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检索条件"任意字段=27th International Conference on Parallel and Distributed Computing, Euro-Par 2021"

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A New parallel Methodology for the Network Analysis of COVID-19 Data

A New Parallel Methodology for the Network Analysis of COVID...

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26th international conference on parallel and distributed computing (euro-par)

作者： Agapito, Giuseppe Milano, Marianna Cannataro, Mario Magna Graecia Univ Catanzaro Data Analyt Res Ctr I-88100 Catanzaro Italy Magna Graecia Univ Catanzaro Dept Med & Surg Sci I-88100 Catanzaro Italy Magna Graecia Univ Catanzaro Dept Legal Econ & Social Sci I-88100 Catanzaro Italy

ISBN: (纸本)9783030715939;9783030715922

Coronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across the world. In this article, we present a new methodology for network-based analysis of Italian COVID-19 data. the methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data;(ii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships;(iii) the discovering communities of regions that show similar behaviour. the methodology is general and can be applied to world-wide data about COVID-19. Experiments was performed on real datasets about Italian regions, and they although the limited size of the Italian COVID-19 dataset, a quite linear speed-up was obtained up to six cores.

关键词： COVID-19 Network analysis parallel computing

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Performance Evaluation of Java/PCJ Implementation of parallel Algorithms on the Cloud

Performance Evaluation of Java/PCJ Implementation of Paralle...

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26th international conference on parallel and distributed computing (euro-par)

作者： Nowicki, Marek Gorski, Lukasz Bala, Piotr Nicolaus Copernicus Univ Torun Fac Math & Comp Sci Torun Poland Univ Warsaw Interdisciplinary Ctr Math & Computat Modeling Warsaw Poland

ISBN: (纸本)9783030715939;9783030715922

Cloud resources are more often used for large scale computing and data processing. However, the usage of the cloud is different than traditional High-Performance computing (HPC) systems and both algorithms and codes have to be adjusted. this work is often time-consuming and performance is not guaranteed. To address this problem we have developed the PCJ library (parallel computing in Java), a novel tool for scalable high-performance computing and big data processing in Java. In this paper, we present a performance evaluation of parallel applications implemented in Java using the PCJ library. the performance evaluation is based on the examples of highly scalable applications that run on the traditional HPC system and Amazon AWS Cloud. For the cloud, we have used Intel x86 and ARM processors running Java codes without changing any line of the program code and without the need for time-consuming recompilation. Presented applications have been parallelized using the PGAS programming model and its realization in the PCJ library. Our results prove that the PCJ library, due to its performance and ability to create simple portable code, has great promise to be successful for the parallelization of various applications and run them on the cloud with a similar performance as for HPC systems.

关键词： Cloud computing parallel computing Performance evaluation Java PCJ HPC Cloud

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HighPerMeshes - A Domain-Specific Language for Numerical Algorithms on Unstructured Grids

HighPerMeshes - A Domain-Specific Language for Numerical Alg...

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26th international conference on parallel and distributed computing (euro-par)

作者： Alhaddad, Samer Foerstner, Jens Groth, Stefan Gruenewald, Daniel Grynko, Yevgen Hannig, Frank Kenter, Tobias Pfreundt, Franz-Josef Plessl, Christian Schotte, Merlind Steinke, thomas Teich, Juergen Weiser, Martin Wende, Florian Paderborn Univ Paderborn Ctr Parallel Comp Paderborn Germany Paderborn Univ Dept Comp Sci Paderborn Germany Paderborn Univ Dept Elect Engn Paderborn Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Hardware Software Codesign Dept Comp Sci Erlangen Germany Fraunhofer Inst Techno & Wirtschaftsmath Kaiserslautern Germany Zuse Inst Berlin Berlin Germany

ISBN: (纸本)9783030715939;9783030715922

Solving partial differential equations on unstructured grids is a cornerstone of engineering and scientific computing. Nowadays, heterogeneous parallel platforms with CPUs, GPUs, and FPGAs enable energy-efficient and computationally demanding simulations. We developed the HighPerMeshes C++-embedded Domain-Specific Language (DSL) for bridging the abstraction gap between the mathematical and algorithmic formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different parallel programming and runtime models on the other hand. thus, the HighPerMeshes DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HighPerMeshes DSL, and demonstrate its usage with three examples, a Poisson and monodomain problem, respectively, solved by the continuous finite element method, and the discontinuous Galerkin method for Maxwell's equation. the mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented. Finally, the achievable performance and scalability are demonstrated for a typical example problem on a multi-core CPU cluster.

关键词： Domain-specific language Numerical algorithms Unstructured grids parallel computing

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DAG-Based Efficient parallel Scheduler for Blockchains: Hyperledger Sawtooth as a Case Study 29th

DAG-Based Efficient Parallel Scheduler for Blockchains: Hype...

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29th international conference on parallel and distributed computing (euro-par)

作者： Piduguralla, Manaswini Chakraborty, Saheli Anjana, parwat Singh Peri, Sathya Indian Inst Technol Hyderabad Kandi 502284 Telangana India

ISBN: (纸本)9783031396977;9783031396984

Blockchain technology is a distributed, decentralized, and immutable ledger system. It is the platform of choice for managing smart contract transactions (SCTs). Smart contracts are pieces of code that capture business agreements between interested parties and are commonly implemented using blockchains. A block in a blockchain contains a set of transactions representing changes to the system and a hash of the previous block. the SCTs are executed multiple times during the block production and validation phases across the network. In most of the existing blockchains, transactions are executed sequentially. In this work, we propose a parallel direct acyclic graph (DAG) based scheduler module for concurrent execution of SCTs. this module can be seamlessly integrated into the blockchain framework, and the SCTs in a block can be executed efficiently, resulting in higher throughput. the dependencies among the SCTs of a block are represented as a DAG data structure which enables parallel execution of the SCTs. Furthermore, the DAG data structure is shared with block validators, allowing resource conservation for DAG creation across the network. To ensure secure parallel execution, we design a secure validator capable of validating and identifying incorrect DAGs shared by malicious block producers. For evaluation, our framework is implemented in Hyperledger Sawtooth V1.2.6. the performance across multiple smart contract applications is measured for the various schedulers. We observed that our proposed executor exhibits a 1.58 times performance improvement on average over serial execution.

关键词： Smart Contract Executions Blockchains Hyperledger Sawtooth parallel Scheduler

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Accelerating Nested Data parallelism: Preserving Regularity 26th

Accelerating Nested Data Parallelism: Preserving Regularity

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26th international conference on parallel and distributed computing (euro-par)

作者： van den Haak, Lars B. McDonell, Trevor L. Keller, Gabriele K. de Wolff, Ivo Gabe Eindhoven Univ Technol Eindhoven Netherlands Univ Utrecht Utrecht Netherlands

ISBN: (纸本)9783030576752;9783030576745

Irregular nested data-parallelism is a powerful programming model which enables the expression of a large class of parallel algorithms. However, it is notoriously difficult to compile such programs to efficient code for modern parallel architectures. Regular data-parallelism, on the other hand, is much easier to compile to efficient code, but too restricted to express some problems conveniently or in a manner to exploit the full parallelism. We extend the regular data-parallel programming model to allow for the parallel execution of array-level conditionals and iterations over irregular nested structures, and present two novel static analyses to optimise the code generated for these programs which reduces the costs of this more powerful irregular model. We present benchmarks to support our claim that these extensions are effective as well as feasible, as they enable to exploit the full parallelism of an important class of algorithms, and together with our optimisations lead to an improvement in absolute performance over an implementation limited to exploiting only regular parallelism.

关键词： Arrays GPGPU Nested data parallelism parallel functional programming

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Load Balancing Optimization for Transformer in distributed Environment 27

Load Balancing Optimization for Transformer in Distributed E...

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27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Ma, Delu Lei, Zhou Chen, Shengbo Wang, Peng School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665408783

In recent years, the demand for artificial intelligence applications has increased dramatically. Complex models can promote machine learning to achieve excellent results, but computing efficiency has gradually reached a bottleneck. therefore, more researchers are exploring the improvement of the efficiency of intelligent computing systems. distributed machine learning can improve the efficiency of model training and inference, but problems such as communication delay and load imbalance between computing nodes still exist. In the multi-GPU distributed computing environment, this paper takes the vision field algorithm VIT (vision transformer) as the optimization object, which has the advantage of convenient parallel training, and proposes several related solutions. Firstly, the parameter server is used as the system logic architecture and in order to reduce the idleness of the computing devices during the training process, the device working status query mechanism is designed to realize load balancing. Secondly, combined with the pre-trained small VIT algorithm model, semi-asynchronous communication method is proposed to reduce the communication overhead of computing devices and accelerate global convergence. the results of this experiment carried out in the existing distributed environment has demonstrated that compared with the existing synchronization method, the computational efficiency has been improved well under the premise of slightly reducing the accuracy. © 2021 IEEE.

关键词： Computation theory

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Large-Scale Network Embedding in Apache Spark 21

Large-Scale Network Embedding in Apache Spark

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27th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Lin, Wenqing Tencent Interact Entertainment Grp Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781450383325

Network embedding has been widely used in social recommendation and network analysis, such as recommendation systems and anomaly detection with graphs. However, most of previous approaches cannot handle large graphs efficiently, due to that (i) computation on graphs is often costly and (ii) the size of graph or the intermediate results of vectors could be prohibitively large, rendering it difficult to be processed on a single machine. In this paper, we propose an efficient and effective distributed algorithm for network embedding on large graphs using Apache Spark, which recursively partitions a graph into several small-sized subgraphs to capture the internal and external structural information of nodes, and then computes the network embedding for each subgraph in parallel. Finally, by aggregating the outputs on all subgraphs, we obtain the embeddings of nodes in a linear cost. After that, we demonstrate in various experiments that our proposed approach is able to handle graphs with billions of edges within a few hours and is at least 4 times faster than the state-of-the-art approaches. Besides, it achieves up to 4.25% and 4.27% improvements on link prediction and node classification tasks respectively. In the end, we deploy the proposed algorithms in two online games of Tencent with the applications of friend recommendation and item recommendation, which improve the competitors by up to 91.11% in running time and up to 12.80% in the corresponding evaluation metrics.

关键词： network embedding distributed computing graph partitioning

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***: Integrated parallel Density-Based Clustering through Locality-Sensitive Hashing 28th

***: Integrated Parallel Density-Based Clustering Through Lo...

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28th international conference on parallel and distributed computing (euro-par)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9783031125973;9783031125966

Locality-sensitive hashing (LSH) is an established method for fast data indexing and approximate similarity search, with useful parallelism properties. Although indexes and similarity measures are key for data clustering, little has been investigated on the benefits of LSH in the problem. Our proposition is that LSH can be extremely beneficial for parallelizing high-dimensional density-based clustering e.g., DBSCAN, a versatile method able to detect clusters of different shapes and sizes. We contribute to fill the gap between the advancements in LSH and density-based clustering. We show how approximate DBSCAN clustering can be fused into the process of creating an LSH index, and, through parallelization and fine-grained synchronization, also utilize efficiently available computing capacity. the resulting method, ***, can support a wide range of applications with diverse distance functions, as well as data distributions and dimensionality. We analyse its properties and evaluate our prototype implementation on a 36-core machine with 2-way hyper threading on massive data-sets with various numbers of dimensions. Our results show that *** effectively complements established state-of-the-art methods by up to several orders of magnitude of speed-up on higher dimensional datasets, with tunable high clustering accuracy through LSH parameters.

关键词： Clustering algorithms

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Recommender Systems based on parallel and distributed Deep Learning 27

Recommender Systems based on Parallel and Distributed Deep L...

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27th Pan-Hellenic conference on Progress in computing and Informatics with international participation, PCI 2023

作者： Stergiopoulos, Vaios Tousidou, Eleni Corral, Antonio Dept. of Electrical and Computer Eng. University of Thessaly Volos Greece Department of Informatics University of Almeria Almeria Spain

ISBN: (纸本)9798400716263

As individuals have become overloaded with information, Recommender Systems (RS) were created to provide machine generated recommendations. Significant advancements in RS have been made thanks to Machine Learning methods;Deep Learning (DL) in particular has become extremely popular. Despite the fact that Deep neural networks (DNNs) upgrade notably the performance of RS, they make them larger and more memory-intensive systems. To that end, the solution is adding (data or model) parallel and distributed algorithms to DL RS. In this paper, we present our large-scale, multi-staged, hybrid RS that processes a million-scale dataset, as well as the most noteworthy parallel or/and distributed DL systems. Finally, we outline directions regarding the future evolution of our RS by adding some features and ideas from such systems. © 2023 Owner/Author.

关键词： Recommender systems

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An Edge Attribute-Wise partitioning and distributed Processing of R-GCN Using GPUs

An Edge Attribute-Wise Partitioning and Distributed Processi...

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26th international conference on parallel and distributed computing (euro-par)

作者： Kibata, Tokio Tsukada, Mineto Matsutani, Hiroki Keio Univ 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9783030715939;9783030715922

GCN (Relational Graph Convolutional Network) is one of GNNs (Graph Neural Networks). the model tries predicting latent information by considering directions and types of edges in graph-structured data, such as knowledge bases. the model builds weight matrices to each edge attribute. thus, the size of the neural network increases linearly with the number of edge types. Although GPUs can be used for accelerating the R-GCN processing, there is a possibility that the size of weight matrices exceeds GPU device memory. To address this issue, in this paper, an edge attribute-wise partitioning is proposed for R-GCN. the proposed partitioning divides the model and graph data so that R-GCN can be accelerated by using multiple GPUs. Also, the proposed approach can be applied to sequential execution on a single GPU. Both the cases can accelerate the R-GCN processing with large graph data, where the original model cannot be fit into a device memory of a single GPU without partitioning. Experimental results demonstrate that our partitioning method accelerates R-GCN by up to 3.28 times using four GPUs compared to CPU execution for a dataset with more than 1.6 million nodes and 5 million edges. Also, the proposed approach can accelerate the execution even with a single GPU by 1.55 times compared to the CPU execution for a dataset with 0.8 million nodes and 2 million edges.

关键词： GPU R-GCN GNN Graph data Knowledge base

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