Symmetry in Distributed Algorithms and Parallel Algorithms and Their Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6018

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, Saint Petersburg Electrotechnical University “LETI”, ul. Professora Popova 5, 197376 St. Petersburg, Russia
Interests: data mining; parallel algorithms; distributed systems

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Saint Petersburg Electrotechnical University “LETI”, ul. Professora Popova 5, 197376 St. Petersburg, Russia
Interests: high-performance computing; distributed computing; synchronization; multithreading; concurrent data structures; non-blocking synchronization; MPI; remote memory access; PGAS; performance engineering; microarchitectural optimization; compilers; LLVM; code optimization

E-Mail Website
Guest Editor
St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia
Interests: IoT security; security of wireless sensor networks; network security

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to all aspects of parallel and distributed computing, high-performance computing, and multithreading.  We also invite researchers and developers in such fields as performance engineering, code generation and optimization. This includes parallel programming libraries and languages, high-performance computing, concurrent algorithms and data structures, distributed architectures, tools, runtime-systems, and applications, modern compilers and algorithms for code optimization with particular focus on quality, performance, and scalability. We also expect that these Special Issues will discover the ideas of symmetry in these fields. Thus, the goal of the Special Issue is to improve understanding of the principles underlining parallel and distributed algorithms, to reveal the connection between high-performance and multithreading computing, as well as compiler optimization, and asymmetry above all.

Dr. Kholod Ivan
Dr. Alexey Paznikov
Dr. Vasily Desnitsky
Guest Editors

Manuscript Submission Information

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Keywords

  • high-performance computing
  • parallel programming
  • MPI
  • multithreading
  • concurrent data structures
  • synchronization
  • compiler optimization
  • software performance engineering
  • code generation
  • distributed computing
  • cloud, fog and edge computing
  • federated learning

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Published Papers (3 papers)

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Research

15 pages, 890 KiB  
Article
Efficient Sender-Based Message Logging Tolerating Simultaneous Failures with Always No Rollback Property
by Jinho Ahn
Symmetry 2023, 15(4), 816; https://doi.org/10.3390/sym15040816 - 28 Mar 2023
Viewed by 1199
Abstract
Most of the existing sender-based message logging protocols cannot commonly handle simultaneous failures because, if both the sender and the receiver(s) of each message fail together, the receiver(s) cannot obtain the recovery information of the message. This unfortunate situation may happen due to [...] Read more.
Most of the existing sender-based message logging protocols cannot commonly handle simultaneous failures because, if both the sender and the receiver(s) of each message fail together, the receiver(s) cannot obtain the recovery information of the message. This unfortunate situation may happen due to their asymmetric logging behavior. This paper presents a novel sender-based message logging protocol for broadcast network based distributed systems to overcome the critical constraint of the previous ones with the following three features. First, when more than one process crashes at the same time, the protocol enables the system to ensure the always no rollback property by symmetrically replicating the recovery information at each process or group member connected on a network. Second, it can make the first feature persist even if the general form of communication for the system is a combination of point-to-point and group ones. Third, the communication overhead resulting from the replication can be highly lessened by making full use of the capability of the standard broadcast network in both communication modes. Experimental outcomes verify that, no matter which communication patterns are applied, it can reduce about 4.23∼9.96% of the total application execution time against the latest enabling the traditional ones to cope with simultaneous failures. Full article
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18 pages, 2258 KiB  
Article
Study on Destructive Informational Impact in Unmanned Aerial Vehicles Intergroup Communication
by Egor Marinenkov, Sergei Chuprov, Nikita Tursukov, Iuliia Kim and Ilia Viksnin
Symmetry 2022, 14(8), 1580; https://doi.org/10.3390/sym14081580 - 1 Aug 2022
Viewed by 1503
Abstract
In this paper, we propose a novel approach to formalize the impact of malicious intergroup informational attacks toward a group of unmanned aerial vehicles communication. Infrequent but critical situations arise when an already authorized group member starts to transmit false data to other [...] Read more.
In this paper, we propose a novel approach to formalize the impact of malicious intergroup informational attacks toward a group of unmanned aerial vehicles communication. Infrequent but critical situations arise when an already authorized group member starts to transmit false data to other group participants. These scenarios can be caused by a software or hardware malfunction or a malicious attack, and cannot be prevented by the conventional security measures. The impact of such actions can be critical for a group’s performance. To address this issue, we develop and formalize the model of unmanned aerial vehicles’ intergroup communication and provide the calculus for a group’s performance destructive impact. We employ a multi-agent-based approach to formalize the information interaction between the participants of the unmanned aerial vehicles group. The model we propose possesses such properties as symmetry and scalability, as it considers individual participants as separate homogeneous distributed agents that have to perform their tasks in parallel to achieve the joint group goal. We classify informational threats by the type of the destructive impact they cause: apparent and hidden. Data contained in informational messages is categorized according to the agent’s destructive impact premeditation degree: intentional and unintentional. To verify the model proposed, we conduct an empirical study. The results show that the false data transmitted during the intergroup communication adversely affects the group’s performance, and such an impact can be measured and quantified. Full article
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28 pages, 14573 KiB  
Article
Particle Swarm Optimization Embedded in UAV as a Method of Territory-Monitoring Efficiency Improvement
by Iuliia Kim, João Pedro Matos-Carvalho, Ilya Viksnin, Tiago Simas and Sérgio Duarte Correia
Symmetry 2022, 14(6), 1080; https://doi.org/10.3390/sym14061080 - 24 May 2022
Cited by 3 | Viewed by 2261
Abstract
Unmanned aerial vehicles have large prospects for organizing territory monitoring. To integrate them into this sphere, it is necessary to improve their high functionality and safety. Computer vision is one of the vital monitoring aspects. In this paper, we developed and validated a [...] Read more.
Unmanned aerial vehicles have large prospects for organizing territory monitoring. To integrate them into this sphere, it is necessary to improve their high functionality and safety. Computer vision is one of the vital monitoring aspects. In this paper, we developed and validated a methodology for terrain classification. The overall classification procedure consists of the following steps: (1) pre-processing, (2) feature extraction, and (3) classification. For the pre-processing stage, a clustering method based on particle swarm optimization was elaborated, which helps to extract object patterns from the image. Feature extraction is conducted via Gray-Level Co-Occurrence Matrix calculation, and the output of the matrix is turned into the input for a feed-forward neural network classification stage. The developed computer vision system showed 88.7% accuracy on the selected test set. These results can provide high quality territory monitoring; prospectively, we plan to establish a self-positioning system based on computer vision. Full article
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