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Advanced Analytics as a Service in Smart Factories 20

Advanced Analytics as a Service in Smart Factories

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20th IEEE Jubilee World symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Soderi, Mirco Kamath, Vignesh Morgan, Jeff Breslin, John G. National University of Ireland Data Science Institute Galway Ireland

ISBN: (纸本)9781665497046

A key aspect of Intelligent Manufacturing is the interface between the Edge and Fog layers on one side and the Cloud on the other side. Once that some data extraction and cleaning has been performed in the (logical and/or physical) nearby of the production line, it is necessary to move to the Cloud where distributed data and parallel computations make it possible to train and use Machine Learning models. For a wide range of applications, it is also necessary to restructure and reconfigure the computation network over the time, reacting to relevant events at real-time. This can be achieved (i) by making the network and all of its components fully configurable through API calls, (ii) by using containerization technologies, and (iii) by relying on graphical user interfaces for development, data visualization, monitoring, and interaction. In this work, an architecture for a computation network is presented that (i) spreads across the Edge, Fog and Cloud layers, (ii) is fully configurable through API calls, and (iii) is based on Docker, Node-RED, MQTT, Scala, Spark, and Cloud storage technologies such as Hadoop. Two proofs of concept are presented: a clustering-based alerter, and a demo environment plus a Postman collection including over 600 API calls to demonstrate how the proposed architecture enables Big Data stream transformations and analytics as a Service. © 2022 IEEE.

关键词： Cloud computing distributed databases Computer architecture Standardization Big Data Real-time systems Sparks

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High Performance Visualization for Astrophysics and Cosmology

High Performance Visualization for Astrophysics and Cosmolog...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Nicola Tuccari Eva Sciacca Fabio Vitello Iacopo Colonnelli Yolanda Becerra Enric Sosa Cintero Guillermo Marin Milan Jaros Lubomir Riha Petr Strakos Sebastian Trujillo-Gomez Emiliano Tramontana Robert Wissing Istituto Nazionale di Astrofisica Catania Italy Università di Torino Torino Italy Barcelona Supercomputing Center Barcelona Spain IT4Innovations National Supercomputing Center Ostrava Czech Republic Heidelberg Institute for Theoretical Studies Heidelberg Germany Università degli Studi di Catania Italy University of Oslo Institute of Theoretical Astrophysics Oslo Norway

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Modern Astrophysics and Cosmology (A&C) projects produce immense data volumes, necessitating advanced software tools for data access, storage, and analysis. Visualization Interface for the Virtual Observatory (VisIVO) is one such tool enabling multi-dimensional data analysis and knowledge discovery across complex astrophysical datasets. Leveraging containerization and virtualization, VisIVO has been deployed on various distributed computing platforms. Additionally, Blender, an open-source 3D suite, provides robust tools for rendering and processing volumetric data, making it suitable for visualizing complex datasets. At the SPACE Center of Excellence these tools are being adapted for high-performance visualization of cosmological simulations performed with GADGET and ChaNGa on pre-exascale systems. However, implementing high-performance visualization on diverse HPC platforms presents several challenges, including hardware and software compatibility, data management, scalability, performance portability, and efficient resource allocation. This paper outlines strategies to integrate VisIVO with workflow frameworks and streaming platforms to address these challenges. Workflow frameworks enhance portability, scheduling, and reproducibility of visualization workflows on pre-exascale systems used in A&C simulations. We also discuss the use of streaming platforms to enable concurrent (i.e. in-situ) analysis and visualization of simulations, reducing the need to store full simulation data by leveraging distributed databases that stream the output data in real time. Lastly, we present an adaptation of Blender to handle large-scale particle-based astrophysical data, offering high-quality visualization with interactive exploration capabilities.

关键词： Adaptation models Analytical models Astrophysics Computational modeling Scalability Data visualization distributed databases Data models Reproducibility of results Real-time systems

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Falcon: A Timestamp-based Protocol to Maximize the Cache Efficiency in the distributed Shared Memory

Falcon: A Timestamp-based Protocol to Maximize the Cache Eff...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jin Zhang Xiangyao Yu Zhengwei Qi Haibing Guan Shanghai Jiao Tong University Shanghai China University of Wisconsin-Madison Madison WI USA

distributed shared memory (DSM) systems can handle data-intensive applications and recently receiving more attention. A majority of existing DSM implementations are based on write-invalidation (WI) protocols, which achieve sub-optimal performance when the cache size is small. Specifically, the vast majority of invalidation messages become useless when evictions are frequent. The problem is troublesome regarding scarce memory resources in data centers. To this end, we propose a self-invalidation protocol Falcon to eliminate invalidation messages. It relies on per-operation timestamps to achieve the global memory order required by sequential consistency (SC). Furthermore, we conduct a comprehensive discussion on the two protocols with an emphasis on the cache size impact. We also implement both protocols atop a recent DSM system, Grappa. The evaluation shows that the optimal protocol can improve the performance of a KV database by 27% and a graph processing application by 71.4% against the vanilla cache-free scheme.

关键词： distributed processing Data centers Protocols Memory management distributed databases

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Consistent Lock-free parallel Stochastic Gradient Descent for Fast and Stable Convergence 35

Consistent Lock-free Parallel Stochastic Gradient Descent fo...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Backstrom, Karl Walulya, Ivan Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden

ISBN: (纸本)9781665440660

Stochastic Gradient Descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous shared-memory parallel SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD!, have received interest in certain contexts, due to reduced overhead compared to synchronous parallelization. Despite that they induce staleness and inconsistency, they have shown speedup for problems satisfying smooth, strongly convex targets, and gradient sparsity. Recent works take important steps towards understanding the potential of parallel 50D for problems not conforming to these strong assumptions, in particular for deep learning (DL). There is however a gap in current literature in understanding when AsyncSGD algorithms are useful in practice, and in particular how mechanisms for synchronization and consistency play a role. We contribute with answering questions in this gap by studying a spectrum of parallel algorithmic implementations of AsyncSGD, aiming to understand how shared-data synchronization influences the convergence properties in fundamental DL applications. We focus on the impact of consistency-preserving non-blocking synchronization in SGD convergence, and in sensitivity to hyperparameter tuning. We propose Leashed-SGD, an extensible algorithmic framework of consistency-preserving implementations of AsyncSGD, employing lock-free synchronization, effectively balancing throughput and latency. Leashed-SGD features a natural contention-regulating mechanism, as well as dynamic memory management, allocating space only when needed. We argue analytically about the dynamics of the algorithms, memory consumption, the threads' progress over time, and the expected contention. We provide a comprehensive empirical evaluation, validating the analytical claims, benchmarking the proposed Leashed-SGD framework, and comparing to baselines for two prominent deep learning (DL) applications: multilayer perceptrons (MLP) and convolutional neural networks (CNN). We o

关键词： artificial neural networks parallel algorithms lock-free synchronization stochastic gradient descent

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Optimizing Optical Character Recognition Accuracy in Livestock Weighting systems Through distributed Intelligent Computer Vision

Optimizing Optical Character Recognition Accuracy in Livesto...

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IEEE international symposium on Consumer Electronics (ISCE)

作者： M. Rosyad Ahsanul Wafa Muhaimin Toh-arlim Sritrusta Sukaridhoto Rizqi Putri Nourma Budiarti Agus Prayudi Dept. of Multimedia Creative Technology Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Faculty of Economics Business and Digital Technology Universitas Nahdlatul Ulama Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

This research develops and optimizes an optical character recognition (OCR) system for goat weighing scales using intelligent computer vision and distributed computing to enhance accuracy and efficiency in real-world operational conditions. Images of the scales are captured and processed to remove noise, then recognized using deep learning algorithms. The implementation of distributed computing frameworks like Apache Hadoop and TensorFlow enables efficient parallel processing. Results show a 20% increase in OCR accuracy and a 30% improvement in processing efficiency compared to conventional systems. This integration provides a scalable and reliable solution for applications in the livestock industry, with potential adaptation for various operational conditions and other livestock types.

关键词： Deep learning Weight measurement Computer vision Technological innovation Accuracy Scalability Optical character recognition Livestock Reliability Sustainable development

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HPPython: Extending Python with HPspmd for Data parallel Programming 23

HPPython: Extending Python with HPspmd for Data Parallel Pro...

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Proceedings of the 2023 2nd international symposium on Computing and Artificial Intelligence

作者： Judah Josiah Nava Jaden Jinu Lee Hanku Lee Computer Science & Information Systems Department Minnesota State University Moorhead USA

ISBN: (纸本)9798400708954

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite Python's popularity as a programming language in scientific and engineering applications and its native support for executing various processes, HPPython brings substantial simplification to the development of parallel programs and empowers program distribution across heterogeneous clusters consisting of multiple physical computers. HPPython leverages the MPI standard for its underlying communication, thereby harnessing the benefits of the SPMD model. Additionally, HPPython introduces novel syntax and constructs, such as parallel loops and distributed lists, while endeavoring to retain the natural essence of the original language. This paper delves into the distinct components of HPPython and elucidates their integration, establishing HPPython as a viable solution for parallel programming in today's data-driven world.

关键词： Data parallel Language distributed Computing High-Performance Computing parallel Computing

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Non-deterministic event brokered computing 2022

Non-deterministic event brokered computing

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12th international symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART)

作者： Brown, Andrew Thomas, David Vousden, Mark Bragg, Graeme Luk, Wayne Beaumont, Jonny Todman, Tim Yakovlev, Alex Rafiev, Ashur Univ Southampton Southampton Hants England Imperial Coll London London England Univ Newcastle Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781450396608

This paper reviews the massively micro-parallel compute system POETS (Partially Ordered Event Triggered System) and illustrates its potential for speeding up demanding applications. Application domains that benefit from POETS include simulations of physical systems that can be discretised as a mesh. The problem graph is distributed over a large compute mesh;each mesh vertex contains a processor - an FPGA-based RISC-V thread supporting custom instructions in our prototype - and a small amount of local problem state data. There is no central overseer of any sort and processors cannot see memory besides their own. A problem graph vertex interacts with a neighbour to send a state change by sending an asynchronous packet. The packets are fixed size and small - currently 64 bytes - and the hardware communications infrastructure is very fast. Applications can use an asynchronous 'packet storm' approach;run synchronously using a hardware idle barrier or run in a globally asynchronous, locally synchronous manner. Results show significant wallclock speedup and power consumption improvement over conventional systems: for one application we show a 40-fold speedup over a conventional CPU-based system;versus a multi-GPU system, the POETS cluster is 26% faster, 60% more power efficient, and 34% more energy efficient.

关键词： Event-driven Network-on-chip Massively parallel

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FedGAC: Graph Federated Learning with Gradients Aggregation Calibration for Non-IID and Long-Tailed Data

FedGAC: Graph Federated Learning with Gradients Aggregation ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jie Li Jinyan Wang Rongbin Deng Dongqi Yan Qiyu Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Federated graph learning (FGL) combines the advantages of graph neural networks and federated learning, extracting valuable information from decentralized graph data while preserving data privacy. Existing FGL methods typically consider mitigating the effect of non-IID under the class balanced assumption. However, in many real-world scenarios, the data class distribution often exhibits a long-tailed distribution, which can lead to the model being severely biased towards the head classes, thus affecting the overall performance of the global model. To address the challenges posed by non-IID and long-tailed distribution data in FGL, this paper proposes a method called FedGAC to train a globally balanced model. In the local stage, FedGAC employs feature-structure decoupled graph attention network to train client models, introduces class-importance balanced strategy to mitigate class imbalance issues, and leverages knowledge relearning strategy to enhance model generalization. In the global stage, FedGAC employs an adaptive gradients aggregation strategy that dynamically adjusts balancing factors based on the degree of class imbalance in each client’s data, thereby optimizing the aggregation process. Additionally, it utilizes global class gradients to calibrate learnable features. Experimental results demonstrate that, compared to the state-of-the-art federated learning methods, FedGAC achieves superior performance on highly non-IID and long-tailed distribution datasets.

关键词： Knowledge engineering Adaptation models distributed processing Heavily-tailed distribution Federated learning distributed databases Data models Graph neural networks Servers Optimization

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Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference

Exploiting Inter-Layer Expert Affinity for Accelerating Mixt...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jinghan Yao Quentin Anthony Aamir Shafi Hari Subramoni Dhabaleswar K. DK Panda Department of Computer Science and Engineering The Ohio State University Columbus OH U.S.

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

In the realm of large language models (LLMs) like the Generative Pre-trained Transformer (GPT), the Mixture of Experts (MoE) paradigm has emerged as a powerful technique for enhancing model expressiveness and accuracy. However, the deployment of GPT MoE models for parallel inference on distributed systems presents significant challenges, primarily due to the extensive Alltoall communication required for expert routing and aggregation. This communication bottleneck exacerbates the already complex computational landscape, hindering the efficient utilization of high-performance computing resources. In this paper, we propose a lightweight optimization technique called ExFlow, to largely accelerate the inference of these MoE models. We take a new perspective on alleviating the communication overhead by exploiting the inter-layer expert affinity. Unlike previous methods, our solution can be directly applied to pre-trained MoE models without any fine-tuning or accuracy degradation. By proposing a context-coherent expert parallelism on distributed systems, our ExFlow design only uses one Alltoall communication to deliver the same functionality while previous methods all require two Alltoalls. By carefully examining the conditional probability in tokens’ routing across multiple layers, we proved that pre-trained GPT MoE models implicitly exhibit a strong inter-layer expert affinity. We then design an efficient integer programming model to precisely capture such features and show that by properly placing the experts on corresponding GPUs, we can reduce up to 67% of tokens’ cross-GPU routing latency on various hardware configurations and topologies. Our solution beats the cutting-edge Deepspeed-MoE in GPT MoE models with experts from 8 to 64, with up to 2.2x improvement in inference throughput. To the best of our knowledge, this is the first work in leveraging inter-layer expert affinity to accelerate the inference of GPT MoE models. We further provide a detailed study of how

关键词： Training Accuracy Computational modeling Large language models parallel processing Routing Transformers

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Secure and Efficient Cloud Ciphertext Deduplication Based on SGX

Secure and Efficient Cloud Ciphertext Deduplication Based on...

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international Conference on parallel and distributed systems (ICPADS)

作者： Limin Li Guiyun Qin Pengtao Liu Chengyu Hu Shanqing Guo School of Cyber Science and Technology Shandong University Qingdao Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Qingdao Quancheng Laboratory Jinan School of Cyberspace Security Shandong University of Political Science and Law Jinan

With the development of data outsourcing technology, the data stored by cloud storage servers are exploding. Secure deduplication for encrypted data helps cloud servers reduce storage overhead in the scenario that cloud users outsource their data in ciphertext. To satisfy client-side semantic security, most existing deduplication schemes for encrypted data need trusted third parties. However, trusted third parties are difficult to deploy and may cause potential risks. Therefore, we propose a secure cloud ciphertext deduplication scheme based on Intel SGX. The proposed scheme uses the Enclave security container provided by Intel SGX as the trusted execution environment on the cloud server to replace the trusted third party to perform sensitive operations. At the same time, our scheme simplifies the secure management of the file encryption keys so that the encryption key of the files with the same data can be securely distributed to other owners of the same file without the need for the original uploader online. We prove the security of the proposed scheme and the experiment shows the efficiency of the scheme.

关键词： Cloud computing Semantics distributed databases Containers Encryption Outsourcing Servers

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