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检索条件"任意字段=9th International Conference on Parallel and Distributed Computing, Applications and Technologies"

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Point cloud indexing using Big Data technologies

Point cloud indexing using Big Data technologies

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Kocon, Kevin Bormann, Pascal Tech Univ Darmstadt Fraunhofer IGD Darmstadt Germany

ISBN: (纸本)9781665439022

We present a system for scalable indexing of very large point cloud data in the cloud using common Big Data technologies. We adjusted the procedure for building the common modifiable-nested-octree data-structure to the Map-Reduce paradigm using Morton-Codes for fast point grouping and sampling. Our system requires only two shuffle operations independently of the size of the point cloud, thus enabling linear scalability with the number of cluster nodes. Combining the modifiable-nested-octree with a hash-based grid also allows our system to efficiently deal with data updates. Based on a series of experiments with freely available point cloud datasets of up to 82 billion points we show that our system outperforms the state-of-the-art by up to a factor of 3. As our system is cloud-native, even the largest point cloud datasets can be indexed efficiently. Storing the resulting data-structure in a distributed database enables fast range queries supporting level of detail for web-based rendering.

关键词： Point cloud indexing Cloud computing Map-Reduce

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EFFICIENT parallel ALGORIthMS FOR COUPLED FLUID-PARTICLE SIMULATION 9

EFFICIENT PARALLEL ALGORITHMS FOR COUPLED FLUID-PARTICLE SIM...

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9th international conference on Computational Methods for Coupled Problems in Science and Engineering, COUPLED PROBLEMS 2021

作者： Baldan, Giacomo Bellosta, Tommaso Guardone, Alberto Department of Aerospace Science and Technology Politecnico di Milano Via La Masa 34 Milano20156 Italy

Coupled fluid-particle simulations are routinely used in a variety of applications, ranging from respiratory droplet spreading to internal combustion engines, from ink-jet printing to in-flight ice accretion. the efficiency of parallel algorithms to simulate fluid-particle systems is strongly influenced by the different evolution of the flow and the particles dynamics. Indeed, a domain partitioning based on particle workload is possibly sub-optimal in terms of the number of fluid volume elements associated to each process. In this work, an efficient mesh partitioning based on graph representation is implemented. It can handle unstructured hybrid meshes composed by triangles and quadrilaterals in two spatial dimensions, and by tetrahedra, hexahedra, prisms, and pyramids in three dimensions. In order to obtain a domain decomposition to efficiently follow the particle trajectories, a preliminary solution is computed to suitably tag the fluid domain cells. the obtained weights represent the element probabilities to be crossed by particles. the algorithm is implemented using MPI distribute memory environment. the proposed approach is tested against reference cases for the coupled flow-particle simulation of ice accretion over 2D and 3D geometries. Two different cloud droplet impact test cases have been simulated: a NACA 0012 wing section and a NACA 64A008 swept horizontal tail. the computed collection efficiency compares fairly well with reference numerical and experimental data. the parallel efficiency of the algorithm is verified on a distributed memory cluster. © 2021 9th international conference on Computational Methods for Coupled Problems in Science and Engineering, COUPLED PROBLEMS 2021. All rights reserved.

关键词： Mesh generation

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VM BASED EVALUATION of the SCALABLE parallel MINIMUM SPANNING TREE ALGORIthM for PGAS MODEL 9

VM BASED EVALUATION of the SCALABLE PARALLEL MINIMUM SPANNIN...

引用

9th international conference "distributed computing and Grid technologies in Science and Education", GRID 2021

作者： Bejanyan, Vahag Astsatryan, Hrachya Institute for Informatics and Automation Problems The National Academy of Sciences Armenia

the minimum spanning tree problem has influential importance in computer science, network analysis, and engineering. However, the sequential algorithms become unable to process the given problem as the volume of the data representing graph instances overgrowing. Instead, the high-performance computational resources pursue to simulate large-scale graph instances in a distributed manner. Generally, the standard shared or distributed memory models like OpenMP and Message Passing Interface are applied to address the parallelization. Nevertheless, as an emerging alternative, the Partitioned Global Address Space model communicates in the form of asynchronous remote procedure calls to access distributed-shared memory, positively affecting the performance using overlapping communications and locality-aware structures. the paper presents a modification of the Kruskal algorithm for MST problems based on performance and energy-efficiency evaluation relying on emerging technologies. the algorithm evaluation shows great scalability within the server up to 16 vCPU and between the physical servers coupled with a connected weighted graph using different vertices, edges, and densities. © 2021 CEUR-WS. All rights reserved.

关键词： Message passing

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A parallel Chain Mail Approach for Scalable Spatial Data Interpolation

A Parallel Chain Mail Approach for Scalable Spatial Data Int...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Asratyan, Albert Sheikholeslami, Sina Vlassov, Vladimir KTH Royal Inst Technol Dept Comp Sci Stockholm Sweden

ISBN: (纸本)9781665439022

Deteriorating air quality is a growing concern that has been linked to many health-related issues. Its monitoring is a good first step to understanding the problem. However, it is not always possible to collect air quality data from every location. Various data interpolation techniques are used to assist with populating sparse maps with more context, but many of these algorithms are computationally expensive. this work introduces a three-step Chain Mail algorithm that uses kriging (without any modifications to the base algorithm) and achieves upto x100 execution time improvement with minimal accuracy loss (relative RMSE of 3%) by running concurrent interpolation executions. this approach can be described as a multiple-step parallel interpolation algorithm that includes specific regional border data manipulation for achieving greater accuracy. It does so by interpolating geographically defined data chunks in parallel and sharing the results with their neighboring nodes to provide context and compensate for lack of knowledge of the surrounding areas. Combined with a serverless cloud architecture, this approach opens doors to interpolating large data sets in a matter of minutes while remaining cost-efficient. the effectiveness of the three-step Chain Mail approach depends on the equal point distribution among all nodes and the resolution of the parallel configuration. In general, it offers a good balance between execution speed and accuracy.

关键词： distributed computing parallel Execution Data Interpolation Kriging Geostatistics Air Quality

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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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IT SOLUTIONS for JINR TASKS on the "GOVORUN" SUPERCOMPUTER 9

IT SOLUTIONS for JINR TASKS on the "GOVORUN" SUPERCOMPUTER

引用

9th international conference "distributed computing and Grid technologies in Science and Education", GRID 2021

作者： Podgainy, Dmitriy Belaykov, Dmitriy Nechaevsky, Andrey Streltsova, Oksana Vorontsov, Aleksey Zuev, Maksim Meshcheryakov Laboratory of Information Technologies JINR

the "Govorun" supercomputer is a heterogeneous computing system that contains computing architectures of different types, including graphics accelerators. the given architecture of the supercomputer allows users to choose optimal computing facilities for their tasks. To enhance the efficiency of solving user tasks, as well as to expand the efficiency of utilizing both computing resources and data processing and storage resources, a number of special IT solutions have been implemented on the "Govorun" supercomputer. A hierarchical hyperconverged data processing and storage system with a software-defined architecture is referred to the first type of IT solutions. the implementation of this system is caused by the fact that modern supercomputers are used not only as traditional computing environments for carrying out massively parallel calculations, but also as systems for Big Data analysis and artificial intelligence tasks that arise in different scientific and applied tasks. the second type of IT solutions lies in resource orchestration, which means that computational elements (CPU cores and graphics accelerators) and data storage elements (SSDs) form independent computing and data storage fields. Due to it, the user can allocate for his task the required number and type of compute nodes (including the required number of graphics accelerators), as well as the required volume and type of data storage systems. © 2021 CEUR-WS. All rights reserved.

关键词： Supercomputers

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LIFE CYCLE MANAGEMENT SERVICE for the COMPUTE NODES of the TIER1, TIER2 SITES (JINR) 9

LIFE CYCLE MANAGEMENT SERVICE for the COMPUTE NODES of the T...

引用

9th international conference "distributed computing and Grid technologies in Science and Education", GRID 2021

作者： Baranov, Aleksandr Golunov, Alexey Mitsyn, Valery Kashunin, Ivan Meshcheryakov Laboratory of Information Technologies Joint Institute for Nuclear Research 6 Joliot-Curie Moscow region Dubna 141980 Russia

Megascience experiments, such as CMS, ATLAS, ALICE, MPD, BM@N, etc., are served at the Meshcheryakov Laboratory of Information technologies (MLIT) of the Joint Institute for Nuclear Research (JINR) using the available computing infrastructure. To ensure the guaranteed and stable operation of the infrastructure under constant load conditions, the centralized and timely maintenance of software and the rapid introduction of new compute nodes are required. As a solution to this task, a life cycle management service (LCMS) was created;its purpose is to automate the process related to the software maintenance and commissioning of new computing resources. the paper will give an overview of the service and its components. © 2021 CEUR-WS. All rights reserved.

关键词： Grid computing

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Real Time Crime Data Analysis with Hadoop and MapReduce: A Case Study From Sri Lanka

Real Time Crime Data Analysis with Hadoop and MapReduce: A C...

引用

international conference on Information Technology Research (ICITR)

作者： Dhanuka Dayawansa Hamza Nauzad Sithara Kumarasingha Uthpala Isuranga Prasanna S. Haddela Samadhi Rathnayake Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Modern law enforcement faces significant challenges in managing and analyzing the exponential growth of crime data, which often includes unstructured, high-volume, and real-time information. Traditional relational database systems struggle to handle these complexities, limiting the ability of agencies to derive actionable insights for proactive measures. this research addresses these challenges by proposing a scalable distributed framework utilizing Hadoop and MapReduce for real-time crime data analysis. the objectives include enabling efficient ETL (Extract, Transform, Load) processes, implementing a robust star schema for structured data storage, and providing actionable insights through an integrated real-time dashboard. the methodology employs a multi-node Hadoop cluster for parallel processing, optimizing data integration and analysis capabilities. Results demonstrate significant improvements in processing speed, fault tolerance, and scalability, validated through the framework's application in Sri Lanka's crime data analysis. Findings reveal enhanced resource allocation, crime pattern identification, and operational efficiency for law enforcement. this research establishes a cost-effective, high-performance solution to modern criminological data challenges, with future potential for predictive analytics and machine learning integration.

关键词： Data analysis Law enforcement Scalability Fault tolerant systems Stars Predictive models parallel processing Real-time systems Resource management Predictive analytics

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JINR distributed INFORMATION and computing ENVIRONMENT: PARTICIPANTS, FEATURES and CHALLENGES 9

JINR DISTRIBUTED INFORMATION and COMPUTING ENVIRONMENT: PART...

引用

9th international conference "distributed computing and Grid technologies in Science and Education", GRID 2021

作者： Balashov, Nikita Kuprikov, Igor Kutovskiy, Nikolay Makhalkin, Alexandr Mazhitova, Yelena Pelevanyuk, Igor Semenov, Roman Meshcheryakov Laboratory of Information Technologies Joint Institute for Nuclear Research 6 Joliot-Curie Moscow region Dubna 141980 Russia Dubna State University 19 Universitetskaya str. Moscow region Dubna 141980 Russia Institute of Nuclear Physics 1 Ibragimova str. Almaty050032 Kazakhstan Plekhanov Russian University of Economics 36 Stremyanny per Moscow117997 Russia

the JINR distributed Information and computing Environment (DICE) was created to combine resources for solving common scientific tasks, as well as to distribute peak loads across the resources of partner organizations from the JINR Member States. To monitor the hardware resources and services of the growing DICE infrastructure, a system based on Prometheus and thanos was designed and deployed. the collected metrics, including the geographical location of the JINR DICE participants, are visualized using Grafana. Software distribution is carried out with the help of the CERN Virtual Machines File System. All these topics, as well as challenges and possible overcomes, are covered in detail. © 2021 CEUR-WS. All rights reserved.

关键词： Ceph Cloud computing Cloud integration DIRAC OpenNebula

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Prototyping of Low-Cost Configurable Sparse Neural Processing Unit with Buffer and Mixed-Precision Reshapeable MAC Array

Prototyping of Low-Cost Configurable Sparse Neural Processin...

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international conference on parallel and distributed Systems (ICPADS)

作者： Binyi Wu Wolfgang Furtner Bernd Waschneck Christian Mayr Faculty of Electrical and Computer Engineering Technische Universit¨at Dresden Dresden Germany Division of Power and Sensor Systems Infineon Technologies Munich Germany Centre for Tactile Internet with Human-in-the-loop (CeTI) Technische Universität Dresden Dresden Germany

More recently, it has become possible to run deep learning algorithms on edge devices such as microcontrollers due to continuous improvements in neural network optimization algorithms such as quantization and neural architecture search. Nonetheless, most of the embedded hardware available today still falls short of the requirements of running deep neural networks. As a result, specialized processors have emerged to improve the inference efficiency of deep learning algorithms. However, most are not for edge applications that require efficient and low-cost hardware. therefore, we design and prototype a low-cost configurable sparse Neural Processing Unit (NPU). the NPU has a built-in buffer and a reshapable mixed-precision multiply-accumulator (MAC) array. the computing and memory resources of the NPU are parameterized, and different NPUs can be derived. Besides, users can also conFigure the NPU at runtime to fully utilize the resources. In our experiments, the 200MHz NPU with only 32 MACs is more than 32 times faster than the 400MHzSTM32H7 when inferring MobileNet-Vl. Besides, the yielded NPUs can achieve roofline or even beyond roofline performance. the buffer and reshapeable MAC array push the NPU’s attainable performance to the roofline, while the feature of supporting sparsity allows the NPU to obtain performance beyond the roofline.

关键词： Deep learning Runtime Quantization (signal) Program processors Neural networks Random access memory Prototypes

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