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29th IEEE International parallel and distributed Processing Symposium (IPDPS)

作者： Lu, Guoming Xu, Jie Liu, Jieyan Dai, Bo Gui, Shenglin Zhan, Siyu Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781467376846

In Fall 2013, we began participation in NSF/IEEE TCPP 2013 Early Adopters Program. This paper presents our efforts to incorporate parallel and distributed computing topics into our undergraduate computer science and engineering curriculum with the guide of the IEEE-TCPP model Curriculum. So far, TCPP recommended curriculum has been integrated eight courses, evaluations show our integration effort is successful. Evaluation also shows that practices such as lab/homework assignment effective improve students' conception, and contest club is a necessary complementarity of in-class courses.

关键词： parallel and distributed computing Curriculum reformation Computer Science undergraduate Curriculum

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NSF/IEEE-TCPP curriculum initiative on parallel and distributed computing: core topics for undergraduates (abstract only) 14

NSF/IEEE-TCPP curriculum initiative on parallel and distribu...

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Proceedings of the 45th ACM technical symposium on Computer science education

作者： Sushil K. Prasad Almadena Chtchelkanova Anshul Gupta Arnold Rosenberg Alan Sussman Georgia State University Atlanta USA National Science Foundation Arlington USA IBM T.J. Watson Research Yorktown USA Northeastern University Boston USA University of Maryland College Park USA

ISBN: (纸本)9781450326056

parallelism pervades all aspects of modern computing, from in-home devices such as cell phones to large-scale supercomputers. Recognizing this - and motivated by the premise that every undergraduate student in a computer-related field should be prepared to cope with parallel computing - a working group sponsored by NSF and IEEE/TCPP, and interacting with the ACM CS2013 initiative, has developed guidelines for assimilating parallel and distributed computing (PDC) into the core undergraduate curriculum. Over 100 Early-Adopter institutions worldwide are currently modifying their computer-related curricula in response to the guidelines. Additionally, the CDER Center for Curriculum Development and Educational Resources, which grew out of the working group, is currently assembling a book of contributed essays on how to teach PDC topics in lower-level CS/CE courses, to fill the serious lack of textual material for students and *** session is intended: (i) to report on the current state of this initiative; (ii) to bring together authors of book chapters and Early Adopters and other interested parties for discussions on ongoing activities and needs; (iii) to discuss the initiative and collect direct feedback from the community.

关键词： education parallel and distributed computing early adopters learning outcomes educational resources undergraduate curriculum bloom's classification

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Innovations in mathematical modeling, AI, and optimization techniques

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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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Mathematical modeling and problem solving: from fundamentals to applications

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JOURNAL OF SUPERcomputing 2024年第10期80卷 14116-14119页

作者： Ohue, Masahito Sasayama, Kotoyu Takata, Masami Tokyo Inst Technol Sch Comp Dept Comp Sci Yokohama Kanagawa 2268501 Japan Mitsubishi Elect Bldg Solut Corp Elevator & Escalator Dev Dept Aichi 4928682 Japan Nara Womens Univ Res Grp Informat & Commun Technol Life Nara 6308506 Japan

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on parallel and distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas.

关键词： Mathematical modeling Machine learning parallel and distributed computing Drug discovery and materials science

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GSParLib: A multi-level programming interface unifying OpenCL and CUDA for expressing stream and data parallelism

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COMPUTER STANDARDS & INTERFACES 2025年 92卷

作者： Rockenbach, Dinei A. Araujo, Gabriell Griebler, Dalvan Fernandes, Luiz Gustavo Pontif Catholic Univ Rio Grande do Sul PUCRS Sch Technol Porto Alegre Brazil

The evolution of Graphics Processing Units (GPUs) has allowed the industry to overcome long-lasting problems and challenges. Many belong to the stream processing domain, whose central aspect is continuously receiving and processing data from streaming data producers such as cameras and sensors. Nonetheless, programming GPUs is challenging because it requires deep knowledge of many-core programming, mechanisms and optimizations for GPUs. Current GPU programming standards do not target stream processing and present programmability and code portability limitations. Among our main scientific contributions resides GSParLib, a C++ multi-level programming interface unifying CUDA and OpenCL for GPU processing on stream and data parallelism with negligible performance losses compared to manual implementations;GSParLib is organized in two layers: one for general-purpose computing and another for high-level structured programming based on parallel patterns;a methodology to provide unified and driver agnostic interfaces minimizing performance losses;a set of parallelism strategies and optimizations for GPU processing targeting stream and data parallelism;and new experiments covering GPU performance on applications exposing stream and data parallelism.

关键词： Application programming interface Stream processing parallel programming parallel and distributed computing Graphics processing units

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Low Complexity SLP: An Inversion-Free, parallelizable ADMM Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第9期23卷 12424-12439页

作者： Yang, Junwen Li, Ang Liao, Xuewen Masouros, Christos Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Shaanxi Key Lab Deep Space Explorat Intelligent In Xian 710049 Shaanxi Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England

We propose a parallel constructive interference (CI)-based symbol-level precoding (SLP) approach for massive connectivity in the downlink of multiuser multiple-input single-output (MU-MISO) systems, with only local channel state information (CSI) used at each processor unit and limited information exchange between processor units. We explore and reveal the separability of the SLP model. By reformulating the power minimization (PM) SLP problem and exploiting the separability of the corresponding reformulation, the original problem is decomposed into several parallel subproblems via the ADMM framework with closed-form solutions, leading to a substantial reduction in computational complexity. The sufficient condition for guaranteeing the convergence of the proposed approach is derived, based on which an adaptive parameter tuning strategy is proposed to accelerate the convergence rate. To avoid the large-dimension matrix inverse operation, an efficient algorithm is proposed by employing the standard proximal term and by leveraging the singular value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted to fully eliminate the matrix inversion, and a parallel inverse-free SLP (PIF-SLP) algorithm is finally obtained. Numerical results validate our derivations above, and demonstrate that the proposed PIF-SLP algorithm can significantly reduce the computational complexity compared to the state-of-the-arts.

关键词： Precoding Interference Convergence Vectors Signal to noise ratio Optimization Computational complexity Massive MU-MISO constructive interference symbol-level precoding power minimization ADMM parallel and distributed computing

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Layer-wise partitioning and merging for efficient and scalable deep learning

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期149卷 432-444页

作者： Akintoye, S. B. Han, L. Lloyd, H. Zhang, X. Dancey, D. Chen, H. Zhang, D. Manchester Metropolitan Univ Dept Comp & Math Manchester England Univ Sheffield Dept Comp Sci Sheffield England Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking problems, leading to poor performance in terms of training time. The existing parallel strategies to mitigate these problems provide suboptimal runtime performance. In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance. The novelty of the proposed work consists of (1) a layer-wise partition and merging model which can minimise communication overhead between devices without the memory cost of existing strategies during the training process;(2) a forward pass and backward pass parallelisation to address the update locking problem and minimise the total training cost. The experimental evaluation on real use cases shows that the proposed method outperforms the state-of-the-art approaches in terms of training speed;and achieves almost linear speedup without compromising the accuracy performance of the non-parallel approach.& COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Deep Neural Network Deep learning Data parallelism Model parallelism parallel and distributed computing High performance computing

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An Algebraic Multigrid-Based Physical Factorization Preconditioner for the Multi-Group Radiation Diffusion Equations in Three Dimensions

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 410-432页

作者： Xiaoqiang Yue Zekai Zhang Xiaowen Xu Shuying Zhai Shi Shu National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100094China School of Mathematical Sciences Huaqiao UniversityQuanzhou 362021China.

The paper investigates the robustness and parallel scaling properties of a novel physical factorization preconditioner with algebraic multigrid subsolves in the iterative solution of a cell-centered finite volume discretization of the threedimensional multi-group radiation diffusion *** key idea is to take advantage of a particular kind of block factorization of the resulting system matrix and approximate the left-hand block matrix selectively spurred by parallel processing *** spectral property of the preconditioned matrix is then *** practical strategy is considered sequentially and in ***,numerical results illustrate the numerical robustness,computational efficiency and parallel strong and weak scalabilities over the real-world structured and unstructured coupled problems,showing its competitiveness with many existing block preconditioners.

关键词： Radiation diffusion equations physical factorization preconditioning algebraic multigrid parallel and distributed computing

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Analysis of a parallel and distributed BPSO Algorithm for EEG Classification: Impact on Energy, Time and Accuracy 11th

Analysis of a Parallel and Distributed BPSO Algorithm for EE...

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11th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Escobar, Juan Jose Lopez-Rodriguez, Jesus Garcia-Gil, Diego Morcillo-Jimenez, Roberto Prieto, Beatriz Ortiz, Andres Kimovski, Dragi Univ Granada CITIC Dept Software Engn Granada Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada Spain Univ Granada CITIC Dept Comp Engn Automat & Robot Granada Spain Univ Granada CITIC Dept Comp Sci & Artificial Intelligence Granada Spain Univ Malaga Dept Commun Engn Malaga Spain Univ Klagenfurt Inst Informat Technol Klagenfurt Austria

ISBN: (纸本)9783031646287;9783031646294

Today, the amount of data generated each year is growing exponentially, directly affecting the time required for its analysis. This problem worsens with high-dimensional datasets, such as those used in electroencephalography, so a good feature selection method and techniques that improve algorithms' efficiency are increasingly relevant. Consequently, computing time and energy consumption are reduced, which could be used to explore more solutions to the problem. However, it is also necessary to adapt the applications to take advantage of the hardware offered by high-performance computing systems. Therefore, in this work, a parallel and distributed binary particle swarm optimization algorithm has been implemented, used as a feature selection method, and applied to two real electroencephalography datasets: the University of Essex dataset and the well-known BCI Competition IV 2a dataset. The proposed method has been analyzed in a multi-node computing cluster, not only in terms of classification accuracy, but also from the energy-time point of view to study its impact depending on different experimental conditions and datasets used.

关键词： parallel and distributed computing Energy impact EEG classification KNN Particle swarm optimization Feature selection

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DS-ADMM plus plus : A Novel distributed Quantized ADMM to Speed up Differentially Private Matrix Factorization

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第6期33卷 1289-1302页

作者： Zhang, Feng Xue, Erkang Guo, Ruixin Qu, Guangzhi Zhao, Gansen Zomaya, Albert Y. China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Oakland Univ Dept Engn & Comp Sci Rochester MI 48309 USA South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Univ Sydney Sch Comp Sci Camperdown NSW 2006 Australia

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent efficiency, to achieve the following two pieces of work: (1) Integrated local differential privacy paradigm into DS-ADMM to provide the privacy-preserving property;(2) Introduced a stochastic quantized function to reduce transmission overheads in ADMM to further improve efficiency. We named our work DS-ADMM++, in which one '+' refers to differential privacy, and the other '+' refers to quantized techniques. DS-ADMM++ is the first to perform efficient and private matrix factorization under the scenarios of differential privacy and DS-ADMM. We conducted experiments with benchmark data sets to demonstrate that our approach provides differential privacy and excellent scalability with a decent loss of accuracy.

关键词： Differential privacy Quantization (signal) Matrix decomposition Recommender systems Charge coupled devices Machine learning Costs Matrix factorization parallel and distributed computing differential privacy ADMM quantization

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