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Applied Sciences
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Applications of Artificial Intelligence in Operating Systems
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Applications of Artificial Intelligence in Operating Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1718

Special Issue Editors

Dr. Donghyun Kang

E-Mail Website
Guest Editor
Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea
Interests: operating systems; file and storage systems (for NAND flash memory and byte-addressable NVRAM); artificial intelligence (AI) systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jaeho Kim

E-Mail Website
Guest Editor
Department of Software Engineering, Gyeongsang National University, Jinjusi 52828, Republic of Korea
Interests: storage system; concurrency; operating system; computer architecture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the explosive increase in AI/ML applications has accelerated the development of software systems optimizing CPU, GPU, memory and storage technologies. In addition, with the advent of new hardware and software platforms for AI/ML technologies, traditional operating systems are updating their approaches.

This Special Issue on “Applications of Artificial Intelligence in Operating Systems” welcomes submissions of recent research work on applications and software systems for artificial intelligence (AI) and machine learning (ML).

We are open to a broad thematic range of papers covering software optimizations of AI and ML across operating systems, memory and storage challenges, new hardware technologies and research trends offering readers knowledge for adopting AI/ML for system software in various mobile, embedded and enterprise environments.

Dr. Donghyun Kang
Dr. Jaeho Kim
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. Applied Sciences 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

  • on-device AI
  • memory/storage for AI/ML
  • emerging memory/storage hierarchy design for AI/ML
  • processing in memory (PIM)/in-storage processing technologies for AI/ML
  • parallel processing for AI/ML
  • energy-efficient memory/storage management for AI/ML
  • file system design
  • key-value and NoSQL storage

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Published Papers (1 paper)

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Research

16 pages, 580 KiB  
Article
Check-QZP: A Lightweight Checkpoint Mechanism for Deep Learning Frameworks
by Sangheon Lee, Gyupin Moon, Chanyong Lee, Hyunwoo Kim, Donghyeok An and Donghyun Kang
Appl. Sci. 2024, 14(19), 8848; https://doi.org/10.3390/app14198848 - 1 Oct 2024
Viewed by 1034
Abstract
In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures [...] Read more.
In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures or random crashes. However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments (e.g., AWS and Azure) because excessive I/O operations issued by other running applications interfere with the checkpoint operations in the storage stacks. To efficiently address the above two problems, we carefully designed Check-QZP, which reduces the amount of data required for checkpoint operations and parallelizes executions on the CPU and GPU by understanding the internal behaviors of the training step. For the evaluation, we implemented Check-QZP and compared it with the traditional approach in real-world multi-tenant scenarios. In the evaluation, Check-QZP outperformed the baseline in all cases in terms of the overall checkpoint time and the amount of data generated by the checkpoint operations, reducing them by up to 87.5% and 99.8%, respectively. In addition, Check-QZP achieved superior training speeds compared to the baseline. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Operating Systems)
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