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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

this paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. the study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. the proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： Distributed cloud

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High Performance Dataframes from parallel processing Patterns 14th

High Performance Dataframes from Parallel Processing Patter...

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Perera, Niranda Kamburugamuve, Supun Widanage, Chathura Abeykoon, Vibhatha Uyar, Ahmet Shan, Kaiying Maithree, Hasara Lenadora, Damitha Kanewala, thejaka Amila Fox, Geoffrey Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Indiana University Alumni BloomingtonIN47405 United States University of Virginia CharlottesvilleVA22904 United States University of Moratuwa Moratuwa Bandaranayake Mawatha10400 Sri Lanka University of Illinois Urbana-Champaign UrbanaIL61801 United States Biocomplexity Institute and Initiative University of Virginia CharlottesvilleVA22904 United States

ISBN: (纸本)9783031304415

the data science community today has embraced the concept of Dataframes as the de facto standard for data representation and manipulation. Ease of use, massive operator coverage, and popularization of R and Python languages have heavily influenced this transformation. However, most widely used serial Dataframes today (R, pandas) experience performance limitations even while working on even moderately large data sets. We believe that there is plenty of room for improvement by investigating the generic distributed patterns of dataframe operators. In this paper, we propose a framework that lays the foundation for building high performance distributed-memory parallel dataframe systems based on these parallel processing patterns. We also present Cylon, as a reference runtime implementation. We demonstrate how this framework has enabled Cylon achieving scalable high performance. We also underline the flexibility of the proposed API and the extensibility of the framework on different hardware. To the best of our knowledge, Cylon is the first and only distributed-memory parallel dataframe system available today. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Memory architecture

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GPU4SNN: GPU-Based Acceleration for Spiking Neural Network Simulations 14th

GPU4SNN: GPU-Based Acceleration for Spiking Neural Network ...

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Satpute, Nitin Hambitzer, Anna Aljaberi, Saeed Aaraj, Najwa Cryptography Research Centre Technology Innovation Institute 9639 Masdar City Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031304415

Spiking Neural Networks (SNNs) are the most common and widely used artificial neural network models in bio-inspired computing. However, SNN simulation requires high computational resources. therefore, multiple state-of-the-art (SOTA) algorithms explore parallel hardware based implementations for SNN simulation, such as the use of Graphics processing Units (GPUs). However, we recognize inefficiencies in the utilization of hardware resources in the current SOTA implementations for SNN simulation, namely, the Neuron (N)-, Synapse (S)-, and Action Potential (AP)-algorithm. this work proposes and implements two novel algorithms on an NVIDIA Ampere A100 GPU: the Active Block (AB)- and Single Kernel Launch (SKL)-algorithm. the proposed algorithms consider the available computational resources on both, the Central processing Unit (CPU) and GPU, leading to a balanced workload for SNN simulation. Our SKL-algorithm is able to remove the CPU bottleneck completely. the average speedups obtained by the best of the proposed algorithms are factors of 0.83 ×, 1.36 × and 1.55 × in comparison to the SOTA algorithms for firing modes 0, 1 and 2 respectively. the maximum speedups obtained are factors of 1.9 ×, 2.1 × and 2.1 × for modes 0, 1 and 2 respectively. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Special issue on advances in techniques for assessment performance portability of HPC applications

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Future Generation Computer Systems 2025年 171卷

作者： Marowka, Ami Stpiczyński, Przemyslaw Wyrzykowski, Roman Parallel Research Lab Petach Tikva49729 Israel Institute of Computer Science Maria Curie-Sklodowska University Lublin20-033 Poland Department of Computer Science Czestochowa University of Technology Czestochowa42-201 Poland

this special issue aims to present new developments and advances in techniques for assessment performance portability of high performance computing applications. It contains revised and extended versions of selected papers presented at the 10th Workshop on Language-Based parallel Programming Models, WLPP 2024, which was a part of 15th international conference on parallel processing and applied mathematics, PPAM 2024, held on September 8–11, 2024, in Ostrava, Czech Republic. © 2025

关键词： HPC applications parallel programming languages Performance portability

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An Improved Algorithm for Fredholm Integral Equations 14th

An Improved Algorithm for Fredholm Integral Equations

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14th international conference on Large-Scale Scientific Computations (LSSC)

作者： Todorov, Venelin Georgiev, Slavi Apostolov, Stoyan Dimov, Ivan Bulgarian Acad Sci Inst Math & Informat Dept Informat Modeling Acad Georgi Bonchev StrBlock 8 Sofia 1113 Bulgaria Bulgarian Acad Sci Inst Informat & Commun Technol Dept Parallel Algorithms Acad Georgi Bonchev StrBlock 25A Sofia 1113 Bulgaria Angel Kanchev Univ Ruse Fac Nat Sci & Educ Dept Appl Math & Stat 8 Studentska Str Ruse 7004 Bulgaria Sofia Univ Fac Math & Informat Sofia Bulgaria

ISBN: (纸本)9783031562075;9783031562082

Integral equations are of high applicability in different areas of applied mathematics, physics, engineering, geophysics, electricity and magnetism, kinetic theory of gases, quantum mechanics, mathematical economics, and queuing theory. that is why it is reasonable to develop and study efficient and reliable approaches to solve integral equations. For multidimensional problems the existing biased stochastic algorithms based on evaluation of finite number of integrals will suffer more from the effect of high dimensionality, because they are based on quadrature points. So we need advanced unbiased algorithms for solving the multidimensional problem which will be developed in this paper.

关键词： Multidimensional Fedholm integral equation Unbiased approach Monte Carlo method

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Using AI-based Edge processing in Monitoring the Pedestrian Crossing 14th

Using AI-based Edge Processing in Monitoring the Pedestria...

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Karbowiak, Lukasz Kubanek, Mariusz Czestochowa University of Technology Dabrowskiego 69 Czestochowa42 -201 Poland

ISBN: (纸本)9783031304415

In edge processing, data collection devices are used to pre-filter data. As a result, only data of interest will be written to memory. this approach significantly reduces the amount of transferred data. Various algorithms, such as background segmentation or artificial intelligence (AI) techniques based on various neural networks, can be used to detect data of interest. this paper uses an AI-based technique in the edge processing environment to perform the learning process with a chosen neural network. the environment containing Nvidia Jetson Xavier NX is employed to train the MobileNetV3 network dedicated to detecting objects of interest like people or vehicles while monitoring the pedestrian crossing. the network consists of the initial fully connected convolution layer with 32 filters, followed by 19 residual bottleneck layers. this paper also proposes a learning process that uses the collected data after manual validation and significantly improves accuracy over the original network. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge computing

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Heart Arrhythmia Detection with Novel Approach H3-SAD 8

Heart Arrhythmia Detection with Novel Approach H3-SAD

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8th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Cakal, Kursat Efe, Mehmet Onder Hacettepe Univ Comp Engn Dept TR-06800 Ankara Turkey

ISBN: (纸本)9781665496070

Research on signals collected from the human heart has been a core subject area as the heart displays a rich set of dynamical information that needs careful analysis for medical diagnosis and treatment. the acquisition of the electrical activity signals is a convenient way to analyze, control, evaluate and understand the heart. Electrocardiography (ECG) measurements are used to categorize the heartbeat behaviors to achieve classification. ECG heartbeat signal classification methods range from classical signal processing to convolutional neural networks. Heterogeneous Harmonization of Heartbeat Signals for Arrhythmia Detection (H3-SAD) method based CNN is proposed in this study. H3-SAD method differs from other methods in the literature with its robust and tempered classification ability against heterogeneous spectrums of ECG Signal by targeting being a part of high mobility lifestyle. Literature studies have reasonable estimation rates for MIT-BIH Dataset but not for the heterogeneous acquisition of data in real-life applications. the key point that tempers our classification algorithm is applied dynamic augmentation details towards different signal sources and input values that adduct data to real-life, and heterogeneous augmentation-based CNN architecture.

关键词： Heart Heart beat Heuristic algorithms Pattern classification Electrocardiography Signal processing Classification algorithms

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An Illicit Bitcoin Address Analysis Scheme Based on Subgraph Evolution 24

An Illicit Bitcoin Address Analysis Scheme Based on Subgraph...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yang, Qingqing Xiang, Yuexin Liu, Wenmao Ren, Wei School of Computer Science China University of Geosciences Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430078 China Nsfocus Technologies Group Co. Ltd. Beijing China

ISBN: (纸本)9798350319934

Bitcoin, due to its decentralization and anonymity, is more frequently utilized in cybercrimes such as money laundering, darknet market, and blackmail. In order to maintain the development of cryptocurrency communities, it is vital to fight against these cryptocurrency-related (e.g., Bitcoin) cybercrimes. To better monitor cryptocurrency-based cybercrime, it is essential for a deeper understanding of the patterns that exist in Bitcoin transactions. In this paper, we propose a novel framework consisting of empirical analysis and machine learning-based analysis for studying three typical categories of Bitcoin addresses from a dynamic perspective of subgraph evolution. Specifically, we collect 22,001 continuous blocks to build a Bitcoin transaction graph and extract subgraphs from the constructed graph for each address in different periods. In the empirical analysis, we have several important findings from three perspectives. While in the machine learning-based analysis, we propose a 110-dimensional feature set for classification tasks among these three types of Bitcoin addresses, where decision tree, k-nearest neighbor algorithm, and random forest are applied. the experimental results illustrate that metrics of all classifiers can achieve 0.88, and the highest accuracy rate reaches 0.95 in the random forest classifier. In addition, we explore the feature importance in the random forest using the SHAP library and discuss several interesting phenomena that appear in the evolution process of the subgraphs. © 2022 IEEE.

关键词： Bitcoin

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Sparse Matrix Ordering for Fine Grain parallel Triangular Solve Using SIMD 15th

Sparse Matrix Ordering for Fine Grain Parallel Triangular S...

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15th international conference on parallel processing and applied mathematics, PPAM 2024

作者： Maarouf, Aboul-Karim Mohamed El Giraud, Luc Guermouche, Abdou Guignon, thomas IFP Energies nouvelles Solaize France Inria Centre of Bordeaux University Talence France LaBRI Université de Bordeaux Bordeaux France

ISBN: (纸本)9783031856969

the evolution of processor hardware increasingly supports fine grain parallelism through SIMD (Single Instruction, Multiple Data) vector instruction sets and hardware threading. For instance, the new ARM SVE instruction set allows for hardware implementation of up to 32 double precision SIMD vector sizes per hardware thread. In this work, we focus on vectorization of the triangular solves required in BiCGStab preconditioned with ILU(0) that is particularly numerically effective for IFPEN applications. In our context, expressing some parallelism can be achieved by changing the sparse structure of the matrices through unknown ordering;that can be recast in terms of graph ordering and coloring. We use a graph coloring method named ColorRCM to exhibit fine grain parallelism to feed the SIMD computing units while improving the convergence of the Krylov solver compared to classical greedy graph coloring method. We first evaluate the performance of SIMD-SpTRSV using the permutation provided by ColorRCM and achieve an acceleration between 1.7 and 6 in AVX2 compared to Intel MKL 21.4. then we examine the impact of ColorRCM ordering on ILU(0)-BiCGStab performance on 201 matrices, including those from the Suite Sparse matrix (the University of Florida Sparse Matrix Collection: https://***/10.1145/2049662.2049663) collection and from the IFPEN porous media flow simulations. the solver configuration uses the ColorRCM ordering and vectorized with AVX2 instructions showed the best convergence times in two thirds of the tests. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Matrix algebra

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Kokkos-Based Implementation of MPCD on Heterogeneous Nodes 1

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Halver, Rene Junghans, Christoph Sutmann, Godehard Jülich Supercomputing Centre Institute for Advanced Simulation Forschungszentrum Jülich Jülich52425 Germany Los Alamos National Laboratory CCS-7 Los AlamosNM87545 United States ICAMS Ruhr-University Bochum Bochum44801 Germany

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

the Kokkos based library Cabana, which has been developed in the Co-design Center for Particle Applications (CoPA), is used for the implementation of Multi-Particle Collision Dynamics (MPCD), a particle-based description of hydrodynamic interactions. It allows a performance portable implementation, which has been used to study the interplay between CPU and GPU usage on a multi-node system. As a result, we see most advantages in a homogeneous GPU usage, but we also discuss the extent to heterogeneous applications, using both CPU and GPU concurrently. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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