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Using Neuromorphic Hardware for the Scalable Execution of Massively parallel, Communication-Intensive algorithms 11

Using Neuromorphic Hardware for the Scalable Execution of Ma...

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11th IEEE/ACM international conference on Utility and Cloud Computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Blin, Louis Awan, Ahsan Javed Heinis, thomas Imperial Coll London Dept Comp London England

ISBN: (纸本)9781728103594

Neuromorphic hardware like SpiNNaker offers massive parallelism and efficient communication of small payloads to accelerate the simulation of spiking neurons in neural networks. In this paper, we demonstrate that this hardware is also beneficial for other for applications which require massive parallelism and the large-scale exchange of small messages. More specifically, we study the scalability of PageRank on SpiNNaker and compare it to an implementation on traditional hardware. In our experiments, we show that PageRank on SpiNNaker scales better than on traditional multicore architectures.

关键词： Neurons Hardware Neuromorphics Computational modeling Multicore processing Payloads

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Application Research of parallel Optimization Technology in Hydrological Model 4

Application Research of Parallel Optimization Technology in ...

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2019 4th international conference on Intelligent Computing and Signal processing, ICSP 2019

作者： Liu, Fang Zhou, Shuai College of Science Xian Shiyou University Xian710065 China

Hydrological model parameters are generally considered to be a simplified representation that characterizes hydrologic processes, As hydrological models continue to deepen the application of hydrological processes in the basin, they face enormous calculations. Meanwhile, in pursuit calibrating the model parameters by optimal algorithms for higher accuracy, the computation burden of optical techniques has become much heavier. therefore, in order to solve this problem of low efficiency of hydrological model calculation, this paper uses parallel PSO algorithm to calibrate the TOPMODEL model parameters, and then uses parallel computing to process the flow generation in each sub-basin. the results show that the daily runoff simulation value of tangnaihai hydrological station fits well with the measured hydrological process;Whether PSO or sub-basin all can improve computational efficiency by using parallel optimization techniques, the former and the latter increased by 3.22 and 2.57 times, respectively. the results provide a reference for further understanding the application of parallel computing in hydrological models. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Computational efficiency

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Towards Heterogeneous Network Alignment: Design and Implementation of a Large-Scale Data processing Framework 24th

Towards Heterogeneous Network Alignment: Design and Implemen...

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24th international European conference on parallel and Distributed Computing (Euro-Par)

作者： Milano, Marianna Veltri, Pierangelo Cannataro, Mario Guzzi, Pietro H. Magna Graecia Univ Catanzaro Dept Med & Surg Sci Catanzaro Italy Univ Catanzaro Data Analyt Res Ctr Catanzaro Italy

ISBN: (纸本)9783030105495;9783030105488

the importance of the use of networks to model and analyse biological data and the interplay of bio-molecules is widely recognised. Consequently, many algorithms for the analysis and the comparison of networks (such as alignment algorithms) have been developed in the past. Recently, many different approaches tried to integrate into a single model the interplay of different molecules, such as genes, transcription factors and microRNAs. A possible formalism to model such scenario comes from node coloured networks (or heterogeneous networks) implemented as node/edge-coloured graphs. Consequently, the need for the introduction of alignment algorithms able to analyse heterogeneous networks arises. To the best of our knowledge, all the existing algorithms are not able to mine heterogeneous networks. We propose a two-step alignment strategy that receives as input two heterogeneous networks (node-coloured graphs) and a similarity function among nodes of two networks extending the previous formulations. We first build a single alignment graph. then we mine this graph extracting relevant subgraphs. Despite this simple approach, the analysis of such networks relies on graph and subgraph isomorphism and the size of the data is still growing. therefore the use of high-performance data analytics framework is needed. We here present HetNetAligner a framework built on top of Apache Spark. We also implemented our algorithm, and we tested it on some selected heterogeneous biological networks. Preliminary results confirm that our method may extract relevant knowledge from biological data reducing the computational time.

关键词： Heterogeneous network Network alignment Apache Spark

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parallel multi-scale CNN for image denoising 19

Parallel multi-scale CNN for image denoising

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Proceedings of the 5th international conference on Communication and Information processing

作者： Taoling Zhao Yi Zhong Yuanli Wang Wuhan University of Technology Wuhan Hubei

ISBN: (纸本)9781450372589

Deep learning has achieved outstanding performance in the field of image processing. Inspired by the inception model, we propose a deep neural network convolving simultaneously at two different scales. We utilize the convolution kernel of different sizes to carry out the convolution, which can obtain the features of the image at different scales. the features of the output are no longer uniformly distributed, but converge the features with strong correlation. Batch normalization (BN) and residual learning (RL) are utilized to speed up the training process as well as enhance the denoising ability of model. the experimental results of three salt-and-pepper noise levels based on the public dataset BSDS500 show that the newly proposed method can obtain significantly superior performance than current state-of-the-art approaches and higher peak signal to Noise Ratio (PSNR) in salt-and-pepper noise removal.

关键词： feature extraction at different scales residual learning inception model batch normalization image denoising

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Big Data architectures Benchmark for Forecasting Electricity Consumption

Big Data Architectures Benchmark for Forecasting Electricity...

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international conference on Cloud Technologies and Applications (CloudTech)

作者： Houda Daki Asmaa El Hannani Hassan Ouahmane Laboratory of Information Technologies National School of Applied Sciences University of Chouaib Doukkali El Jadida Morocco

ISBN: (数字)9781728161754

ISBN: (纸本)9781728161761

Now a day, educational institutions present one of the highest power consuming sector due to their new activities and occupancy pattern. this enormous amount of energy consumption at the university need a huge effort to reduce it. Smart grid is among the efficient solution to save energy and balance supply and demand. For the same purpose, the National School of Applied Sciences of El Jadida-Morocco wants take advantage from smart grid to maintain the balance between energy production and consumption. Despite of all added value of this smart grid solution for the school, it has the issue of managing energy production surplus, because it cannot inject it into Moroccan electrical infrastructure neither store it using storage devices. So, to overcome this challenge the system need to predict electrical consumption to be able to produce exactly the same value. Recently, Big Data contributed very well in analysing electrical consumption data using many tools and advanced techniques. It process, interprets and analyzes huge quantity of data to make it more profitable and valuable. For that reason, the school will take refuge in Big data technology to implement a custom system to predict electrical energy consumption by analyze all factors that influence electrical energy use. In this paper, we propose a benchmark of the main Big Data architectures in the field and that will cover all electrical energy data processing from data collection, data storage, data analytic and data visualization. the aim of this benchmark is to choose the optimal architecture in term of fault tolerance, resource management, data storage and data modelling to forecast electricity consumption in educational institutions.

关键词： Data analysis Memory Computer architecture Big Data Benchmark testing Smart grids Resource management

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Optimizing Machine Learning algorithms on Multi-core and Many-core architectures using thread and Data Mapping 26

Optimizing Machine Learning Algorithms on Multi-core and Man...

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26th Euromicro international conference on parallel, Distributed, and Network-Based processing (PDP)

作者： Serpa, Matheus S. Krause, Arthur M. Cruz, Eduardo H. M. Navaux, Philippe O. A. Pasin, Marcelo Felber, Pascal Fed Univ Rio Grande Sul UFRGS Inst Informat Porto Alegre RS Brazil Univ Neuchatel Neuchatel Switzerland

ISBN: (纸本)9781538649756

Driven by the development of new technologies such as personal assistants or autonomous cars, machine learning has rapidly become one of the most active fields in computer science. the algorithms at the core of machine learning are notoriously demanding in terms of resources. It is therefore of paramount importance to optimize their operation on modern processors. Several approaches have been proposed to accelerate machine learning on GPUs and massively parallel computers, as well as dedicated ASICs. In this paper, we focus on Intel's multi-core Xeon and many-core accelerator Xeon Phi Knights Landing, which can host several hundreds of threads on the same CPU. In such architectures, thread and data mapping are keys for performance. We study the impact of mapping strategies, revealing that, with smart mapping policies, one can indeed significantly speed up machine learning applications on manycore architectures. Execution time was reduced by up to 25.2% and 18.5% on Intel Xeon and Xeon Phi KNL, respectively.

关键词： Machine learning thread mapping Data mapping Memory accesses

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Multi-BSP vs. BSP: A Case of Study for Dell AMD Multicores 26

Multi-BSP vs. BSP: A Case of Study for Dell AMD Multicores

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26th Euromicro international conference on parallel, Distributed, and Network-Based processing (PDP)

作者： Trabes, Guillermo Gil-Costa, Veronica Printista, Marcela Marin, Mauricio Univ Nacl San Luis Dept Informat LIDIC San Luis Argentina Univ Nacl San Luis San Luis Argentina CCT San Luis CONICET San Luis Argentina Univ Santiago Chile CeBiB Santiago Chile

ISBN: (纸本)9781538649756

Computer models have been used as a bridge between parallel algorithms and hardware architectures. the Bulk-Synchronous parallel (BSP) is a well-known computing model originally devised for distributed algorithms running on clusters of single-core processors. the Multi-BSP model, that extends the classic BSP model, was recently proposed for multi-core processors. However, this model - implemented through the MulticoreBSP-for-C library-presents some restrictions such as the explicit synchronizations between the cores, introducing some challenges on which the hardware characteristics should be taken into account to properly model the parallel algorithms. therefore, we explore the suitability of these models for the Dell multi-core architecture. the objectives of this contribution are twofold. First, we model two different multi-core Dell architectures. Second, we show that a simple model with few parameters can be easily adapted to each Dell platform rather than complex models which tends to use tricky hardware parameters.

关键词： parallel computing models bulk-synchronous parallel - BSP Multi-BSP Dell multi-cores

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An Empirical Comparison of k-Shortest Simple Path algorithms on Multicores 18

An Empirical Comparison of <i>k</i>-Shortest Simple Path Alg...

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47th international conference on parallel processing (ICPP)

作者： Ajwani, Deepak Duriakova, Erika Hurley, Neil Meyer, Ulrich Schickedanz, Alexander Nokia Bell Labs Dublin Ireland Univ Coll Dublin Sch Comp Sci Insight Ctr Data Analyt Dublin Ireland Goethe Univ Frankfurt Germany

ISBN: (纸本)9781450365109

We consider the loop less k-shortest path (KSP) problem. Although this problem has been studied in the sequential setting for at least the last two decades, no good parallel implementations are known. In this paper, we provide (i) a first systematic empirical comparison of various KSP algorithms and heuristic optimisations, (ii) carefully engineer various parallel implementations of these sequential algorithms and (iii) perform an extensive study of these parallel implementations on a range of graph classes and multicore architectures to determine the best algorithm and parallelization strategy for different graph classes. We find that even though the worst-case complexity of the best undirected KSP algorithm O (k (m + n log n)) is significantly better than that of the popular and considerably simpler directed KSP algorithm O(kn(m + n log n)), the two algorithms are fairly competitive in terms of their empirical performance on small diameter graphs. Furthermore, we show that a few simple optimisations help to bridge the gap between these KSP algorithms even more. However, on moderate to large diameter graphs, the undirected KSP algorithm is considerably faster than the directed algorithms, both in sequential and parallel settings. In terms of the parallelisation strategy, simply replacing the shortest path subroutine by parallel A-stepping algorithm can provide a good speed-up for many KSP algorithms on random graphs. In contrast, for graphs with skewed degree distribution, a more complex strategy of parallelizing the different deviations and then parallelizing the shortest path computation inside the deviations with the remaining threads, provides a better performance.

关键词： Algorithm Engineering parallel k-Shortest Path parallel Graph algorithms parallel Single Source Shortest Path

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An Axiomatization for BSP algorithms 18th

An Axiomatization for BSP Algorithms

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Marquer, Yoann Gava, Frederic Univ Paris East Lab Algorithms Complex & Log LACL Creteil France

ISBN: (纸本)9783030050573;9783030050566

the Gurevich's thesis stipulates that sequential abstract state machines (ASMS) capture the essence of sequential algorithms. On another hand, the bulk-synchronous parallel (BSP) bridging model is a well known model for HPC algorithm design. It provides a conceptual bridge between the physical implementation of the machine and the abstraction available to a programmer of that machine. the assumptions of the BSP model are thus provide portable and scalable performance predictions on most HPC systems. We follow Gurevich's thesis and extend the sequential postulates in order to intuitively and realistically capture BSP algorithms.

关键词： BSP ASM parallel algorithm HPC Postulates Cost model

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Large-Scale Data-Driven Financial Risk Modeling using Big Data Technology 5

Large-Scale Data-Driven Financial Risk Modeling using Big Da...

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11th IEEE/ACM international conference on Utility and Cloud Computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Stockinger, Kurt Heitz, Jonas Bundi, Nils Breymann, Wolfgang Zurich Univ Appl Sci Winterthur Switzerland

ISBN: (纸本)9781538655023

Real-time financial risk analytics is very challenging due to heterogeneous data sets within and across banks worldwide and highly volatile financial markets. Moreover, large financial organizations have hundreds of millions of financial contracts on their balance sheets. Since there is no standard for modelling financial data, current financial risk algorithms are typically inconsistent and non-scalable. In this paper, we present a novel implementation of a real-world use case for performing large-scale financial risk analytics leveraging Big Data technology. Our performance evaluation demonstrates almost linear scalability.

关键词： Big Data parallel processing Computational Finance Data Modeling

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