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Electronics
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High-Performance Software Systems
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High-Performance Software Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 3848

Special Issue Editors

Dr. Jinkun Geng

E-Mail Website
Guest Editor
Computer Science Department, Stanford University, Stanford, CA 94305, USA
Interests: high-performance networking systems; fault-tolerant systems
Dr. Xizhe Yin

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA
Interests: parallel/high-performance computing; graph processing systems
Dr. Yifan Mao

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
Interests: decentralized network; network algorithm

Special Issue Information

Dear Colleagues,

The field of software engineering is rapidly evolving, with high-performance software systems at the forefront of this technological revolution. High-performance software systems are crucial in numerous fields where efficiency and speed are paramount. From scientific research requiring massive computational power to real-time applications in healthcare and finance, the demand for optimized software solutions is ever increasing. This Special Issue aims to gather pioneering research that addresses these needs, providing valuable insights and advancements to both academia and industry.

The Special Issue invites any innovative solution for a significant problem involving software systems design and implementation. We take a broad view of systems and solicit contributions from many fields of systems practice. We welcome results and discussion of ideas that further the knowledge and understanding of the systems community.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • High-performance networking systems;
  • High-performance database systems;
  • Novel data management systems;
  • Security and privacy of systems;
  • Availability and fault tolerance;
  • AI systems and systems for AI;
  • Testing and verification of large-scale systems;
  • Decentralized systems;
  • File systems;
  • Cloud-native systems.

By bringing together leading researchers and practitioners in the field, this Special Issue on "High-Performance Software Systems" seeks to foster collaboration, share cutting-edge research, and drive the development of software systems that meet the growing performance demands of modern applications. We look forward to receiving your contributions.

Dr. Jinkun Geng
Dr. Xizhe Yin
Dr. Yifan Mao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • high-performance
  • networking
  • database
  • cloud
  • fault tolerance
  • availability
  • distributed systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

55 pages, 928 KiB  
Article
Optimization Methods, Challenges, and Opportunities for Edge Inference: A Comprehensive Survey
by Runhua Zhang, Hongxu Jiang, Wei Wang and Jinhao Liu
Electronics 2025, 14(7), 1345; https://doi.org/10.3390/electronics14071345 - 27 Mar 2025
Viewed by 1527
Abstract
Artificial intelligence (AI) continues to enhance production efficiency across various fields of society. Considering real-time requirements and privacy issues, edge inference (EI) is shifting from cloud scenarios to edge scenarios. As intelligent models grow in complexity and size, EI encounters significant challenges. To [...] Read more.
Artificial intelligence (AI) continues to enhance production efficiency across various fields of society. Considering real-time requirements and privacy issues, edge inference (EI) is shifting from cloud scenarios to edge scenarios. As intelligent models grow in complexity and size, EI encounters significant challenges. To address these, existing research works have optimized EI from four aspects (model design, model compression, compilation toolchain, and collaborative inference) to ensure the advantages of edge intelligence. However, current works lack a comprehensive classification and discussion of existing research results. Thus, we conduct a comprehensive survey on their state-of-the-art research. Specifically, we first review the background and motivation of EI, then analyze the key issues, characteristics, and technologies of each direction. Finally, we analyze future development trends. This paper can help researchers quickly sort out the different directions of EI optimization and important related work. We hope it can bring inspiration to the researchers in these communities and motivate more follow-up works. Full article
(This article belongs to the Special Issue High-Performance Software Systems)
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14 pages, 4118 KiB  
Article
Towards Sustainability and Energy Efficiency Using Data Analytics for HPC Data Center
by Andrea Chinnici, Eyvaz Ahmadzada, Ah-Lian Kor, Davide De Chiara, Adrián Domínguez-Díaz, Luis de Marcos Ortega and Marta Chinnici
Electronics 2024, 13(17), 3542; https://doi.org/10.3390/electronics13173542 - 6 Sep 2024
Viewed by 1814
Abstract
High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. [...] Read more.
High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. The study starts by gathering and cleaning extensive datasets consisting of job schedules, environmental conditions, cooling systems, and sensors. Descriptive statistics accompanied with visualizations provide deep insight into collated data. Inferential statistics are then used to investigate relationships between various operational variables. Finally, machine learning models predict the average hot-aisle temperature based on cooling parameters, which can be used to determine optimal cooling settings. Furthermore, idle periods for computing nodes are analyzed to estimate wasted energy, as well as for evaluating the effect that idle node shutdown will have on the thermal characteristics of the data center under consideration. It closes with a discussion on how statistical and machine learning techniques can improve operations in a data center by focusing on important variables that determine consumption patterns. Full article
(This article belongs to the Special Issue High-Performance Software Systems)
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