Journal of Parallel and Distributed Computing最新文献

{"title":"Towards efficient program execution on edge-cloud computing platforms","authors":"Jean-François Dollinger,&nbsp;Vincent Vauchey","doi":"10.1016/j.jpdc.2025.105135","DOIUrl":"10.1016/j.jpdc.2025.105135","url":null,"abstract":"<div><div>This paper investigates techniques dedicated to the performance of edge-cloud infrastructures and identifies the challenges to address to maximize their efficiency. Unlike traditional cloud-only processing, edge-cloud platforms meet the stringent requirements of real-time applications via additional computing resources close to the data source. Yet, due to numerous performance factors, it is a complex task to perform efficient computations on such platforms. Thus, we identify the main performance bottlenecks induced by traditional approaches and extensively discuss the performance characteristics of edge computing platforms. Based on these insights, we design an automated framework capable of achieving end-to-end efficacy of edge-cloud applications. We argue that achieving performance on edge-cloud infrastructures requires adaptive offloading of programs based on computational requirements. Thus, we comprehensively study three performance-critical aspects forming the performance workflow of applications: i) performance modelling, ii) program optimization iii) task scheduling. First, we explore performance modelling techniques, forming the foundation of most cost models, to accurately predict and achieve robust code optimization and scheduling. We then cover the whole program optimization chain, from hotspot detection to code optimization, focusing on memory locality, code parallelization, and acceleration. Finally, we discuss task scheduling techniques for selecting the best computing resource and ensuring a balanced workload distribution. Overall, our study provides insights by covering the above performance workflow referencing prominent state-of-the-art works, particularly focusing on those not yet applied in the context of edge-cloud computing. Additionally, we conducted experiments to further validate our findings. Finally, for each topic of interest, we identify the addressed scientific obstacles and outline the open research challenges yet to be overcome.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"205 ","pages":"Article 105135"},"PeriodicalIF":3.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DH_Aligner: A fast short-read aligner on multicore platforms with AVX vectorization","authors":"Qiao Sun ,&nbsp;Feng Chen ,&nbsp;Leisheng Li ,&nbsp;Huiyuan Li","doi":"10.1016/j.jpdc.2025.105142","DOIUrl":"10.1016/j.jpdc.2025.105142","url":null,"abstract":"<div><div>The rapid development of the NGS (Next-Generation Sequencing) technology leads to massive genome data produced at a much higher throughput than before, which leads to great demand for downstream fast and accurate genetic analysis. As one of the first steps of bio-informatical work-flow, read alignment makes an educated guess on where and how a read is mapped to a given reference sequence. In this paper, we propose DH_Aligner, a fast and accurate short read aligner designed and optimized for x86 multi-core platforms with <span>avx2/avx512</span> SIMD instruction sets. It is based on a three-phased aligning work-flow: seeding-filtering-extension and provides an end-to-end solution for read alignment from <span>Fastq</span> to <span>SAM</span> files. Due to a fast seeding scheme and a seed filtering procedure, DH_Aligner can avoid both of a time-consuming seeding phase and redundant workload of aligning reads at seemingly wrong locations. With the introduction of batched-processing methodology, parallelism is easily exploited at data-, instruction- and thread-level. The performance-critical kernels in DH_Aligner are implemented by both <span>avx2</span> and <span>avx512</span> intrinsics for a better performance and portability. On two typical x86 based platforms: Intel Xeon-6154 and Hygon C86-7285, DH_Aligner can produce a near-best accuracy/sensitivity while outperform state-of-the-art parallel implementations with average speedup: 7.8x, 3.4x, 2.8x-6.7x and 1.5x over bwa-mem, bwa-mem2, bowtie2 and minimap2 respectively.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"205 ","pages":"Article 105142"},"PeriodicalIF":3.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MM-AutoSolver: A multimodal machine learning method for the auto-selection of iterative solvers and preconditioners","authors":"Hantao Xiong ,&nbsp;Wangdong Yang ,&nbsp;Weiqing He ,&nbsp;Shengle Lin ,&nbsp;Keqin Li ,&nbsp;Kenli Li","doi":"10.1016/j.jpdc.2025.105144","DOIUrl":"10.1016/j.jpdc.2025.105144","url":null,"abstract":"<div><div>The solution of large-scale sparse linear systems of the form <span><math><mi>A</mi><mi>x</mi><mo>=</mo><mi>b</mi></math></span> is an important research problem in the field of High-performance Computing (HPC). With the increasing scale of these systems and the development of both HPC software and hardware, iterative solvers along with appropriate preconditioners have become mainstream methods for efficiently solving these sparse linear systems that arise from real-world HPC applications. Among abundant combinations of iterative solvers and preconditioners, the automatic selection of the optimal one has become a vital problem for accelerating the solution of these sparse linear systems. Previous work has utilized machine learning or deep learning algorithms to tackle this problem, but fails to abstract and exploit sufficient features from sparse linear systems, thus unable to obtain satisfactory results. In this work, we propose to address the automatic selection of the optimal combination of iterative solvers and preconditioners through the powerful multimodal machine learning framework, in which features of different modalities can be fully extracted and utilized to improve the results. Based on the multimodal machine learning framework, we put forward a multimodal machine learning model called MM-AutoSolver for the auto-selection of the optimal combination for a given sparse linear system. The experimental results based on a new large-scale matrix collection showcase that the proposed MM-AutoSolver outperforms state-of-the-art methods in predictive performance and has the capability to significantly accelerate the solution of large-scale sparse linear systems in HPC applications.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"205 ","pages":"Article 105144"},"PeriodicalIF":3.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel watershed partitioning: GPU-based hierarchical image segmentation","authors":"Varduhi Yeghiazaryan ,&nbsp;Yeva Gabrielyan ,&nbsp;Irina Voiculescu","doi":"10.1016/j.jpdc.2025.105140","DOIUrl":"10.1016/j.jpdc.2025.105140","url":null,"abstract":"<div><div>Many image processing applications rely on partitioning an image into disjoint regions whose pixels are ‘similar.’ The watershed and waterfall transforms are established mathematical morphology pixel clustering techniques. They are both relevant to modern applications where groups of pixels are to be decided upon in one go, or where adjacency information is relevant. We introduce three new parallel partitioning algorithms for GPUs. By repeatedly applying watershed algorithms, we produce waterfall results which form a hierarchy of partition regions over an input image. Our watershed algorithms attain competitive execution times in both 2D and 3D, processing an 800 megavoxel image in less than 1.4 sec. We also show how to use this fully deterministic image partitioning as a pre-processing step to machine-learning-based semantic segmentation. This replaces the role of superpixel algorithms, and results in comparable accuracy and faster training times. The code is publicly available at <span><span>https://github.com/hamemm/PRUF-watershed.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"205 ","pages":"Article 105140"},"PeriodicalIF":3.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration framework for online thread throttling with thread and page mapping on NUMA systems","authors":"Janaina Schwarzrock ,&nbsp;Hiago Mayk G. de A. Rocha ,&nbsp;Arthur F. Lorenzon ,&nbsp;Samuel Xavier de Souza ,&nbsp;Antonio Carlos S. Beck","doi":"10.1016/j.jpdc.2025.105145","DOIUrl":"10.1016/j.jpdc.2025.105145","url":null,"abstract":"<div><div>Non-Uniform Memory Access (NUMA) systems are prevalent in HPC, where optimal thread-to-core allocation and page placement are crucial for enhancing performance and minimizing energy usage. Moreover, considering that NUMA systems have hardware support for a large number of hardware threads and many parallel applications have limited scalability, artificially decreasing the number of threads by using Dynamic Concurrency Throttling (DCT) may bring further improvements. However, the optimal configuration (thread mapping, page mapping, number of threads) for energy and performance, quantified by the Energy-Delay Product (EDP), varies with the system hardware, application and input set, even during execution. Because of this dynamic nature, adaptability is essential, making offline strategies much less effective. Despite their effectiveness, online strategies introduce additional execution overhead, which involves learning at run-time and the cost of transitions between configurations with cache warm-ups, thread and data reallocation. Thus, balancing the learning time and solution quality becomes increasingly significant. In this scenario, this work proposes a framework to find such optimal configurations into a single, online, and efficient approach. Our experimental evaluation shows that our framework improves EDP and performance compared to online state-of-the-art techniques of thread/page mapping (up to 69.3% and 43.4%) and DCT (up to 93.2% and 74.9%), while being totally adaptive and requiring minimum user intervention.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"205 ","pages":"Article 105145"},"PeriodicalIF":3.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fog computing based public key encryption with multi-keyword search for Internet of vehicles","authors":"Mandira Banik,&nbsp;Sanjay Kumar","doi":"10.1016/j.jpdc.2025.105131","DOIUrl":"10.1016/j.jpdc.2025.105131","url":null,"abstract":"<div><div>Fog-based computation is an interesting computing paradigm developed for giving end users access to real-time services in the Internet of Vehicles (IoV). Achieving safe and effective data sharing is a huge challenge in such a dynamic system architecture. As a promising cryptographic primitive, searchable encryption (SE) aims to maintain data searchability while maintaining data confidentiality. Nonetheless, the majority of current methods are open to attacks by leaking exploitation. So, we develop a fog-based searchable public key encryption scheme (FC-PEMKS) that achieves forward security and multi-keyword search in the IoV network. The security proof shows that our model achieves the indistinguishability of trapdoor and keyword index. The outcomes of the comparative simulations and performance analysis demonstrate the viability and effectiveness of the FC-PEMKS scheme in fog-enabled vehicular networks.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"204 ","pages":"Article 105131"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The CAMINOS interconnection networks simulator","authors":"Cristóbal Camarero,&nbsp;Daniel Postigo,&nbsp;Pablo Fuentes","doi":"10.1016/j.jpdc.2025.105136","DOIUrl":"10.1016/j.jpdc.2025.105136","url":null,"abstract":"<div><div>This work presents CAMINOS, a new interconnection network simulator focusing on router microarchitecture. It was developed in Rust, a novel programming language with a syntax similar to C/C++ and strong memory protection.</div><div>The architecture of CAMINOS emphasizes the composition of components. This allows new designs to be defined in a configuration file without modifying source code, greatly reducing effort and time.</div><div>In addition to simulation functionality, CAMINOS assists in managing a collection of simulations as an experiment. This includes integration with SLURM to support executing batches of simulations and generating PDFs with results and diagnostics.</div><div>We show that CAMINOS makes good use of computing resources. Its memory usage is dominated by in-flight messages, showing low overhead in memory usage. We attest that CAMINOS can effectively use CPU time, as scenarios with little contention execute faster.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"204 ","pages":"Article 105136"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Line formation and scattering in silent programmable matter","authors":"Alfredo Navarra ,&nbsp;Francesco Piselli ,&nbsp;Giuseppe Prencipe","doi":"10.1016/j.jpdc.2025.105129","DOIUrl":"10.1016/j.jpdc.2025.105129","url":null,"abstract":"<div><div>Programmable Matter (PM) has been widely investigated in recent years. It refers to some kind of substance with the ability to change its physical properties (e.g., shape or color) in a programmable way. In this paper, we refer to the <span><math><mi>SILBOT</mi></math></span> model, where the particles live and move on a triangular grid, are asynchronous in their computations and movements, and do not possess any direct means of communication (silent) or memory of past events (oblivious).</div><div>Within <span><math><mi>SILBOT</mi></math></span>, we aim at studying <em>Spanning</em> problems, i.e., problems where the particles are required to suitably span all over the grid. We first address the <span>Line Formation</span> problem where the particles are required to end up in a configuration where they all lie on a line, i.e., they are aligned and connected. Secondly, we deal with the more general <span>Scattering</span> problem: starting from any initial configuration, we aim at reaching a final one where no particles occupy neighboring nodes. Furthermore, we investigate configurations where some nodes of the grid can be occupied by unmovable elements (i.e., obstacles) from both theoretical and experimental view points.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"204 ","pages":"Article 105129"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight path aggregation network for pedestrian detection on FPGA board","authors":"Riadh Ayachi ,&nbsp;Mouna Afif ,&nbsp;Yahia Said ,&nbsp;Abdessalem Ben Abdelali","doi":"10.1016/j.jpdc.2025.105137","DOIUrl":"10.1016/j.jpdc.2025.105137","url":null,"abstract":"<div><div>In urban environments, pedestrian safety stands as a pivotal metric dictating the accuracy and efficacy of cutting-edge technologies like Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. However, the deployment of such technologies introduces various constraints, notably including the computational resources of processing boards. Therefore, constructing a robust pedestrian detection system necessitates achieving a delicate balance between performance and computational complexity. In this study, we propose the development of a lightweight Convolutional Neural Network (CNN) model specifically tailored for pedestrian detection. The backbone architecture of the model was meticulously searched using a network search engine predicated on the Multi-Objective Genetic Algorithm (NSGA-II) with a customized strategy. Notably, we shifted the search space from central processing units to Multi-Processor System-on-Chip (MPSoC) devices, aligning with the practical considerations of real-world applications. Our proposed model capitalizes on the path aggregation architecture coupled with a lightweight backbone design. The core concept revolves around the efficient transfer of high semantic features from the network's bottom to its top via the shortest path, thereby enhancing detection rates without introducing undue computational complexity. To ensure compatibility with embedded devices with limited memory, the proposed model underwent compression via quantization and pruning techniques. For rigorous evaluation, we tested the pedestrian detection model on the Xilinx ZCU 102 board, utilizing the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset for training and evaluation purposes. The reported results substantiate the efficacy of our proposed model, boasting a mean average precision (mAP) of 93.6 % alongside a commendable processing speed of 13 frames per second (FPS). These outcomes underscore the suitability of the proposed model for real-life scenarios, wherein ensuring a high level of safety remains paramount.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"204 ","pages":"Article 105137"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitigating DDoS attacks in containerized environments: A comparative analysis of Docker and Kubernetes","authors":"Yung-Ting Chuang,&nbsp;Chih-Han Tu","doi":"10.1016/j.jpdc.2025.105130","DOIUrl":"10.1016/j.jpdc.2025.105130","url":null,"abstract":"<div><div>Containerization has become the primary method for deploying applications, with web services being the most prevalent. However, exposing server IP addresses to external connections renders containerized services vulnerable to DDoS attacks, which can deplete server resources and hinder legitimate user access. To address this issue, we implement twelve different mitigation strategies, test them across three common types of web services, and conduct experiments on both Docker and Kubernetes deployment platforms. Furthermore, this study introduces a cross-platform, orchestration-aware evaluation framework that simulates realistic multi-service workloads and analyzes defense strategy performance under varying concurrency conditions. Experimental results indicate that Docker excels in managing white-listed traffic and delaying attacker responses, while Kubernetes achieves low completion times, minimum response times, and low failure rates by processing all requests simultaneously. Based on these findings, we provide actionable insights for selecting appropriate mitigation strategies tailored to different orchestration environments and workload patterns, offering practical guidance for securing containerized deployments against low-rate DDoS threats. Our work not only provides empirical performance evaluations but also reveals deployment-specific trade-offs, offering strategic recommendations for building resilient cloud-native infrastructures.</div></div>","PeriodicalId":54775,"journal":{"name":"Journal of Parallel and Distributed Computing","volume":"204 ","pages":"Article 105130"},"PeriodicalIF":3.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}