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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

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21st IFIP WG 6.1 international conference on distributed applications and Interoperable Systems, DAIS 2021 held as part of 16th international Federated conference on distributed Computing techniques, DisCoTec 2021

21st IFIP WG 6.1 International Conference on Distributed App...

引用

21st IFIP WG 6.1 international conference on distributed applications and Interoperable Systems, DAIS 2021 held as part of 16th international Federated conference on distributed Computing techniques, DisCoTec 2021

ISBN: (纸本)9783030781972

The proceedings contain 10 papers. The special focus in this conference is on distributed applications and Interoperable Systems. The topics include: Simulation of Large Scale Computational Ecosystems with Alchemist: A Tutorial;network Federation for Inter-cloud Operations;specK: Composition of Stream processing applications over Fog Environments;ASPAS: As Secure as Possible Available Systems;portable Intermediate Representation for Efficient Big Data Analytics;shared-Dining: Broadcasting Secret Shares Using Dining-Cryptographers Groups;UCBFed: Using Reinforcement Learning Method to Tackle the Federated Optimization Problem;keVlar-Tz: A Secure Cache for Arm TrustZone: (Practical Experience Report);analysis and Improvement of Heterogeneous Hardware Support in Docker Images.

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HPC-in-Containers: A Containerized parallel Environment for parallel Programming Learning Using Docker

HPC-in-Containers: A Containerized Parallel Environment for ...

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international conference on Information Technology Systems and Innovation (ICITSI)

作者： Andria Arisal Heru Suhartanto Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting.

关键词： Visualization Technological innovation Portable computers parallel programming Source coding Instruction sets Education parallel processing User experience Monitoring

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Layer-wise Top-$k$ Gradient Sparsification for distributed Deep Learning

Layer-wise Top-$k$ Gradient Sparsification for Distributed D...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Guangyao Li Institute of Automation Chinese Academy of Sciences Beijing China

distributed training is widely used in training large-scale deep learning models, and data parallelism is one of the dominant approaches. Data-parallel training has additional communication overhead, which might be the bottleneck of training system. Top- $\boldsymbol{k}$ sparsification is a successful technique to reduce the communication volume to break the bottleneck. However, top- $\boldsymbol{k}$ sparsification cannot be executed until backpropagation is completed, which disables the overlap of backpropagation computations and gradient communications, leading to limiting the system scaling efficiency. In this paper, we propose a new distributed optimization approach named LKGS-SGD, which combines synchronous SGD (S-SGD) with a novel layer-wise top- $\boldsymbol{k}$ sparsification algorithm (LKGS). The LKGS-SGD enables the overlap of computations and communications, and adapts to gradient exchange at layer-wise. Evaluations are conducted by real-world applications. Experimental results show that LKGS-SGD achieves similar convergence to dense S-SGD, while outperforming the original S-SGD and S-SGD with top- $\boldsymbol{k}$ sparsification.

关键词： Training Backpropagation Deep learning Limiting Scalability distributed databases parallel processing

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parallel Implementation of SHA256 on Multizone Heterogeneous Systems

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Yongtao Luo Jie Liu Tiaojie Xiao Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing program for Frontier Equipment National University of Defense Technology Changsha China National Supercomputer Center in Tianjin Tianjin China

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability.

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Machine learning based network intrusion detection for data streaming IoT applications 21

Machine learning based network intrusion detection for data ...

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21st ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Winter)

作者： Yahyaoui, Aymen Lakhdhar, Haithem Abdellatif, Takoua Attia, Rabah Univ Carthage SERCOM Lab Mil Acad Fondouk Jedid Tunis Tunisia Mil Acad Fondouk Jedid Tunis Tunisia Univ Carthage SERCOM Lab Tunis Tunisia

ISBN: (纸本)9781665418430

In recent years, Internet of Things (IoT) technologies have been widely used in many fields such as surveillance, healthcare, smart metering and environment monitoring. This extensive usage leads to massive data management and a complexity in data analysis. A huge number of IoT sensors are deployed for monitoring task and send continuously their collected data to gateways. IoT applications are analyzing these data flows and making real time decisions about specific monitored events (fire, flood, terrorist attacks, etc.). Anomalies that may be related to sensor failures or network intrusions are affecting such decisions. Therefore, they should be detected and eliminated as soon as they arrive. This task requires real time data processing detectors for making accurate and fast predictions. In this paper, we design an architecture for a real time network intrusion detection system for IoT streaming data. The system was developed, deployed and tested with the two leading stream processing frameworks (Apache fink and Apache Spark Streaming). We used two different public datasets and different machine learning algorithms. Results show considerable throughputs and high detection accuracy especially for Apache Flink.

关键词： Anomaly detection Intrusion detection Stream processing Machine Learning Internet of Things

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10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

10th International Conference on Innovation and New Trends i...

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10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

ISBN: (纸本)9783031473654

The proceedings contain 15 papers. The special focus in this conference is on Innovation and New Trends in Information Technology. The topics include: Intelligent Multi-agent distributed System for Improving and Secure Travel Procedures: Al-Karama-King Hussein Bridge Study Case;a New Approach for the Analysis of Resistance to Change in the Digital Transformation Context;augmented Data Warehouses for Value Capture;a parallel processing Architecture for Querying distributed and Heterogeneous Data Sources;comprehensive Data Life Cycle Security in Cloud Computing: Current Mastery and Major Challenges;systematic Mapping Study on applications of Deep Learning in Stock Market Prediction;exploring the Knowledge Distillation;intelligent Traffic Congestion and Collision Avoidance Using Multi-agent System Based on Reinforcement Learning;BERT for Arabic NLP applications: Pretraining and Finetuning MSA and Arabic Dialects;VacDist MAS for Covid-19 Vaccination Distribution: Palestine as a Case Study;Smart E-Waste Management System Utilizing IoT and DL Approaches;towards a Platform for Higher Education in Virtual Reality of Engineering Sciences;Robust Intelligent Control for Two Links Robot Based ACO Technique.

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parallelizing Sieve of Eratosthenes Algorithm in Banking Application

Parallelizing Sieve of Eratosthenes Algorithm in Banking App...

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Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), international conference on

作者： P Priya Ponnuswamy Sadhana V C P Shabariram Kanderi Yaghna Chowdary Sanju Revanth A R Department of CSE PSG Institute of Technology and Applied Research Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The essential role of prime numbers in cryptographic within banking applications is emphasizing the significance of secure encryption keys and digital signatures. It delves into the optimization of prime number generation by parallelizing the Sieve of Eratosthenes algorithm, particularly for generating large prime numbers. The paper explores the practical implementation of parallel computing techniques like OpenMP and MPI to achieve this parallelization, highlighting its advantages. parallelization ensures the faster encryption of the account number which in turn enhances the However, challenges such as load balancing and communication overhead are also addressed, offering insights into their effective management. This research demonstrates the viability of parallelization for improved security and efficiency in banking cryptographic operations

关键词： Multi-factor authentication Scalability Message passing Banking Passwords parallel processing Reliability engineering

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parallel Committees: High-Performance, Scalable, Secure and Fault-Tolerant Data Replication Using a Novel Sharding Technique

Parallel Committees: High-Performance, Scalable, Secure and ...

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Blockchain Computing and applications (BCCA), international conference on

作者： Siamak Solat Farid Naït-Abdesselam Université de Paris Cité France ENGIE Laboratory for Computer Science & Artificial Intelligence France University of Missouri Kansas City USA

distributed replication systems that use consensus mechanisms to process clients' requests have major limitations and problems in scalability, throughput, and performance. Such problems are mainly due to the time and message complexity of the consensus algorithms used in such systems. Sharding has already been implemented in several blockchain-based replication systems and although it has shown remarkable potential to improve performance and scalability, yet current sharding techniques have several significant scalability and security issues. In this paper, we propose a novel sharding technique for distributed replication systems that use a consensus to process clients' requests. We modified the approach of how nodes are allocated between shards through the public key generation process. Our approach effectively reduces undesirable cross-shard transactions that are more complex and costly to process than intra-shard transactions. Also, there is no concept of Beacon chain — as a shared-ledger between shards — which imposes additional scalability limitations and security issues on the network. With our approach, a network using a classical distributed consensus algorithm can grow outstandingly in number of nodes and still remain permissionless,

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Towards an Optimized Heterogeneous distributed Task Scheduler in OpenMP Cluster

Towards an Optimized Heterogeneous Distributed Task Schedule...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

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: (数字)9798350355543

ISBN: (纸本)9798350355550

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： Runtime Scheduling algorithms parallel programming Optimal scheduling Cluster computing parallel processing Dynamic scheduling Large-scale systems Resource management Iterative methods

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A Novel Algorithm for Bi-objective Performance-Energy Optimization of applications with Continuous Performance and Linear Energy Profiles on Heterogeneous HPC Platforms 27th

A Novel Algorithm for Bi-objective Performance-Energy Optimi...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Khaleghzadeh, Hamidreza Manumachu, Ravi Reddy Lastovetsky, Alexey Univ Portsmouth Sch Comp Portsmouth Hants England Univ Coll Dublin Sch Comp Sci Dublin 4 Ireland

ISBN: (纸本)9783031061561;9783031061554

Performance and energy are the two most important objectives for optimization on heterogeneous HPC platforms. This work studies a mathematical problem motivated by the bi-objective optimization of a matrix multiplication application on such platforms for performance and energy. We formulate the problem and propose an algorithm of polynomial complexity solving the problem where all the application profiles of objective type one are continuous and strictly increasing, and all the application profiles of objective type two are linear increasing. We solve the problem for the matrix multiplication application employing five heterogeneous processors that include two Intel multicore CPUs, an Nvidia K40c GPU, an Nvidia P100 PCIe GPU, and an Intel Xeon Phi. Based on our experiments, a dynamic energy saving of 17% is gained while tolerating a performance degradation of 5% (a saving of 106 J for an execution time increase of 0.05 s).

关键词： Bi-objective optimization Min-max optimization Min-sum optimization Performance optimization Energy optimization

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