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Innovations in mathematical modeling, AI, and optimization techniques

JOURNAL OF SUPERcomputing 2025年第1期81卷 1-4页

作者： Ohue, Masahito Yasuo, Nobuaki Takata, Masami Inst Sci Tokyo Sch Comp Dept Comp Sci Yokohama Kanagawa 2268501 Japan Inst Sci Tokyo Acad Convergence Mat & Informat TAC MI Tokyo 1528550 Japan Nara Womens Univ Res Grp Informat & Commun Technol Life Nara 6308506 Japan

this special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational science. It features the most notable papers from the "Mathematical Modeling and Problem Solving" workshop at PDPTA'24, the 30th international conference on parallel and distributed Processing Techniques and applications. the issue showcases pioneering research in areas such as natural language processing, system optimization, and high-performance computing. the nine selected studies include novel AI-driven methods for chemical compound generation, historical text recognition, and music recommendation, along with advancements in hardware optimization through reconfigurable accelerators and vector register sharing. Additionally, evolutionary and hyper-heuristic algorithms are explored for sophisticated problem-solving in engineering design, and innovative techniques are introduced for high-speed numerical methods in large-scale systems. Collectively, these contributions demonstrate the significance of AI, supercomputing, and advanced algorithms in driving the next generation of scientific discovery.

关键词： Mathematical modeling Artificial intelligence parallel and distributed computing Reconfigurable computing Drug discovery

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Proceedings of the 15th international conference on Cloud computing and Services Science, CLOSER 2025

Proceedings of the 15th International Conference on Cloud Co...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

ISBN: (纸本)9789897587474

the proceedings contain 28 papers. the topics discussed include: performance and usability implications of multiplatform and WebAssembly containers;operations patterns for hybrid quantum applications;optimization of cloud-native application execution over the edge cloud continuum enabled by DVFS;energy-aware node selection for cloud-based parallel workloads with machine learning and infrastructure as code;security-aware allocation of replicated data in distributed storage systems;performance analysis of mdx ii: a next-generation cloud platform for cross-disciplinary data science research;data orchestration platform for AI workflows execution across computing continuum;framework for decentralized data strategies in virtual banking: navigating scalability, innovation, and regulatory challenges in thailand;and anomaly detection for partially observable container systems based on architecture profiling.

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Containerized Data-Flow Processing for Scalable Real-Time Analytics on Edge Devices 25th

Containerized Data-Flow Processing for Scalable Real-Time A...

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25th international conference on parallel and distributed computing, applications and technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Y. Zomaya, Albert School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW2006 Australia

ISBN: (纸本)9789819642069

the increasing demand for real-time data analysis in Internet of things (IoT) ecosystems has created several challenges, particularly in environments where resources are limited, and minimizing data processing latency is critical. this paper investigates the use of Linux Containerization (LXC) as a lightweight, resource-efficient alternative to traditional virtual machine managers for accelerating data-flow processing applications. We propose an architecture that integrates Linux Containerization (LXC) with a Proportional-Integral-Derivative (PID) controller to dynamically manage I/O resource allocation, ensuring Quality of Service (QoS) for I/O-intensive data analytics workloads. the proposed PID controller continuously monitors system performance and dynamically adjusts I/O resource allocation to mitigate performance degradation. through a series of experiments, we demonstrate that LXC-based solutions outperform traditional methods in managing I/O-intensive data analytics workloads in IoT and edge ecosystems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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I/O Latency Management in Private Cloud Infrastructures 25th

I/O Latency Management in Private Cloud Infrastructures

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25th international conference on parallel and distributed computing, applications and technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

this paper examines how co-locating multiple VMs on a single physical server with shared storage impacts I/O performance, specifically focusing on latency of I/O operations, and the overall throughput. We introduce a Proportional-Integral-Derivative (PID) controller to maintain I/O latency close to a predefined target by dynamically adjusting I/O requests based on real-time measurements. through a series of experiments, we evaluated the effectiveness of our PID controller in comparison to VMware’s default QoS mechanism within a private cloud environment. the results indicate that the PID controller significantly enhances performance for latency-critical workloads, achieving substantially higher compliance with latency targets compared to the default mechanism. However, this prioritization of high-priority VMs comes at the expense of increased latency and reduced throughput for lower-priority workloads during periods of I/O contention. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： three term control systems

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Towards Optimizing Cost and Performance for parallel Workloads in Cloud computing

Towards Optimizing Cost and Performance for Parallel Workloa...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Maas, William Rossi, Fábio Diniz Luizelli, Marcelo C. Navaux, Philippe O.A. Lorenzon, Arthur F. Institute of Informatics Federal University of Rio Grande do Sul Brazil Federal Institute Farroupilha Campus Alegrete Brazil Federal University of Pampa Campus Alegrete Brazil

ISBN: (纸本)9789897587474

the growing popularity of data-intensive applications in cloud computing necessitates a cost-effective approach to harnessing distributed processing capabilities. However, the wide variety of instance types and configurations available can lead to substantial costs if not selected based on the parallel workload requirements, such as CPU and memory usage and thread scalability. this situation underscores the need for scalable and economical infrastructure that effectively balances parallel workloads’ performance and expenses. To tackle this issue, this paper comprehensively analyzes performance, costs, and trade-offs across 18 parallel workloads utilizing 52 high-performance computing (HPC) optimized instances from three leading cloud providers. Our findings reveal that no single instance type can simultaneously offer the best performance and the lowest costs across all workloads. Instances that excel in performance do not always provide the best cost efficiency, while the most affordable options often struggle to deliver adequate performance. Moreover, we demonstrate that by customizing instance selection to meet the specific needs of each workload, users can achieve up to 81.2% higher performance and reduce costs by 95.5% compared to using a single instance type for every workload. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： parallel processing systems

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Advancing Evasion: distributed Backdoor Attacks in Federated Learning 25th

Advancing Evasion: Distributed Backdoor Attacks in Federate...

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25th international conference on parallel and distributed computing, applications and technologies, PDCAT 2024

作者： Wang, Jian Shen, Hong Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia School of Software Technology Guangzhou Institute of Software Guangzhou China

ISBN: (纸本)9789819642069

Federated Learning (FL) is vulnerable to backdoor attacks through data poisoning if the data is not scrutinized, as malicious participants can inject backdoor triggers in normal samples, leading to poisoned updates. distributed backdoor attacks pose a greater threat than centralized ones, as they often use fixed pixel blocks as triggers, increasing the risk of detection. this paper presents a novel distributed backdoor attack strategy that leverages edge structure poisoning to circumvent existing defense mechanisms, employing a distributed poisoning strategy to evade current defense mechanisms, thereby enhancing the stealth of the attack. Experimental results on multiple benchmark datasets demonstrate that this method is more effective and stealthy compared to other backdoor attack methods. Furthermore, this paper also proposes targeted defense strategies based on the experimental results, offering a new perspective on the security of FL systems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams 25th

Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams

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25th international conference on parallel and distributed computing, applications and technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

Graphics Processing Units (GPUs) are widely used as powerful hardware accelerators for data-intensive tasks. However, their efficacy can be hindered by constraints in device memory and data transfer speeds via the PCI-E link. this paper explores the use of GPUs for executing scalable sorting operations that exceeds the GPU’s global memory by a factor of at least two orders of magnitude. To address this challenge, we leverage the power of NVIDIA’s asynchronous CUDA streams. Our solution enhances GPU performance by implementing a pipeline model to offload sorting operations from the Solid-State Drive (SSD) to GPU memory via the host’s main memory. this strategy maximizes the multiway concurrency level to reduce the overhead associated with data transfer within the GPU memory hierarchy. We perform extensive performance evaluations by sorting 128 billion data elements initially stored on an external NVMe SSD. the results confirm the effectiveness of our approach, showcasing a speed-up of 4.3× compared to the results for CPU-based out-of-memory sorting of skewed data based on Intel’s threading Building Blocks (TBB) sorting algorithm. Our results include the cost of transfer times to and from the main memory, a factor often overlooked in similar studies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graphics processing unit

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Fast Approximation for Scheduling Malleable Jobs on parallel Batch Machines with Rejection 25th

Fast Approximation for Scheduling Malleable Jobs on Parall...

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25th international conference on parallel and distributed computing, applications and technologies, PDCAT 2024

作者： Xia, Fenghe Guo, Longkun Zhang, Xiaoyan School of Mathematics and Statistics Fuzhou University Fuzhou350116 China School of Mathematical Science Institute of Mathematics and Ministry of Education Key Laboratory of NSLSCS Nanjing Normal University Nanjing210023 China

ISBN: (纸本)9789819642069

the rapid growth of cloud computing has brought new challenges in parallel Batch Machine Scheduling (PBMS), particularly when incorporating malleability and rejection constraints. this has led to the parallel Batch Machine Scheduling with Malleability and Rejection problem (PBMSMR), which involves malleable jobs whose widths can be adjusted during execution within specified limits and incorporates job rejection subject to a penalty threshold. Based on an analysis of key properties of batch scheduling with job rejection, we develop an approximation algorithm for PBMSMR by employing a greedy approach that reorders and iteratively refines job sets to minimize the objective while respecting rejection constraints. the algorithm achieves a time complexity of O(n2logn) and an approximation ratio of (4-2Km), where K and m denote the capacities and the number of the machines, respectively. For jobs with identical release times, we fine-tune the algorithm to achieve an approximation ratio of (3+2(K-1)Km). Additionally, for PBMS with two machines of non-identical capacities and fixed-width jobs, we achieve a ratio of 175 with O(nlogn), improving upon the previous state-of-the-art approximation ratio of 5 with a runtime of O(n2). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Job shop scheduling

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parallel computing technologies: 9th international conference, PaCT 2007, Proceedings

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9th international conference on parallel computing technologies, PaCT 2007

ISBN: (纸本)9783540739395

the proceedings contain 63 papers. the topics discussed include: looking for a definition of dynamic distributed system;adaptive workflow nets for grid computing;a stochastic semantics for BioAmbients;a categorical observation of timed testing equivalence;from unreliable objects to reliable objects: the case of atomic registers and consensus;towards a computing model for open distributed systems;enhancing online computer games for open distributed systems;enhancing online computer games for grids;optimized parallel approach for 3D modelling of forest fire behaviour;a high-level toolkit for development of distributed scientific applications;orthogonal organized finite state machine application to sensor acquired information;parallel broadband finite element time domain algorithm implemented to dispersive electromagnetic problem;and strategies for development of a parallel program for protoplanetary disc simulation.

关键词： parallel processing systems

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parallel Acceleration of Transportation Problem Solving Using SpMV in the Industrial Chain Context 25th

Parallel Acceleration of Transportation Problem Solving Usi...

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25th international conference on parallel and distributed computing, applications and technologies, PDCAT 2024

作者： Dong, De Lin, Guanyu Xu, Yicheng Ma, Ruikang Xia, Chenliang Fan, Jianping Li, Fuchong Southern University of Science and Technology Guangdong Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China Jinan Industrial Development Park Technology Development Co. Ltd. Shandong Jinan China

ISBN: (纸本)9789819642069

As modern industrial chains grow increasingly complex and time-sensitive, traditional transportation planning methods face efficiency bottlenecks. To address this, we propose a parallelization method based on Sparse Matrix-Vector Multiplication (SpMV) to accelerate the Transportation Simplex Algorithm (TSA) for large-scale transportation problems. Existing approaches primarily leverage data parallelism but underutilize GPU computational potential. this study transforms sequential algorithms into matrix-based representations, combining SpMV and parallel graph algorithms to enhance TSA performance. Key contributions include mapping performance bottlenecks—potential vector calculation and closed-loop search—onto SpMV operations, and converting branch logic into matrix operations to reduce divergence. Experiments on a 10000×10000 dataset demonstrate a 20.8x speedup for the parallelized MODI algorithm and a 28x overall speedup for solving the Transportation Problem, significantly improving efficiency. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

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