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Decentralized parallel Blockchain Agricultural Product Traceability System Security Analysis 5

Decentralized Parallel Blockchain Agricultural Product Trace...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Fenglin, Sun Guangxi Normal University Qixing District Guangxi Guilin City541000 China

ISBN: (纸本)9798350395235

this research work presents a decentralized parallel blockchain-based agricultural product traceability system, which aims to enhance information security and data model efficiency, and achieve real-time tracking of agricultural products. through an in-depth analysis of decentralized blockchain applications, the system adopts distributed ledger processing for low-cost transactions. the Digital Promise technology ensures the credibility of the data transmission and privacy protection. the proposed decentralized parallel blockchain structure shows significant traceability results, ensuring transparent traceability of agricultural products. Experimental results demonstrate the effectiveness of the system, improving food safety and quality management. the decentralized nature reduces the risk of data tampering, thereby enhancing public trust. Experimental results show short query response times, efficient data transfers, and zero failures, highlighting the system's excellent performance in handling traceability queries. these results underscore the remarkable advantages of blockchain technology in increasing the efficiency and reliability of traceability systems. © 2024 ieee.

关键词： Blockchain

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Challenges of Data Consistency in High-Concurrency Environments: Algorithms and Implementation for the Electric Power Industrial Internet Platform 5

Challenges of Data Consistency in High-Concurrency Environme...

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5th International Conference on Information Science, parallel and distributed Systems, ISPDS 2024

作者： Zhou, Zhongran Wang, Lili Song, Chunhe Shen, Yaowei Li, Mafeng Liu, Sai Nari Information & Communication Technology Co.Ltd Nanjing China Chinese Academy of Sciences Shenyang Institute of Automation 110006 China Liaoning University School of Information Technology Shenyang China

ISBN: (纸本)9798350352719

With the rapid development of the Electric Power Industrial Internet, challenges in high-concurrency data processing have become increasingly prominent, particularly issues related to data consistency, which directly affect the stability and reliability of the system. this study addresses the distributed data consistency problems in the Electric Power Industrial Internet platform, proposing an optimized algorithmic framework designed specifically for high concurrency and dynamic environments to enhance the response speed and data processing capabilities of the power system. this paper first analyzes common data consistency challenges in high-concurrency environments within the power system, including data synchronization delays, complexity in fault recovery, and resource allocation issues. Based on these challenges, we introduce and optimize distributed data consistency algorithms, combined with load balancing and resource optimization techniques, significantly reducing communication costs and enhancing processing efficiency. Additionally, the paper designs a robust fault tolerance mechanism to ensure data integrity and consistency in the event of node failures. through simulation experiments, we have confirmed the effectiveness of the proposed algorithms in terms of processing speed, system response time, and fault recovery, significantly optimizing the performance of the Electric Power Industrial Internet platform. this research not only enhances the robustness of the power system in facing high-concurrency data challenges but also provides important technical support and a theoretical basis for future research and applications in the Electric Power Industrial Internet. © 2024 ieee.

关键词： Data Consistency distributed Algorithms Electric Power Industrial Internet High-Concurrency processing System Optimization

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Design and Implementation of Adaptive Resource Placement Component Based on Storm 5

Design and Implementation of Adaptive Resource Placement Com...

引用

5th International Conference on Information Science, parallel and distributed Systems, ISPDS 2024

作者： Li, Lina Lv, Junan Wang, Shuxin Hu, Chuncheng Xu, Yiming Li, Nianfeng Changchun University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798350352719

In Storm systems, an efficient resource placement strategy is key to ensure application performance. However, the Storm platform uses a polled resource placement strategy, which leads to large resource and communication overheads. To solve this problem, we design and implement a Storm-based Adaptive Resource Placement Assembly (ARPA) that can dynamically adjust the placement of resources based on the real-time state and load of the system to minimise resource overhead and communication overhead. We tested the performance of APRA on a real streaming dataset. Compared with other traditional strategies, ARPA has significant advantages in terms of communication cost and number of resources used. © 2024 ieee.

关键词： adaptive components distributed processing dynamic resource placement Storm Storm API

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An Efficient 2D Method for Training Super-Large Deep Learning Models 37

An Efficient 2D Method for Training Super-Large Deep Learnin...

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37th ieee International parallel and distributed processing symposium (IPDPS)

作者： Xu, Qifan You, Yang Univ Calif Los Angeles Los Angeles CA 90095 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350337662

Since the rise of Transformer [22] and BERT [6], large language models [7, 12] have been proposed and shown unprecedented performance in tasks like translation, classification, and text generation. However, due to the memory constraint, model parallelism must be used to split the model across multiple processors. Interlayer partition, intra-layer partition, and sparse activation are the major approaches to achieve model parallelism. Among them, interlayer partition [10, 11] often requires the model to be explicitly expressed as a stack of sub-modules, the number of which equals to the number of processors, and would introduce either gradient staleness or bubble overhead;while the sparse activation [12] is primarily designed for Google TPU cluster and hard to deploy on GPU servers, intra-layer partition [17], especially Megatron-LM [18], can be easily deployed on GPU servers and has been adopted in subsequent works like Turing-NLG and M6. though as pioneers of intra-layer parallelism, they still show memory redundancy and sub-optimal communication efficiency, which reveals the space for further improvements. In this work, we leverage SUMMA [21] and propose Optimus, a highly efficient and scalable paradigm for training super-large language models. In Optimus, activations and gradients are partitioned and distributed along processors all the way through forward and backward propagations, with hardly any memory redundancy. the isoefficiency of communication in pure model parallelism improves from W similar to p(3) for Megatron-LM, to W similar to (root p logp)(3) for our Optimus. this framework is implemented with open-source deep learning framework, PyTorch, and consolidates existing techniques such as mixed precision training [13], activation checkpointing [5], and data parallelism. In experiments on TACC Frontera supercomputers, Optimus shows 1.48x the speed for training, 1.78x speed for inference, and 8x the maximum batch size over Megatron-LM on 64 GPUs in pure mod

关键词： matrix-matrix multiplication distributed training neural networks natural language processing

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proceedings - 2023 ieee International parallel and distributed processing symposium, IPDPS 2023

Proceedings - 2023 IEEE International Parallel and Distribut...

引用

37th ieee International parallel and distributed processing symposium, IPDPS 2023

ISBN: (纸本)9798350337662

the proceedings contain 95 papers. the topics discussed include: distributed sparse random projection trees for constructing K-nearest neighbor graphs;fast deterministic gathering with detection on arbitrary graphs: the power of many robots;accurate and efficient distributed covid-19 spread prediction based on a large-scale time-varying people mobility graph;accelerating packet processing in container overlay networks via packet-level parallelism;efficient hardware primitives for immediate memory reclamation in optimistic data structures;efficient hardware primitives for immediate memory reclamation in optimistic data structures;accelerating distributed deep learning training with compression assisted Allgather and reduce-scatter communication;accelerating CNN inference on long vector architectures via co-design;exploiting input tensor dynamics in activation checkpointing for efficient training on GPU;drill: log-based anomaly detection for large-scale storage systems using source code analysis;dynasparse: accelerating GNN inference through dynamic sparsity exploitation;exploiting sparsity in pruned neural networks to optimize large model training;SRC: mitigate I/O throughput degradation in network congestion control of disaggregated storage systems;boosting multi-block repair in cloud storage systems with wide-stripe erasure coding;on doorway egress by autonomous robots;and on the arithmetic intensity of distributed-memory dense matrix multiplication involving a symmetric input matrix (SYMM).

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Markov Stochastic Processes Application Tools to Improve the Characteristics of distributed Systems Synchronization Procedures 5

Markov Stochastic Processes Application Tools to Improve the...

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Azarnova, Tatyana Vasilyevna Polukhin, Pavel Valerievich Kashirina, Irina Leonidovna Voronezh State University Applied Mathematics Informatics and Mechanics Voronezh Russia

ISBN: (纸本)9798350309126

Appling mathematical modeling methods for analyses synchronization processes of distributed data processing systems makes it possible to significantly optimize these processes and guarantee the required level of consistency. To analyze stochastic distributed synchronization procedures, mathematical modeling tools are needed to adequately reflect their stochastic nature and form a probabilistic time characteristics assessment of the shared resource ownership distributed between the parallel processes. the research carried out within the current paper shows that stochastic modeling tools are able to solve a number of complex problems in this area and in particular to adapt synchronization algorithms for use in heterogeneous computing systems with a different set of hardware components and providing an acceptable synchronization time for a large amount of data. the paper essentially analyzes Markov synchronization models with group maintenance and considers tools for bringing these models to the case of arbitrary service time. © 2023 ieee.

关键词： Random processes

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proceedings - 2023 ieee International Conference on parallel and distributed processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2023

Proceedings - 2023 IEEE International Conference on Parallel...

引用

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

ISBN: (纸本)9798350329223

the proceedings contain 153 papers. the topics discussed include: transaction data management optimization based on multi-partitioning in blockchain systems;semi-asynchronous federated learning optimized for NON-IID data communication based on tensor decomposition;HKTGNN: hierarchical knowledge transferable graph neural network-based supply chain risk assessment;DQR-TTS: semi-supervised text-to-speech synthesis with dynamic quantized representation;deep reinforcement learning-based network moving target defense in DPDK;iNUMAlloc: towards intelligent memory allocation for AI accelerators with NUMA;and predictive queue-based low latency congestion detection in data center networks.

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Dalorex: A Data-Local Program Execution and Architecture for Memory-bound Applications 29

Dalorex: A Data-Local Program Execution and Architecture for...

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29th ieee International symposium on High-Performance Computer Architecture (HPCA)

作者： Orenes-Vera, Marcelo Tureci, Esin Wentzlaff, David Martonosi, Margaret Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665476522

Applications with low data reuse and frequent irregular memory accesses, such as graph or sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor core utilization. While prior work with prefetching, decoupling, or pipelining can mitigate memory latency and improve core utilization, memory bottlenecks persist due to limited off-chip bandwidth. Approaches doing processing in-memory (PIM) with Hybrid Memory Cube (HMC) overcome bandwidth limitations but fail to achieve high core utilization due to poor task scheduling and synchronization overheads. Moreover, the high memory-per-core ratio available with HMC limits strong scaling. We introduce Dalorex, a hardware-software co-design that achieves high parallelism and energy efficiency, demonstrating strong scaling with >16,000 cores when processing graph and sparse linear algebra workloads. Over the prior work in PIM, both using 256 cores, Dalorex improves performance and energy consumption by two orders of magnitude through (1) a tile-based distributed-memory architecture where each processing tile holds an equal amount of data, and all memory operations are local;(2) a task-based parallel programming model where tasks are executed by the processing unit that is co-located with the target data;(3) a network design optimized for irregular traffic, where all communication is one-way, and messages do not contain routing metadata;(4) novel traffic-aware task scheduling hardware that maintains high core utilization;and (5) a data-placement strategy that improves work balance. this work proposes architectural and software innovations to provide the greatest scalability to date for running graph algorithms while still being programmable for other domains.

关键词： distributed scalable parallel data-local near-memory architecture sparse graph NoC network bandwidth

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Efficient Differentially Private Tensor Factorization in the parallel and distributed Computing Paradigm 21

Efficient Differentially Private Tensor Factorization in the...

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作者： Zhang, Feng Wang, Hao Guo, Ruixin Xue, Erkang Qu, Guangzhi China University of Geosciences School of Computer Science Wuhan China Oakland University Department of Computer Science and Engineering Rochester United States

ISBN: (纸本)9798350329223

Tensor factorization plays a fundamental role in multiple areas of AI research. Nevertheless, it encounters significant challenges related to privacy breaches and operational efficiency. In this study, we propose a novel approach that addresses both of these issues simultaneously by integrating differential privacy with parallel and distributed computing. To accommodate diverse scenarios, we introduce two models: DPTF-SVRG and ADMM-DPTF, each leveraging specific techniques. DPTF-SVRG is designed for single-GPU environments and utilizes a unique strategy to reduce gradient variance, enabling faster convergence compared to SGD. Moreover, it achieves parallelism on the GPU through a lock-free asynchronous approach. On the other hand, ADMM-DPTF utilizes distributed ADMM to parallelize DPTF-SVRG, enabling multi-GPU parallelism. Experimental results demonstrate that our algorithms outperform existing benchmarks while maintaining differential privacy. © 2023 ieee.

关键词： Tensors

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distributed Checkpointing in Dataflow with Static Scheduling 35

Distributed Checkpointing in Dataflow with Static Scheduling

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35th ieee International symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Alves, Tiago A. O. Univ Estado Rio De Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9798350381603

the Dataflow model, where instructions or tasks are fired as soon as their input data is ready, was proven to be a good fit for parallel/distributed computation. Previous works have presented DFER (Dataflow Error Recovery Model), that allows transient error and recovery in dataflow by adding special tasks and edges to the dataflow graph itself. However, permanent faults or faults that cause a processing element (PE) to become irresponsive are not addressed by DFER. For those cases it is necessary to adopt a checkpointing method. Since the whole purpose of Dataflow is to achieve high levels of parallelism and explore the potential asynchronicity between PEs, it is clear that the checkpointing method adopted must be uncoordinated and distributed. Current algorithms for distributed checkpointing rely solely on guaranteeing that causality between checkpoints can be trackable. In the context of Dataflow with static scheduling, i.e. when the dataflow graph is partitioned among the available PEs at compile-time, causality trackability is not sufficient as we will show. Since static scheduling of dataflow graphs is very important in various scenarios, it calls for a new algorithm for distributed checkpointing that can be adopted for the execution of statically scheduled dataflow graphs. In this paper we describe why the ability to track causality is not enough for statically scheduled dataflow and introduce a new algorithm for distributed checkpointing specifically tailored for such model of execution.

关键词： dataflow distributed checkpointing fault tolerance parallel programming

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