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parallel Implementation of SHA256 on Multizone Heterogeneous Systems 21

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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21st ieee International symposium on parallel and distributed processing with Applications, 13th ieee International Conference on Big Data and Cloud Computing, 16th ieee International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

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

ISBN: (纸本)9798350329223

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. © 2023 ieee.

关键词： Hash functions

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Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning 36

Why Globally Re-shuffle? Revisiting Data Shuffling in Large ...

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36th ieee International parallel and distributed processing symposium (ieee IPDPS)

作者： Nguyen, Truong thao Trahay, Francois Domke, Jens Drozd, Aleksandr Vatai, Emil Liao, Jianwei Wahib, Mohamed Gerofi, Balazs Natl Inst Adv Ind Sci & Technol Tokyo Japan Inst Polytech Paris Telecom SudParis Paris France RIKEN Ctr Computat Sci Tokyo Japan Southwest Univ China Coll Comp & Informat Sci Chongqing Peoples R China Tokyo Inst Technol Tokyo Japan Amigawa GK Tokyo Japan

ISBN: (纸本)9781665481069

Stochastic gradient descent (SGD) is the most prevalent algorithm for training Deep Neural Networks (DNN). SGD iterates the input data set in each training epoch processing data samples in a random access fashion. Because this puts enormous pressure on the I/O subsystem, the most common approach to distributed SGD in HPC environments is to replicate the entire dataset to node local SSDs. However, due to rapidly growing data set sizes this approach has become increasingly infeasible. Surprisingly, the questions of why and to what extent random access is required have not received a lot of attention in the literature from an empirical standpoint. In this paper, we revisit data shuffling in DL workloads to investigate the viability of partitioning the dataset among workers and performing only a partial distributed exchange of samples in each training epoch. through extensive experiments on up to 2,048 GPUs of ABCI and 4,096 compute nodes of Fugaku, we demonstrate that in practice validation accuracy of global shuffling can be maintained when carefully tuning the partial distributed exchange. We provide a solution implemented in PyTorch that enables users to control the proposed data exchange scheme.

关键词： Training Deep learning distributed processing Costs Neural networks distributed databases Stochastic processes

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ADSwitch: Adaptive CPU Scheduling for distributed Systems 21

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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作者： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. the experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 ieee.

关键词： Recurrent neural networks

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Degree-Aware Kernels for Computing Jaccard Weights on GPUs 36

Degree-Aware Kernels for Computing Jaccard Weights on GPUs

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36th ieee International parallel and distributed processing symposium (ieee IPDPS)

作者： Aljundi, Amro Alabsi Akyildiz, Taha Atahan Kaya, Kamer Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781665481069

Graphs provide the ability to extract valuable metrics from the structural properties of the underlying data they represent. One such metric is the Jaccard Weight of an edge, which is the ratio of the number of common neighbors of the edge's endpoints to the union of the endpoints' neighborhood. A naive implementation of Jaccard Weights computation has a complexity that scales with the number of edges in the graph times the square of the maximum degree. Recently, GPU-based parallel algorithms have been proposed for this problem. However, these algorithms cannot overcome the structural variance within a graph, i.e., the sparsity pattern and degree imbalance, which directly translates to unbalanced work distribution across threads. In this work, we propose an optimized GPU-based algorithm with an ML-based work distribution model that mitigates the unbalanced work distribution. Our algorithm is shown to be up to 35x and on average 12x faster than the state of the art in practice while using less memory. In fact, we show that by manually tweaking the load distribution, a state-of-the-art implementation can be 5x faster. In addition, we propose a multi-core, shared-memory algorithm that applies a traditional but effective technique to improve the computation asymptotically and perform comparably to the GPU algorithms. Our code is available at https://***/SU- HPC/Jaccard-ML

关键词： Jaccard Weights parallel graph algorithms GPU work distribution

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2023 5th International symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2023

2023 5th International Symposium on Robotics and Intelligent...

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5th International symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2023

ISBN: (纸本)9798350316087

the proceedings contain 74 papers. the topics discussed include: scene classification method based on CNN;forward and inverse solution analysis of 3-URU parallel mechanism;quality evaluation of automotive body robot roll-hemming forming process based on computer vision;a multi-objective optimal design method for differential evolution magnet motors based on deep stochastic configuration network;a new kind of seamless fabric setting machine and its numerical control system;energy optimal trajectory planning of welding robot based on improved particle swarm optimization algorithm;energy efficiency optimization of hexapod robots based on central pattern generator control;and recognition and tracking of metal clip-like objects based on deep learning semantic segmentation.

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ASimulated Logistic Algorithm for distributed Flexible Assembly Flowshop Scheduling with Post-processing 15

ASimulated Logistic Algorithm for Distributed Flexible Assem...

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15th International symposium on Industrial Electronics and Applications, INDEL 2024

作者： Hao, Haiqiang Zhu, Haiping School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350352320

An optimized mathematical model has been developed to streamline gas turbine production by targeting the minimization of the overall completion time, known as makespan. the production process is broken down into a series of stages: jobs are processed across multiple parallel factories, assembled into semi-finished goods with flexibility in their assembly order, and then transformed into final products through various stages of additional processing. To initiate solutions, a three-tier composite heuristic is employed, supplemented by six specialized neighborhood structures devised according to the different stages of the problem. Drawing inspiration from natural population growth mechanisms, we introduce a Simulated Logistic Algorithm (SLA) designed for robust solution enhancement. Comparative experiments on both small and extensive scales demonstrate the SLA's superior performance over established optimization techniques such as Particle Swarm Optimization, Tabu Search, and Genetic Algorithms, especially notable in large-scale applications. thorough mathematical and statistical evaluations reinforce the efficacy of the SLA and validate the proposed approach. © 2024 ieee.

关键词： Genetic algorithms

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Privacy-Preserving Federated Learning through Clustered Sampling on Fine-Tuning distributed non-iid Large Language Models 21

Privacy-Preserving Federated Learning through Clustered Samp...

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作者： Yun, Sheng Bhuiyan, Zakirul Alam Sadi, Md. Taufiq Al Hasib Su, Shen Fordham University Department of Computer and Information Sciences New York10458 United States North South University Department of Electrical & Computer Engineering Dhaka Bangladesh Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China

ISBN: (纸本)9798350329223

Recently, Large Language Models (LLMs) have been a phenomenal trend in the Artificial intelligence field. However, training and fine-tuning can be challenging because of privacy concerns and limited computing resources. Federated Learning (FL) has emerged as a novel machine learning framework offering privacy protection. the challenges in applying FL to real-world applications include dealing with heterogeneous data, poor client updates, and client selection. this paper introduces Privacy-preserving Federated Learning through Clustered Sampling on LLMs (FCLM), a framework that clusters models by their distribution similarity. It helps the model group similar models to improve text data heterogeneity handling and privacy concerns in distributed machine-learning environments. the FCLM framework is implemented and evaluated using popular Language models and text data. the framework shows a robust performance over the heterogeneous text data, which can further extend to the use of more complex LLMs. © 2023 ieee.

关键词： Privacy-preserving techniques

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parallel and Batch Multiple Replica Auditing Protocol for Edge Computing 21

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

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作者： Li, Yi Zheng, Wenying Pandi, Vijayakumar Bhuiyan, Md Zakirul Alam thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

the edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 ieee.

关键词： Edge computing

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proceedings of IPDRM 2022: 5th Annual Workshop on Emerging parallel and distributed Runtime Systems and Middleware, Held in conjunction with SC 2022: the International Conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of IPDRM 2022: 5th Annual Workshop on Emerging P...

引用

5th ieee/ACM Annual Workshop on Emerging parallel and distributed Runtime Systems and Middleware, IPDRM 2022

the proceedings contain 3 papers. the topics discussed include: SABO: dynamic MPI+OpenMP resource balancer;LEC-PR: proactive recovery method in erasure-coded storage;and Petri nets for concurrent programming.

ISBN: (纸本)9781665475082

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 ieee.

关键词： Knowledge representation

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