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Applied Sciences
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Parallel, Distributed and Cloud Computing: Status, Prospects and Future
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Parallel, Distributed and Cloud Computing: Status, Prospects and Future

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

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 8975

Special Issue Editors

Prof. Dr. Giuliano Laccetti

E-Mail Website
Guest Editor
Department of Mathematics “R. Caccioppoli”, University of Naples Federico II, 80126 Naples, Italy
Interests: cloud computing; high-performance computing; performance analysis; algorithms; parallel programming; parallel algorithms; scientific software; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Valeria. Mele

E-Mail Website
Guest Editor
Department of Mathematics “R. Caccioppoli”, University of Naples Federico II, 80126 Naples, Italy
Interests: high-performance computing; performance analysis; energy-aware algorithms and systems; machine learning; parallelism in time; parallel programming; parallel algorithms; scalable algorithms; imaging; clustering; fault tolerance; scientific software; GP-GPU; databases

Special Issue Information

Dear Colleagues,

In the last decade, we have seen the rise of Grid and Cloud Computing environments realized through sophisticated middleware acting as operating systems that oversee the efficient management of resources. More recently, the Internet of Things and Edge Computing environments are aimed at making available in a transparent and friendly way the multitude of low power and heterogeneous resources available everywhere around us.

These environments are very different and require sophisticated programming models to achieve high performance with an ever-increasing focus on energy consumption.

The aim of this Issue is to collect the current and new trends in the theoretical, fundamental, and application research in High Performance, Parallel, Distributed and Cloud Computing, taking into account all the aspects that have emerged over time as crucial for systems durability, performance maintenance and increasing, energy sustainability and applications range expansion.

We invite researchers to submit their new results as well as reviews about theories, models, methodologies, technologies, algorithms and/or softwares, systems and architectures, in the field of Parallel, Distributed and Cloud Computing, as well as IoT, AI and Machine Learning, Edge Computing.

Prof. Dr. Giuliano Laccetti
Dr. Valeria. Mele
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

  • cloud computing
  • cloud systems
  • parallel computing
  • distributed computing
  • high-performance computing
  • parallel algorithms
  • parallel dynamical systems
  • GP-GPU
  • big data
  • artificial intelligence
  • machine learning
  • data mining
  • clustering
  • Internet of Things
  • parallelism in time
  • performance analysis
  • load balancing
  • distributed cloud
  • scalable algorithms
  • parallel systems
  • distributed systems
  • energy-aware algorithms and systems

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Published Papers (5 papers)

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Research

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14 pages, 2217 KiB  
Article
Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations
by Pasquale De Luca, Ardelio Galletti and Livia Marcellino
Appl. Sci. 2024, 14(16), 7028; https://doi.org/10.3390/app14167028 - 10 Aug 2024
Viewed by 1324
Abstract
Power consumption is a significant challenge in the sustainability of computational science. The growing energy demands of increasingly complex simulations and algorithms lead to substantial resource use, which conflicts with global sustainability goals. This paper investigates the energy efficiency of different parallel implementations [...] Read more.
Power consumption is a significant challenge in the sustainability of computational science. The growing energy demands of increasingly complex simulations and algorithms lead to substantial resource use, which conflicts with global sustainability goals. This paper investigates the energy efficiency of different parallel implementations of a Cellular Potts model, which models cellular behavior through Hamiltonian energy minimization techniques, leveraging modern GPU architectures. By evaluating alternative solvers, it demonstrates that specific methods can significantly enhance computational efficiency and reduce energy use compared to traditional approaches. The results confirm notable improvements in execution time and energy consumption. In particular, the experiments show a reduction in terms of power of up to 53%, providing a pathway towards more sustainable high-performance computing practices for complex biological simulations. Full article
(This article belongs to the Special Issue Parallel, Distributed and Cloud Computing: Status, Prospects and Future)
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42 pages, 7686 KiB  
Article
Parallel GPU-Acceleration of Metaphorless Optimization Algorithms: Application for Solving Large-Scale Nonlinear Equation Systems
by Bruno Silva, Luiz Guerreiro Lopes and Fábio Mendonça
Appl. Sci. 2024, 14(12), 5349; https://doi.org/10.3390/app14125349 - 20 Jun 2024
Cited by 1 | Viewed by 1253
Abstract
Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear [...] Read more.
Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear equations is still incipient. This paper presents a review and detailed description of the main metaphorless optimization algorithms, including the Jaya and enhanced Jaya (EJAYA) algorithms, the three Rao algorithms, the best-worst-play (BWP) algorithm, and the new max–min greedy interaction (MaGI) algorithm. This article presents improved GPU-based massively parallel versions of these algorithms using a more efficient parallelization strategy. In particular, a novel GPU-accelerated implementation of the MaGI algorithm is proposed. The GPU-accelerated versions of the metaphorless algorithms developed were implemented using the Julia programming language. Both high-end professional-grade GPUs and a powerful consumer-oriented GPU were used for testing, along with a set of hard, large-scale nonlinear equation system problems to gauge the speedup gains from the parallelizations. The computational experiments produced substantial speedup gains, ranging from 33.9× to 561.8×, depending on the test parameters and the GPU used for testing. This highlights the efficiency of the proposed GPU-accelerated versions of the metaphorless algorithms considered. Full article
(This article belongs to the Special Issue Parallel, Distributed and Cloud Computing: Status, Prospects and Future)
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26 pages, 835 KiB  
Article
Collective Communication Performance Evaluation for Distributed Deep Learning Training
by Sookwang Lee and Jaehwan Lee
Appl. Sci. 2024, 14(12), 5100; https://doi.org/10.3390/app14125100 - 12 Jun 2024
Viewed by 2180
Abstract
In distributed deep learning, the improper use of the collective communication library can lead to a decline in deep learning performance due to increased communication time. Representative collective communication libraries such as MPI, GLOO, and NCCL exhibit varying performance based on server environment [...] Read more.
In distributed deep learning, the improper use of the collective communication library can lead to a decline in deep learning performance due to increased communication time. Representative collective communication libraries such as MPI, GLOO, and NCCL exhibit varying performance based on server environment and communication architecture. In this study, we investigate three key aspects to evaluate the performance of the collective communication libraries in a distributed deep learning setting in an intra-node environment. First, we conduct a comparison and analysis of collective communication library performance within common distributed deep learning architectures, such as parameter servers and ring all-reduce methods. Second, we evaluate the performance of these libraries in different environments, including various container platforms and bare metal setups, considering the scalability and flexibility advantages offered by cloud virtualization. Last, to ensure practicality, we assess the libraries’ performance in a Linux shell and within the PyTorch framework. In the cross-docker virtualization environment, NCCL shows up to 213% higher latency compared to single docker, while GLOO exhibits 36% lower latency in single docker than in cross docker, and NCCL achieves up to 345% lower execution time in all-reduce operations compared to other libraries (MPI and GLOO). These findings will inform the selection of an appropriate collective communication library for designing effective distributed deep learning environments. Full article
(This article belongs to the Special Issue Parallel, Distributed and Cloud Computing: Status, Prospects and Future)
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15 pages, 447 KiB  
Article
Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
by Bonmoo Koo, Jaesang Hwang, Jonghyeok Park and Wook-Hee Kim
Appl. Sci. 2023, 13(20), 11130; https://doi.org/10.3390/app132011130 - 10 Oct 2023
Cited by 1 | Viewed by 1393
Abstract
In this work, we propose the Spread approach, which tailors a concurrent range index structure to a range index structure for disaggregated memory connected via RDMA (Remote Direct Memory Access). The Spread approach leverages the concept of tolerating transient inconsistencies in a concurrent [...] Read more.
In this work, we propose the Spread approach, which tailors a concurrent range index structure to a range index structure for disaggregated memory connected via RDMA (Remote Direct Memory Access). The Spread approach leverages the concept of tolerating transient inconsistencies in a concurrent range index structure to reduce the amount of expensive RDMA operations. Based on the Spread approach, we converted Blink-tree, a concurrent range index structure, to a range index structure for disaggregated memory called RF-tree. In our experimental study, RF-tree shows comparable performance to Sherman, a state-of-the-art and carefully crafted range index structure for disaggregated memory. Full article
(This article belongs to the Special Issue Parallel, Distributed and Cloud Computing: Status, Prospects and Future)
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Review

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83 pages, 2747 KiB  
Review
Mathematical Tools for Simulation of 3D Bioprinting Processes on High-Performance Computing Resources: The State of the Art
by Luisa Carracciuolo and Ugo D’Amora
Appl. Sci. 2024, 14(14), 6110; https://doi.org/10.3390/app14146110 - 13 Jul 2024
Cited by 2 | Viewed by 1517
Abstract
Three-dimensional (3D) bioprinting belongs to the wide family of additive manufacturing techniques and employs cell-laden biomaterials. In particular, these materials, named “bioink”, are based on cytocompatible hydrogel compositions. To be printable, a bioink must have certain characteristics before, during, and after [...] Read more.
Three-dimensional (3D) bioprinting belongs to the wide family of additive manufacturing techniques and employs cell-laden biomaterials. In particular, these materials, named “bioink”, are based on cytocompatible hydrogel compositions. To be printable, a bioink must have certain characteristics before, during, and after the printing process. These characteristics include achievable structural resolution, shape fidelity, and cell survival. In previous centuries, scientists have created mathematical models to understand how physical systems function. Only recently, with the quick progress of computational capabilities, high-fidelity and high-efficiency “computational simulation” tools have been developed based on such models and used as a proxy for real-world learning. Computational science, or “in silico” experimentation, is the term for this novel strategy that supplements pure theory and experiment. Moreover, a certain level of complexity characterizes the architecture of contemporary powerful computational resources, known as high-performance computing (HPC) resources, also due to the great heterogeneity of its structure. Lately, scientists and engineers have begun to develop and use computational models more extensively to also better understand the bioprinting process, rather than solely relying on experimental research, due to the large number of possible combinations of geometrical parameters and material properties, as well as the abundance of available bioprinting methods. This requires a new effort in designing and implementing computational tools capable of efficiently and effectively exploiting the potential of new HPC computing systems available in the Exascale Era. The final goal of this work is to offer an overview of the models, methods, and techniques that can be used for “in silico” experimentation of the physicochemical processes underlying the process of 3D bioprinting of cell-laden materials thanks to the use of up-to-date HPC resources. Full article
(This article belongs to the Special Issue Parallel, Distributed and Cloud Computing: Status, Prospects and Future)
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