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Evaluating Data-parallel distributed Training Strategies 14

Evaluating Data-Parallel Distributed Training Strategies

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14th international conference on COMmunication Systems and networks, COMSNETS 2022

作者： Ponnuswami, Ganesan Kailasam, Sriram Dinesh, Dileep Aroor Indian Institute of Technology School of Computing and Electrical Engineering Himachal Pradesh Mandi India

ISBN: (纸本)9781665421041

Deep neural networks (DNNs) require distributed training strategies to deal with large data sizes. TensorFlow is one of the most widely used frameworks that support distributed training. Among the TensorFlow training strategies, the mirrored strategy (MS) and multi-worker mirrored strategy (MWMS) follow synchronous distributed model training;the parameter server strategy (PSS) implements asynchronous methods for training. In this work, we analyze the performance of distributed training strategies in synchronous and asynchronous settings by implementing LSTM and MLP on two industrial time-series datasets from network management systems (NMS) and Google cluster trace (GCT), and Inception-v3 model on CIFAR-10 image dataset. The strategies were compared by empirically evaluating loss, accuracy, training time, and scalability. © 2022 IEEE.

关键词： Long short-term memory

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Joint Partitioning and Sampling Algorithm for Scaling Graph Neural Network 29

Joint Partitioning and Sampling Algorithm for Scaling Graph ...

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29th Annual IEEE international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Das, Manohar Lal Jatala, Vishwesh Gupta, Gagan Raj Indian Inst Technol Bhilai Dept EECS Raipur Madhya Pradesh India

ISBN: (纸本)9781665494236

Graph Neural Network (GNN) has emerged as a popular toolbox for solving complex problems on graph data structures. Graph neural networks use machine learning techniques to learn the vector representations of nodes and/or edges. Learning these representations demands a huge amount of memory and computing power. The traditional shared-memory multiprocessors are insufficient to meet real-world data's computing requirements;hence, research has gained momentum toward distributed GNN. Scaling the distributed GNN has the following challenges: (1) the input graph needs to be efficiently partitioned, (2) the cost of communication between compute nodes should be reduced, and (3) the sampling strategy should be efficiently chosen to minimize the loss in accuracy. To address these challenges, we propose a joint partitioning and sampling algorithm, which partitions the input graph with weighted METIS and uses a bias sampling strategy to minimize total communication costs. We implemented our approach using the DistDGL framework and evaluated it using several real-world datasets. We observe that our approach (1) shows an average reduction in communication overhead by 53%, (2) requires less partitioning time to partition a graph, (3) shows improved accuracy, (4) shows a speed up of 1.5x on OGB-Arxiv dataset, when compared to the state-of-the-art DistDGL implementation.

关键词： Graph Neural Network Graph Partitioning distributed Processing Deep Learning

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Dynamic Priority Coflow Scheduling in Optical Circuit Switched networks 24th

Dynamic Priority Coflow Scheduling in Optical Circuit Switch...

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24th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2023

作者： Ren, Hongkun Shen, Hong Wang, Xin School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9789819982103

OCS (Optical Circuit Switch) is increasingly popular for accelerating data transmission of coflows due to its higher bandwidth and lower power consumption compared with EPS (Electronic Packet Switch), where a coflow is a collection of related parallel flows between two computation stages in data-intensive applications. However, the extra port constraints and reconfiguration delay of OCS obstruct the efficiency of OCS operations. This paper studies the problem of coflow scheduling in the OCS of datacenter networks to minimize the total Coflow Completion Time (CCT). We propose a Dynamic Priority Coflow Scheduling Algorithm that schedules coflows preemptively by considering coflow transmission time and OCS reconfiguration delay jointly to dynamically update each coflow's priority, which can significantly reduce the waiting time of small coflows and reduce head-of-line blocking. Extensive simulations based on Facebook data traces show that our approach outperforms the state-of-the-art scheme OMCO [19] significantly, and transmits multiple coflows 1.30× faster than OMCO. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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A parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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Computational Storage for an Energy-Efficient Deep Neural Network Training System 29th

Computational Storage for an Energy-Efficient Deep Neural Ne...

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29th international conference on parallel and distributed computing (Euro-Par)

作者： Li, Shiju Tang, Kevin Lim, Jin Lee, Chul-Ho Kim, Jongryool SK Hynix Memory Forest x&D Solut Lab San Jose CA 95134 USA Texas State Univ Dept Comp Sci San Marcos TX USA

ISBN: (纸本)9783031396977;9783031396984

Near-storage data processing and computational storage have recently received considerable attention from the industry as energy- and cost-efficient ways to improve system performance. This paper introduces a computational-storage solution to enhance the performance and energy efficiency of an AI training system, especially for training a deep learning model with large datasets or high-dimensional data. Our system leverages dimensionality reduction effectively by offloading its operations to computational storage in a systematic manner. Our experiment results show that it can reduce the training time of a deep learning model by over 40.3%, while lowering energy consumption by 38.2%.

关键词： Deep Neural networks Computational Storage Near-Storage Data Preprocessing Model Training Energy Efficiency

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Long-Term and Periodicity-Aware Spatio-Temporal Model for Traffic Flow Prediction 25th

Long-Term and Periodicity-Aware Spatio-Temporal Model for Tr...

引用

25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Hua, Qiang Lv, DongLiang Dong, Chun-Ru Zhang, Feng College of Mathematics and Information Science Hebei University Baoding071002 China

ISBN: (纸本)9789819642069

With the continuous expansion of comprehensive road transport networks in major cities, accurate traffic flow prediction—an essential component of intelligent urban transport systems has become increasingly challenging. Spatio-Temporal Graph Neural networks (STGNNs), which combine time-series models like the Transformer with spatial relationship learning models such as Graph Neural networks (GNNs), have shown significant promise in addressing this challenge. However, they still face issues like insufficient exploitation of long-term dependencies, high structural and computational complexity, and underutilization of periodic patterns in traffic data. To address these limitations, we propose LL-MLP, a spatio-temporal model based on an encoder-decoder architecture. Our approach extends the look-back window to better capture long-term dependencies, introduces learnable parameter matrices to enhance spatial information extraction, and incorporates periodic information as a dynamic covariate to further leverage temporal patterns. Extensive experiments on four real-world traffic datasets demonstrate that LL-MLP achieves performance on par with or exceeding that of existing model architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture 29

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Yue Wang, Li Chen, Yanyan Liu, Qi Pan, Jingshan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor's storage structure;combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency;optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication;employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation. © 2023 IEEE.

关键词： Communication-avoid Heterogeneous System Jacobi preconditioner PCG algorithm SW26010-Pro

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Edge DDoS Attack Detection Method Based on Software Defined networks 21st

Edge DDoS Attack Detection Method Based on Software Defined ...

引用

21st international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Ren, Gangsheng Zhang, Yang Zhang, Shukui Long, Hao Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9783030953843;9783030953836

Edge computing extends the traditional cloud computing architecture by using the computing and storage resources on the edge of the network, making people's work and life more convenient. However, these devices at the edge of the network are widely distributed and the environment is relatively complex. Attackers use these vulnerable IoT devices to build botnets to initiate distributed denial of service attacks, posing a serious threat to the normal use of such networks. In response to this problem, we propose an anomaly detection framework based on software-defined networking (SDN). The edge controller in the SDN network is used to obtain the flow information and extract the features of the flow. The XGBoost algorithm optimized by genetic algorithm (GA-XGBoost) we proposed is used to classify and detect the flow. Experimental results show that compared with other algorithms of the same type, our proposed algorithm has a lower false alarm rate and higher accuracy.

关键词： Edge computing Software-defined network (SDN) distributed denial of service attack (DDoS) Anomaly detection Machine learning

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Fine Time Granularity Allocation Optimization of Multiple networks Industrial Chains in Task Processing Systems 24th

Fine Time Granularity Allocation Optimization of Multiple Ne...

引用

24th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2023

作者： Li, Pan Di, Kai Bai, Xinlei Jiang, Yuanshuang Chen, Fulin School of Cyber Science and Engineering Southeast University Jiangsu Nanjing211189 China School of Computer Science and Engineering Southeast University Jiangsu Nanjing211189 China

ISBN: (纸本)9789819982103

As the industrial division of labor becomes increasingly specialized, various collaborative relationships between industrial chains develop, forming complex multi-networks. In the task processing system of multiple network industrial chains, there are dynamic online tasks. The arrival and deadline of these tasks cannot be accurately predicted. Therefore, it is necessary to divide the scheduling into finer time granularity to improve the response speed, efficiency, and timeliness, while ensuring the task completion rate and minimizing the task cost. In this paper, we study the characteristics of online tasks in multiple networks industrial chains and design a corresponding online scheduling framework. We analyze the arrival of online tasks in real-world scenarios and propose a passive scheduling algorithm based on the characteristics of different scenarios. The algorithm is tested on several sets of simulated data. Compared with previous heuristic algorithms, our algorithm can achieve better results in terms of task completion time, energy cost, and task completion rate in scenarios with fine time granularity. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge

A Converting Autoencoder Toward Low-latency and Energy-effic...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Mahmud, Hasanul Kang, Peng Desai, Kevin Lama, Palden Prasad, Sushil K. Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

ISBN: (纸本)9798350364613;9798350364606

Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resourcecon-strained edge devices. To address this problem, we propose a novel approach based on "converting" autoencoder and lightweight DNNs. This improves upon recent work such as early-exiting framework and DNN partitioning. Early-exiting frameworks spend different amounts of computation power for different input data depending upon their complexity. However, they can be inefficient in real-world scenarios that deal with many hard image samples. On the other hand, DNN partitioning algorithms that utilize the computation power of both the cloud and edge devices can be affected by network delays and intermittent connections between the cloud and the edge. We present CBNet, a low-latency and energy-efficient DNN inference framework tailored for edge devices. It utilizes a "converting" autoencoder to efficiently transform hard images into easy ones, which are subsequently processed by a lightweight DNN for inference. To the best of our knowledge, such autoencoder has not been proposed earlier. Our experimental results using three popular image-classification datasets on a Raspberry Pi 4, a Google Cloud instance, and an instance with Nvidia Tesla K80 GPU show that CBNet achieves up to 4.8 x speedup in inference latency and 79% reduction in energy usage compared to competing techniques while maintaining similar or higher accuracy.

关键词： Energy-efficiency Deep Neural networks Edge computing Early-exit DNNs Converting Autoencoder

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