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AutoFlow: Hotspot-Aware, Dynamic Load Balancing for distributed Stream processing 21st

AutoFlow: Hotspot-Aware, Dynamic Load Balancing for Distribu...

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21st International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Lu, Pengqi Yue, Yue Yuan, Liang Zhang, Yunquan Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783030953911;9783030953904

Stream applications are widely deployed on the cloud. While modern distributed streaming systems like Flink and Spark Streaming can schedule and execute them efficiently, streaming dataflows are often dynamically changing, which may cause computation imbalance and back-pressure. We introduce AutoFlow, an automatic, hotspot-aware dynamic load balance system for streaming dataflows. It incorporates a centralized scheduler that monitors the load balance in the entire dataflow dynamically and implements state migrations correspondingly. The scheduler achieves these two tasks using a simple asynchronous distributed control message mechanism and a hotspot-diminishing algorithm. The timing mechanism supports implicit barriers and a highly efficient state-migration without global barriers or pauses to operators. It also supports a time-window based load-balance measurement and feeds them to the hotspot-diminishing algorithm without user interference. We implemented AutoFlow on top of Ray, an actor-based distributed execution framework. Our evaluation based on various streaming benchmark datasets shows that AutoFlow achieves good load-balance and incurs a low latency overhead in a highly data-skew workload.

关键词： Stream processing Big data Data skewness Control message Load balance

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Knowledge-aware Multi-view Cross Learning for Edge-based Collaborative Recommendation 29

Knowledge-aware Multi-view Cross Learning for Edge-based Col...

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

作者： Dai, Yang Meng, Shunmei Gu, Huanhuan Liu, Nan Tu, Longchuan NanJing University of Science and Technology School of Cyberspace Security Nanjing China NanJing University State Key Lab. for Novel Software Technology Nanjing China NanJing University of Science and Technology Department of Computer Science and Engineering Nanjing China Nanjing Sinovatio Technology Co. Ltd. Nanjing China

ISBN: (纸本)9798350330717

In order to effectively overcome the sparsity and cold start challenges of collaborative filtering in actual scenarios, and provide more accurate suggestions, knowledge graphs are commonly utilized in recommender systems. Graph neural networks are increasingly used in knowledge graph-based recommender systems. Nevertheless, conventional recommender systems overlook the relationship information between items and entities as well as the sparse supervision signal, potentially leading to a decline in the actual recommendation performance. In this investigation, we propose KMCL, a Knowledge-aware Multi-view Cross-learning framework for edge-based collaborative recommendation. In contrast to the typical manner of dealing with knowledge graphs, our approach investigates three different graph views to improve users' and items' embedding representations, respectively. Firstly, the merged user-item KG graph is utilized to integrate the multi-hop neighbor information of each user, with the Graph Attention network assigning various weights to different neighbors. Secondly, Graph Neural network collects the top-x neighbor nodes for learning in the collaborative view of user-item graph and item-entity graph. Finally, a cross module is developed to share high-level information between embeddings with the same features. In addition, we also introduce the idea of edge computing, which preprocesses and calculates the locally obtained user preferences first, and then inputs them into different views to improve the recommendation efficiency. Experimental results demonstrate that KMCL delivers better suggestions than state-of-the-art baseline models in delivering superior recommendations. Furthermore, our approach maintains higher accuracy in predicting outcomes in less familiar interaction scenarios. © 2023 IEEE.

关键词： Edge Computing Graph Attention network Knowledge Graph Multi-view Cross Learning Recommender System

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Lessons learned from comparing C-CUDA and Python-Numba for GPU-Computing 28

Lessons learned from comparing C-CUDA and Python-Numba for G...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Oden, Lena Fernuniv Inst Math & Informat Hagen Germany

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

Python as programming language is increasingly gaining importance, especially in data science, scientific, and parallel programming. It is faster and easier to learn than classical programming languages such as C. However, usability often comes at the cost of performance and applications written in Python are considered to be much slower than applications written in C or FORTRAN. Further, it does not allow the usage of CPUs-besides of pre-compiled libraries. However, the Numba package promises performance similar to C code for compute intensive parts of a Python application and it supports CUDA, which allows the use of GPUs inside a Python application. In this paper we compare the perforinance of iVumba-CUDA and C-CUDA for different kinds of applications. For compute intensive benchmarks, the performance of the Numba version only reaches between 50% and 85% performance of the CCUDA version, despite the reduction operation, where the Numba version outperforms CUDA. Analyzing the PTX code and CUDA performance counters revealed that index-calculation is one 'uniting factor in Numba. Another problem is the type interference for single precision computations, as sonic values are computed in double precision. By optimizing this within the Numba package, the performance of Numba improves. However, C-CUDA applications still outperform the Numba versions. Further analysis with the CloverLeav Mini App shows that Numba performance further decreases for applications with multiple different compute kernels. The non-CPU part slows down these applications, due to the slow Python interpreter. This leads to a worse GPU utilization.

关键词： Python Graphics processing units Libraries Kernel Indexes Benchmark testing

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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...

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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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Temperature-Aware Core Mapping for Heterogeneous 3D NoC Design Through Constraint Programming 28

Temperature-Aware Core Mapping for Heterogeneous 3D NoC Desi...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Derniriz, Ayhan Ahangari, Hamzeh Ozturk, Ozcan Gebze Tech Univ TR-41400 Kocaeli Turkey Bilkent Univ TR-06800 Ankara Turkey

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

In the context of network-on-Chip (NoC) based Chip Multiprocessor (CMP) design, core mapping for application specific systems is a challenging problem. In such designs, various decisions have to be made that affect performance and power consuinption. Moreover, in emerging 3D NoC systems, by intensification of cooling issues, temperature constraints on hot-spots are added, and problem becomes more complicated. In this paper, an earlier Constraint Programming (('P) methodology for heterogeneous 2D NoC design is extended to 3D model, while critical temperature constraints are accounted. In a single stage, our approach can choose core types from a set of low, medium and high power, and assign them to appropriate places on the mesh which minimizes the overall computation time and communication cost while satisfying the temperature constraints. To achieve our objective, in addition to cores placement problem, tasks should also be scheduled on corresponding cores with matching performance levels to minimize the overall completion time (makespan). Experimental results show that task completion times are more dependent on the mesh structure for our benchmark data. 3D mesh structures may yield shorter task completion times, without compromising thermal constraints. On the other hand, restricting the peak temperature naturally requires the usage of low-performance computing elements which inherently may delay the processing time.

关键词： network-on-Chip 3D Integration Heterogeneous Core Mapping Task Scheduling Constraint Programming

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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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NEPG: Partitioning Large-Scale Power-Law Graphs 21st

NEPG: Partitioning Large-Scale Power-Law Graphs

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21st International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Si, Jiaqi Gan, Xinbiao Bai, Hao Dong, Dezun Pang, Zhengbin Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9783030953911;9783030953904

We propose Neighbor Expansion on power-law graph(NEPG), a distributed graph partitioning method based on a specific power-law graph that offers both good scalability and high partitioning quality. NEPG is based on a heuristic method, Neighbor Expansion, which constructs the different partitions and greedily expands from vertices selected randomly. NEPG improves the partitioning quality by selecting the vertices according to the properties of the power-law graph. We put forward theoretical proof that NEPG can reach the higher upper bound in partitioning quality. The empirical evaluation demonstrates that compared with the state-of-the-art distributed graph partitioning algorithms, NEPG significantly improved partitioning quality while reducing the graph construction time. The performance evaluation demonstrates that the time efficiency of the proposed method outperforms the existing algorithms.

关键词： Graph partitioning distributed graph processing Power-law graph Heuristic algorithm Experimental evaluation

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Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE 21

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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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： Xu, Shujiang Wang, Fansheng Wang, Lianhai Mihaljevic, Miodrag J. Zhang, Shuhui Shao, Wei Huang, Qihao Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Mathematical Institute The Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350329223

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users' identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible. © 2023 IEEE.

关键词： Blockchain

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Eco-FL: Adaptive Federated Learning with Efficient Edge Collaborative Pipeline Training 51

Eco-FL: Adaptive Federated Learning with Efficient Edge Coll...

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51st International conference on parallel processing (ICPP)

作者： Ye, Shengyuan Zeng, Liekang Wu, Qiong Luo, Ke Fang, Qingze Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781450397339

Federated Learning (FL) has been a promising paradigm in distributed machine learning that enables in-situ model training and global model aggregation. While it can well preserve private data for end users, to apply it efficiently on IoT devices yet suffer from their inherent variants: their available computing resources are typically constrained, heterogeneous, and changing dynamically. Existing works deploy FL on IoT devices by pruning a sparse model or adopting a tiny counterpart, which alleviates the workload but may have negative impacts on model accuracy. To address these issues, we propose Eco-FL, a novel Edge Collaborative pipeline based Federated Learning framework. On the client side, each IoT device collaborates with trusted available devices in proximity to perform pipeline training, enabling local training acceleration with efficient augmented resource orchestration. On the server side, Eco-FL adopts a novel grouping-based hierarchical architecture that combines synchronous intra-group aggregation and asynchronous inter-group aggregation, where a heterogeneity-aware dynamic grouping strategy that jointly considers response latency and data distribution is developed. To tackle the resource fluctuation during the runtime, Eco-FL further applies an adaptive scheduling policy to judiciously adjust workload allocation and client grouping at different levels. Extensive experimental results using both prototype and simulation show that, compared to state-of-the-art methods, Eco-FL can upgrade the training accuracy by up to 26.3%, reduce the local training time by up to 61.5%, and improve the local training throughput by up to 2.6x.

关键词： Federated learning edge intelligence pipeline parallelism parallel processing

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An Efficient Graph Accelerator with distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International conference on Algorithms and Architectures for parallel processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： network-on-chip

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