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Design and Implementation of Adaptive Scheduling Management for User-Mode Thread Pool

Design and Implementation of Adaptive Scheduling Management ...

引用

2022 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2022

作者： Cai, Bin Qiu, Bingcheng Yao, Jian Xiao, Xiaohong Liu, Lei Xie, Jiangang Zhang, Xu Wu, Ruilong Guangdong Guangzhou China Wuhan University Hubei Wuhan China Blue Dot Power Data Technology Co. Ltd. Beijing China China Hi-Tech Group Co. Ltd. Guangdong Guangzhou China

ISBN: (纸本)9781510663350

Progress in network and storage technology enables data center services to provide high-concurrency and low-latency processing responses. The user-mode thread pool is one of the technologies that can be used to improve the overall concurrent responsiveness of the service system while ensuring its stability of the system. In the system of multicore processor architecture, the scheduling management of the user-mode thread pool is much more complicated than that of the thread pool. First, the existing method cannot fully utilize the advantage of parallel execution of multicore processor architecture, and it is difficult to implement unified resource provisioning management among multiple user-mode thread pools. Second, the existing user-mode thread scheduling method cannot distinguish the performance requirements of different data processing tasks but treat all user-mode thread tasks in the same way. Last, the existing user-mode thread scheduling method controls the execution of all kernel threads, which is difficult to isolate host threads and other kernel threads in the system. It fails to reflect the performance requirements of different user-mode thread tasks, or to provide finer-grained execution timing control of the user-mode thread. This paper provides a two-level scheduling management method for user-mode thread pool, which can effectively integrate OS scheduling mechanism and user-mode thread scheduling mechanism, with a view to distinguishing the needs of different data processing tasks, enabling more targeted and fine-grained scheduling and execution control of user-mode threads, achieving isolation of host threads from non-host threads and differentiated scheduling control without affecting the scheduling mechanism of the OS itself, and enabling the global unified resource provisioning management of multi-user thread pools for multicore processor architecture. Generally, the new method can greatly improve the efficiency and performance of user thread pools

关键词： network architecture

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Memory-Centric Fusion-based CNN Accelerator with 3D-NoC and 3D-DRAM 19

Memory-Centric Fusion-based CNN Accelerator with 3D-NoC and ...

引用

19th International SoC Design conference (ISOCC) - SoC Technology Towards a New Era of Innovation

作者： Lu, Wei Ge, Pei-Yu Huang, Po-Tsang Chen, Hung-Ming Hwang, Wei Natl Yang Ming Chiao Tung Univ Inst Elect Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Int Coll Semicond Technol Hsinchu Taiwan

ISBN: (纸本)9781665459716

Deep neural networks require enormous computation capacity, great amounts of data accesses from memory and data movement among processing elements (PEs). In fact, data access energy costs on the data access exceed that of computation. Therefore, managing data reuse efficiently and reducing data movement have become the critical design issues. In this paper, an energy-efficient fusion-based DNN is proposed based on a 3D scalable network-on-chip (NoC) with vertical 3D stacked memory to reduce large memory footprints. The effective reuse of data through this approach reduces the data access and energy consumption over current 2D-DRAM designs. A high-efficiency data flow with pipeline, which can process more data in parallel, is also proposed for increasing the resource utilization. Additionally, dynamic voltage/frequency scaling (DVFS) for 3D memory interface and a prefetch technique are utilized into distributed vault controllers to improve system efficiency. Overall, the proposed fusion-based 3D NoC with 3D-DRAM decreases the energy-delay product (EDP) up to 148x compared to conventional NoC with 2D baseline DDR3.

关键词： Fused-Layer 3D network-on-Chip 3D Memory Deep Neural network

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A New parallel Methodology for the network Analysis of COVID-19 Data

A New Parallel Methodology for the Network Analysis of COVID...

引用

26th International conference on parallel and distributed Computing (Euro-Par)

作者： Agapito, Giuseppe Milano, Marianna Cannataro, Mario Magna Graecia Univ Catanzaro Data Analyt Res Ctr I-88100 Catanzaro Italy Magna Graecia Univ Catanzaro Dept Med & Surg Sci I-88100 Catanzaro Italy Magna Graecia Univ Catanzaro Dept Legal Econ & Social Sci I-88100 Catanzaro Italy

ISBN: (纸本)9783030715939;9783030715922

Coronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across the world. In this article, we present a new methodology for network-based analysis of Italian COVID-19 data. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data;(ii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships;(iii) the discovering communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19. Experiments was performed on real datasets about Italian regions, and they although the limited size of the Italian COVID-19 dataset, a quite linear speed-up was obtained up to six cores.

关键词： COVID-19 network analysis parallel computing

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An MPI-based Algorithm for Mapping Complex networks onto Hierarchical Architectures 27th

An MPI-based Algorithm for Mapping Complex Networks onto Hie...

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

作者： Predari, Maria Tzovas, Charilaos Schulz, Christian Meyerhenke, Henning Humboldt Univ Berlin Germany Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9783030856656;9783030856649

processing massive application graphs on distributed memory systems requires to map the graphs onto the system's processing elements (PEs). This task becomes all the more important when PEs have non-uniform communication costs or the input is highly irregular. Typically, mapping is addressed using partitioning, in a two-step approach or an integrated one. parallel partitioning tools do exist;yet, corresponding mapping algorithms or their public implementations all have major sequential parts or other severe scaling limitations. In this paper, we propose a parallel algorithm that maps graphs onto the PEs of a hierarchical system. Our solution integrates partitioning and mapping;it models the system hierarchy in a concise way as an implicit labeled tree. The vertices of the application graph are labeled as well, and these vertex labels induce the mapping. The mapping optimization follows the basic idea of parallel label propagation, but we tailor the gain computations of label changes to quickly account for the induced communication costs. Our MPI-based code is the first public implementation of a parallel graph mapping algorithm;to this end, we extend the partitioning library PARHIP. To evaluate our algorithm's implementation, we perform comparative experiments with complex networks in the million- and billion-scale range. In general our mapping tool shows good scalability on up to a few thousand PEs. Compared to other MPI-based competitors, our algorithm achieves the best speed to quality trade-off and our quality results are even better than non-parallel mapping tools.

关键词： Load balancing Process mapping Hierarchical architectures parallel label propagation

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PipeEdge: Pipeline parallelism for Large-Scale Model Inference on Heterogeneous Edge Devices

PipeEdge: Pipeline Parallelism for Large-Scale Model Inferen...

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euromicro Symposium on Digital System Design

作者： Yang Hu Connor Imes Xuanang Zhao Souvik Kundu Peter A. Beerel Stephen P. Crago John Paul Walters Ming Hsieh Department of Electrical Computer Engineering University of Southern California USA Information Sciences Institute University of Southern California USA Unaffiliated China

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision and natural language processing. However, such models are too compute- or memory-intensive for resource-constrained edge devices. Prior works on parallel and distributed execution primarily focus on training-rather than inference-using homogeneous accelerators in data centers. We propose PipeEdge, a distributed framework for edge systems that uses pipeline parallelism to both speed up inference and enable running larger, more accurate models that otherwise cannot fit on single edge devices. PipeEdge uses an optimal partition strategy that considers heterogeneity in compute, memory, and network bandwidth. Our empirical evaluation demonstrates that PipeEdge achieves 11.88× and 12.78× speedup using 16 edge devices for the ViT-Huge and BERT-Large models, respectively, with no accuracy loss. Similarly, PipeEdge improves throughput for ViT-Huge (which cannot fit in a single device) by 3.93× over a 4-device baseline using 16 edge devices. Finally, we show up to 4.16× throughput improvement over the state-of-the-art PipeDream when using a heterogeneous set of devices.

关键词： Deep learning Digital systems Computational modeling Pipelines Neural networks parallel processing Throughput

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE International conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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Colony: parallel Functions as a Service on the Cloud-Edge Continuum 27th

Colony: Parallel Functions as a Service on the Cloud-Edge Co...

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

作者： Lordan, Francesc Lezzi, Daniele Badia, Rosa M. Barcelona Supercomp Ctr BSC Dept Comp Sci Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual -Agent-, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner. Unlike other FaaS solutions, the described approach proposes to transparently convert the logic of such functions into task-based workflows backing on task-based programming models;thus, agents hosting the execution of the method generate the corresponding workflow and offloading part of the workload onto other agents to improve the overall service performance. On our prototype, the function-to-workflow transformation is performed by COMPSs;thus, developers can efficiently code applications of any of the three envisaged computing scenarios - sense-process-actuate, streaming and batch processing - throughout the whole Cloud-Edge Continuum without struggling with different frameworks specifically designed for each of them.

关键词： Edge Fog Cloud Compute Continuum distributed systems Programming model Runtime system Serverless Function-as-a-Service Stream-processing Task-based workflow

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Ancient Chinese Character Image Retrieval based on Fusing Deep Features and Skeleton Features

Ancient Chinese Character Image Retrieval Based on Fusing De...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Xinhui Wang Xuedong Tian School of Cyber Security and Computer Hebei University Baoding China Institute of Intelligent Image and Document Information Processing Hebei University Baoding China

The complex glyph structures and diverse writing styles of ancient Chinese character images lead to suboptimal performance when existing image retrieval methods are directly applied to datasets of these images. Addressing the impact on retrieval caused by the complex glyph structure and rich detail information characteristic of ancient Chinese character images, a multi-layer feature adaptive fusion model for ancient Chinese character image retrieval is designed. Firstly, a local feature extraction module is constructed to obtain low-level feature maps with different spatial receptive fields. Secondly, to better fuse features of varying scales, an improved ASFF(Adaptively Spatial Feature Fusion) method is employed to build the PSASFF(Pixel Shuffle Adaptively Spatial Feature Fusion) module for adaptive fusion of multi-layer features extracted by the designed network. Finally, in order to reduce the influence of the writing style of ancient Chinese characters on retrieval, the cosine similarity scores of image retrieval before and after fine processing are weighted and used as the ultimate similarity scores. The retrieval method proposed achieves an average retrieval accuracy of mAP@50 and mAP@30 of 0.8537 and 0.9576 respectively on the ancient Chinese character image dataset. Experimental results demonstrate the effectiveness of this method for ancient Chinese character image retrieval.

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FDEN: MINING EFFECTIVE INFORMATION OF FEATURES IN DETECTING network ANOMALIES

FDEN: MINING EFFECTIVE INFORMATION OF FEATURES IN DETECTING ...

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Li, Bin Wang, Yijie Liu, Mingyu Xu, Kele Wang, Zhongyang Cheng, Li Li, Yizhou Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9781728176055

network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method to effectively leverage the features in detecting network anomalies, named FDEn, consisting of flow-based Feature Derivation (FD) and prior knowledge incorporated Ensemble models (En(pk)). To mine the effective information in features, 149 features are derived to enrich the feature set of the original data with covering more characteristics of network traffic. To leverage these features effectively, an ensemble model En(pk), including CatBoost and XGBoost, based on the bagging strategy is proposed to first detect anomalies by combining numerical features and categorical features. And then, En(pk) adjusts the predicted label of specific data by incorporating the prior knowledge of network security. We conduct empirically experiments on the data set provided by the network Anomaly Detection Challenge (NADC), in which we obtain average improvement up to 61.6%, 31.7%, 50.2%, and 45.0%, in terms of the cost score, precision, recall and F1-score, respectively.

关键词： network anomaly detection Feature derivation CatBoost XGBoost Prior knowledge

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Cloud computing resource node allocation algorithm based on load balancing strategy 6

Cloud computing resource node allocation algorithm based on ...

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6th IEEE Information Technology and Mechatronics Engineering conference, ITOEC 2022

作者： Bo, Ye State Grid Xinjiang Electric Power Research Institute Urumqi China

ISBN: (纸本)9781665431859

At present, cloud computing has developed into a high-performance computing service model integrating the technical advantages of distributed computing, utility computing, load balancing, parallel computing, network storage, hot backup redundancy and virtualization. However, due to the lack of effective algorithm at the beginning of DAG task scheduling to ensure the load balance of resource node allocation during task scheduling, this paper proposes a cloud computing resource node allocation algorithm (NA-LB) based on load balancing strategy. The algorithm records the changes of virtual machine operating parameters in the cloud computing resource pool as vector values of spatial vectors, Therefore, the mathematical calculation method of load balance is given and used as the load balance index to measure the allocation of resource nodes. At the same time, the task replication strategy is used to further optimize the task resource node allocation process. The experimental results show that NA-LB algorithm can effectively shorten the DAG data processing time and show good load balance. © 2022 IEEE.

关键词： Cloud computing

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