 9 different functions assemble into the seed configuration with inputs to figure out the result. This model requires 6857 types of computation tiles and 11 types of boundary tiles.

关键词： Division Inversion Finite Field GF(2(n)) Tile Assembly Model

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