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检索条件"任意字段=Distributed Memory Computing Conference"

共 8890 条记录，以下是61-70 订阅

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International conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

International Conference on 6G Communications Networking and...

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International conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

ISBN: (纸本)9789819778751

The proceedings contain 34 papers. The special focus in this conference is on 6G Communications Networking and Signal Processing. The topics include: Secure Clustering and Routing Based on Modified Whale Optimization Algorithm in MANET;improved Whale Optimization Algorithm Based Energy Efficient Secure Routing in Mobile Wireless Sensor Networks;resource Allocation in Cloud computing Using Quasi-Opposition Learning Based Aquila Optimizer;the Quasi-Oppositional-Based Learning Aquila Optimization Routing Protocol in Energy-Aware Wireless Communication;exponential-Weighted Ant Lion Optimization Based Clustering Algorithm for Wireless Body Sensor Networks;Optimized Routing Strategy in Mobile Sink Based WSN Using Multi-objective Osprey Optimization Algorithm;the Coverage Optimization in Wireless Sensor Networks Using Improved Artificial Rabbit Optimization;improved Moth Flame Optimization Based Cluster Head Selection and Data Aggregation Using Machine Learning Approach;Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN;an Effective Energy Consumption and Weighted Clustering Using Improved K-Means Clustering;adaptive Weight Based Harris Hawks Optimization for Energy-Efficient Cluster Head Selection and Routing in Wireless Sensor Networks;An Optimal Cluster Head and Secure Route Path Selection Using Multi Objective—GWO Approach in Wireless Sensor Networks;peak-to-Average Power Ratio Reduction in Orthogonal Frequency Division Multiplexing Using Improved Butterfly Optimization Algorithm;energy Efficient-Based Cluster Head Selection and Routing Protocol for Wireless Sensor Networks Using Aquila Optimizer;ensemble Artificial Neural Network and Support Vector Machine Based Parameter Evaluation in Wireless Sensor Networks;distributed Denial of Service Attack Detection in IoT Utilizing Attention Mechanism Based Long Short-Term memory;e-commerce Payment System Empowered by Blockchain for Enhanced Security.

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A Cloud+HPC hub for astrophysics and cosmology communities

A Cloud+HPC hub for astrophysics and cosmology communities

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Matteo Pasqui ICSC “Centro Nazionale di Ricerca in HPC Big Data and Quantum Computing” INFN Sezione di Roma Tor Vergata

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The advancement of data-intensive analysis has emphasised the need for extensive, scalable environments that enable easy access to diverse computational resources. This project proposes the development of a Scientific Hub for the astrophysics and cosmology communities, leveraging a global, cloud-based infrastructure that integrates Kubernetes-based offloading to ensure efficient, user-friendly access to High Performance computing (HPC), High Throughput computing (HTC) and cloud resources. This solution introduces “transparent offloading” of containerized workloads using Kubernetes API primitives, creating a unified cloud-native interface that abstracts complexities and optimally allocates resources based on the specific needs of each task. Through the Scientific Hub, users could access JupyterHub-supported environments with configurable resources, persistent data storage and secure authentication, facilitating the execution and sharing of scientific workflows in a robust, scalable manner. This contribution showcases the aims and challenges of the development of the Scientific Hub on ICSC partners’ resources (including the Leonardo supercomputer at CINECA), along with some key use cases.

关键词： Astrophysics High performance computing memory Logic gates Fasteners Throughput Data processing Supercomputers Computational efficiency Complexity theory

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Federated Meta Continual Learning for Efficient and Autonomous Edge Inference 24th

Federated Meta Continual Learning for Efficient and Autono...

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24th International conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Bingze Ho, Stella Qu, Youyang Xu, Chenhao Luan, Tom H. Gao, Longxiang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Biomedical Engineering The University of Melbourne VIC Melbourne Australia School of Information Technology Deakin University VIC Geelong Australia School of Cyber Engineering Xidian University Xi’an China

ISBN: (纸本)9789819615476

With an increasing volume of data being collected and processed on edge, Federated Learning (FL) has become a dominant approach to train shared global models on distributed data held by diverse edge clients without exposing privacy. However, with expectations of high-quality user experience, instantaneous intelligent decisions must be made at the network edge to meet performance, security, and latency requirements. Intuitively, this challenge becomes more daunting as edge devices are constrained by limited computing and storage resources. Furthermore, edge clients need to continuously acquire, update, accumulate, and apply knowledge in domains requiring the processing of sensitive data and long-term model training. It has been proven that edge clients often forget previous tasks knowledge after learning new tasks, resulting in catastrophic forgetting. To mitigate this, we propose a Federated Meta Continual Learning (FMCL) approach. It requires edge clients not only to learn new tasks quickly but also to memorize rapidly. Specifically, we adopted a MAML (Model-Agnostic Meta-Learning) framework with memory replay, setting up buffers at edge clients to consolidate old tasks. In such a way, the model learns optimal initialization to quickly adapt to all seen tasks while preventing catastrophic forgetting. Extensive evaluation results indicate that FMCL performs well in alleviating catastrophic forgetting and achieves a good balance between learning new tasks and remembering existing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Towards an Optimized Heterogeneous distributed Task Scheduler in OpenMP Cluster 24

Towards an Optimized Heterogeneous Distributed Task Schedule...

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Proceedings of the SC '24 Workshops of the International conference on High Performance computing, Network, Storage, and Analysis

作者： Rémy Neveu Rodrigo Ceccato Gustavo Leite Guido Araujo Jose M. Monsalve Diaz Hervé Yviquel Instituto de Computação Universidade Estadual de Campinas (UNICAMP) Campinas Brazil

ISBN: (纸本)9798350355543

This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. However, improved scheduling mechanisms are critical to achieving performance and portability in distributed and heterogeneous environments. OpenMP Cluster (OMPC) was introduced to overcome these limitations, extending OpenMP with the Heterogeneous Earliest Finish Time (HEFT) task scheduling algorithm tailored for large-scale systems. To improve scheduling and enable better system utilization, the runtime system must resolve challenges such as changes in the application balance, amount of parallelism, and varying communication *** work presents three key contributions: first, the refactoring of the OMPC runtime to unify task scheduling across devices and hosts; second, the optimization of the HEFT-based scheduling algorithm to ensure efficient task execution in distributed environments; and third, an extensive evaluation of Work Stealing and HEFT scheduling mechanisms in real-world clusters. While the HEFT implementation in OMPC is not fully optimized, this work provides a significant step toward improving distributed task scheduling in cluster computing, offering insights and incremental advancements that support the development of scalable and high-performance applications. Results show improvements of up to 24% in scheduling time while opening up to more extensions in the scheduling methods.

关键词： distributed architectures

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Sequences of distributed Matrix-Vector Product for Very Large and Very Sparse Irregular Matrices 24

Sequences of Distributed Matrix-Vector Product for Very Larg...

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Proceedings of the SC '24 Workshops of the International conference on High Performance computing, Network, Storage, and Analysis

作者： Maxence Vandromme Nicolas Hochart Serge G. Petiton Jérôme Gurhem Miwako Tsuji Mitsuhisa Sato Univ. Lille UMR 9189 - CRIStAL CNRS Lille France USR 3441 - Maison de la Simulation CNRS Saclay France RIKEN Center for Computational Science Kobe Japan

ISBN: (纸本)9798350355543

We study the performance behavior of the sparse matrix-vector product operation in distributed computing environments, in the case of very large non-diagonal matrices where the nonzero elements are placed irregularly across the matrix. In particular, we focus on the distributed storage of the result vector in cases where it becomes too large to be stored fully on each process, and its redistribution between the iterations of a sequence of SpMV operations. We propose two methods to this effect; one aims at minimizing the memory requirements of storing the result vector, the other optimizes the communications required for the redistribution. We perform large-scale experiments on the Fugaku supercomputer, in order to show the importance of taking into account the network topology to correctly identify and address communication bottlenecks. The results show that the most efficient proposed method has a runtime several times faster than a non-optimal one on this topology.

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On the Effectiveness of Unified memory in Multi-GPU Collective Communication

On the Effectiveness of Unified Memory in Multi-GPU Collecti...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Riccardo Strina Ian Di Dio Lavore Marco Santambrogio Michael Papka Zhiling Lan Politecnico di Milano Italy Argonne National Laboratory USA University of Illinois Chicago USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Modern supercomputers are becoming increasingly dense with accelerators. Industry leaders offer multi-GPU architectures with high interconnection bandwidth between the devices to match the requirements of modern workloads. While those technologies advance, it is up to the programmer to successfully exploit them. Recognizing this burden, multiple abstractions have been built. We focus on the NVIDIA Collective Communication Library (NCCL) and Unified memory (UM). The former provides MPI-like directives integrated within the GPU runtime, allowing lower latencies and increasing the bandwidth over previous approaches. The latter simplifies the programming paradigm, offering a unified virtual address space. Moreover, it enables memory oversubscription, drastically reducing the efforts towards handling larger problems without completely restructuring the codebase. This work provides the first joint analysis of NCCL and UM from single-node multi-GPU architectures to a production supercomputer. We explore all the available collective communication directives concerning their power requirements and overall throughput. Moreover, we study the effects of various hyperparameters, e.g., message sizes, oversubscription level, and memory advice, on the overall obtainable performance. Our findings showcase how using UM brings negligible increased energy consumption; moreover, in distributed settings, other restricting factors, such as network bottlenecks, surpass the overhead introduced by UM’s page-eviction mechanisms.

关键词： Power demand Runtime memory management Graphics processing units Production Bandwidth Programming Throughput Supercomputers Resource management

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A GPU Implementation of the Analog Ensemble Method

A GPU Implementation of the Analog Ensemble Method

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Ruben Crico Ines Charles Carlos Balsa José Rufino Université de Toulouse - INP - ENSEEIHT Toulouse France Research Centre in Digitalization and Intelligent Robotics (CeDRI) Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança Bragança Portugal

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The Analog Ensemble (AnEn) method can be used to reconstruct incomplete time series using correlated series. Since the AnEn method may use data including several variables through long periods of time, its storage and computational cost may be substantial, slowing down reconstructions. This paper presents a full GPU implementation of the AnEn method, based on PyCUDA, that leads to a significant a cceleration of its execution. The implementation resorts to several techniques that seek to minimize the consumption of GPU global memory in the various steps of the AnEn algorithm, thus making room for larger input datasets. This is further reinforced by the use of batch processing as a way to automatically adapt the datasets size to the GPU memory available. The GPU implementation was tested on a meteorological dataset spanning 10 years, exhibiting a 30-fold speedup in the reconstruction time against a comparable CPUbased multicore version executed with up to 48 cores. The impact on the reconstruction error of changes on several important parameters of the implementation was also assessed.

关键词： Training Multicore processing Scientific computing Time series analysis memory management Graphics processing units Batch production systems Random access memory Computational efficiency Sorting

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Parallel computing Optimization of Tourism Big Data based on Crawler Programs

Parallel Computing Optimization of Tourism Big Data based on...

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Intelligent Systems and Computational Networks (ICISCN), International conference on

作者： Xiaoyu Zhou School of Management Wuhan Donghu University Wuhan China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy.

关键词： Accuracy Tourism industry Crawlers Standardization Big Data Prediction algorithms Data processing User experience Sparks Optimization

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Conditional Recurrent Neural Networks for Enhancing Throughput Prediction and Slow File Transfers Detection in Large Science Workflows

Conditional Recurrent Neural Networks for Enhancing Throughp...

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Consumer Communications and Networking conference, CCNC IEEE

作者： Boyu Fan Alex Sim Kesheng Wu Jinoh Kim University of California Berkeley Lawrence Berkeley National Laboratory Energy Sciences Network Texas A&M University-Commerce

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Efficient data transfer across scientific computing facilities is critical for enabling timely scientific discoveries. In this work, we explore the options of anticipating extremely slow data transfers to enable preventive actions. However, the dynamic nature of the large distributed scientific workflows driving these data transfers presents significant challenges for predicting network throughput. This study introduces a Conditional Recurrent Neural Network (CondRNN) model, specifically utilizing Conditional Long Short-Term memory (CondLSTM), to integrate both static and dynamic features for enhanced throughput prediction. By leveraging historical transfers as proxy features, more than 60% of predictions achieved an absolute percentage error (APE) of less than 20%, and slow transfers were detected with a precision of 91.7% and recall of 100%, outperforming traditional RNN models. Implementing CondLSTM in scientific computing environments can optimize network resource utilization, ensuring efficient data transmission, thereby supporting the continuous progression of scientific research.

关键词： Recurrent neural networks Accuracy Scientific computing Computational modeling Predictive models Throughput Data transfer Data models Resource management Long short term memory

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Lamellar: A Rust-based Asynchronous Tasking and PGAS Runtime for High Performance computing 24

Lamellar: A Rust-based Asynchronous Tasking and PGAS Runtime...

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Proceedings of the SC '24 Workshops of the International conference on High Performance computing, Network, Storage, and Analysis

作者： Ryan D. Friese Roberto Gioiosa Joseph Cottam Erdal Mutlu Gregory Roek Polykarpos Thomadakis Mark Raugas Pacific Northwest National Laboratory Richland Washington USA

ISBN: (纸本)9798350355543

The discussion around "safe" programming languages has significantly increased in recent years, and is impacting how governments, industry, and academia plan to develop current and future software products. The White House Office of the National Cyber Director released a report [1] in February 2024 calling on the technical community to work towards proactively reducing attack surfaces in cyberspace, in part, specifically by adopting memory safe programming languages. While the main discourse thus far has been focused on cybersecurity, memory safety issues are also a concern in HPC, where memory related errors can result in wasted execution time, incorrect results, etc. Legacy programming languages in HPC such as C and C++ provide freedom and flexibility with memory management, but requires the developer to guarantee safety. While it is possible to develop "un-safe" code in all programming languages, "memory-safe" languages help guarantee safety by utilizing various compile time and runtime checks and validation *** this paper we introduce Lamellar, an asynchronous tasking and PGAS runtime system for HPC written in Rust, one such "memory-safe" language. We describe the entire Lamellar stack, from network interfaces to safe high-level abstractions such as distributed LamellarArrays and Active Messages. The goal of our runtime is to enable end-users to develop entirely safe Rust code in their applications, limiting the use of any "unsafe" code blocks to rigorously tested code blocks within the runtime itself. We conclude by showing comparable performance against several C, C++, and Chapel implementations of a subset of the BALE kernel suite while maintaining strong memory safety principles.

关键词： Asynchronous

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