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PIK-Convolution: Step Convolution Acceleration based on Multi-GPU Architecture

PIK-Convolution: Step Convolution Acceleration based on Mult...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Yutao Peng Yaobin Wang Tianhai Wang Mei Han Yunxin Xu Pingping Tang School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

SimpleConvolution is the most important and time-consuming part of convolutional neural networks (CNN) for image processing. Each slide of the window in two-dimensional convolution will cause repeated memory access. To solve above problems, we propose an algorithm integrated with the hardware level, parallel in kernel Convolution(PIK-Convolution), which reduces the granularity of a SingleConvolution and avoid the problem of storage access pressure caused by spatial locality. Unlike Image to column(im2col) algorithm, we avoid the problem of non-adjacent data in memory directly from hardware perspective. With MGPUSim, a multi-GPU architecture platform, we implement our algorithm. A large number of experimental results show that the proposed algorithm performs well on the new architecture. Compared with the unoptimized algorithm, the overall efficiency of the new algorithm is optimized by 2.2×-7.45×, and the memory access efficiency is an average of 30× of the unoptimized algorithm. Compared to the state-of-the-art convolution acceleration, our algorithm has a performance optimization of 1.5×-2.2.

关键词： distributed processing Convolution Image processing Memory management Hardware Service-oriented architecture Convolutional neural networks Kernel Optimization

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The Art of Sparsity: Mastering High-Dimensional Tensor Storage

The Art of Sparsity: Mastering High-Dimensional Tensor Stora...

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Bin Dong Kesheng Wu Suren Byna Lawrence Berkeley National Laboratory The Ohio State University

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Sparse tensors are prevalent in many applications. While numerous approaches have emerged to optimize the organization of sparse tensors, with the goal of reducing storage requirements and enhancing access performance, a comprehensive examination of the associated time and space complexities has been notably lacking. This study bridges this gap by conducting both theoretical and empirical investigations into various strategies for storing sparse tensors. Our major findings are as follows: (1) Linear address-based organization provides the best balance between storage size and access time; (2) Sparse high-dimensional tensor data can be transformed into lower-dimensional tensors, facilitating efficient storage and access; (3) In the absence of dimension transformation, tree-structured organizations offer compelling performance in low-dimensional tensors and exceptional performance in high-dimensional tensors.

关键词： Bridges distributed processing Tensors Art Conferences distributed databases Organizations

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A distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem

A Distributed Automatic Domain-Specific Multi-Word Term Reco...

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international symposium on parallel and distributed Computing

作者： Ciprian-Octavian Truică Neculai-Ovidiu Istrate Elena-Simona Apostol Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem; (2) perform an in-depth analysis of our architecture in terms of accuracy and scalability; (3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets.

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TGT: Churn Prediction in O2O Logistics with Two-tower Gated Transformer 21

TGT: Churn Prediction in O2O Logistics with Two-tower Gated ...

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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： Guo, Baoshen Wang, Hai Liu, Hao Gong, Wei Xi, Wei Zhou, Xiaolei Wang, Shuai Southeast University China University of Science and Technology of China China Xi'an Jiaotong University China National University of Defense Technology China

ISBN: (纸本)9798350329223

Online-to-Offline (O2O) logistics has developed rapidly and provided great convenience to people's lives. In O2O logistics, one of the essential problems is to predict the logistics customer churn behaviors. Traditional rule-based or survey-based churn prediction methods are laborious and time-intensive while existing machine learning-based churn prediction studies do not consider combining online and offline behaviors, rendering them impractical to be applied to O2O logistics. In this paper, we leverage historical online store operations, logistics provider selections, and the quality of logistics transportation data as the opportunity to investigate customer churn considering both online and offline behaviors. Specifically, we propose TGT, a novel Two-tower Gated Transformer framework for logistics customer churn prediction. TGT consists of two key modules: (i) a two-tower structure to extract complex dependencies among various variables over diverse periods;and (ii) a scale-adaptive convolution module (SAC) to capture the effect of various factors on customer churn in the O2O logistics system across different time scales. We evaluate the effectiveness of the TGT model based on more than half a year of real-world e-commerce data incorporating over 54,000 merchants and 877 million orders, collected from one of the largest logistics platforms in China. Experimental results indicate that our method achieves a 5.5% improvement in the F1 score prediction results. In addition, we deploy TGT at JD Logistics for the real-world churn prediction and reduce the churn rate from 11.75% to 5.94%, which verifies the effectiveness and impacts of the proposed method. © 2023 IEEE.

关键词： Forecasting

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Practical Deanonymization Attack in Ethereum Based on P2P Network Analysis 19

Practical Deanonymization Attack in Ethereum Based on P2P Ne...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Gao, Yue Shi, Jinqiao Wang, Xuebin Shi, Ruisheng Yin, Zelin Yang, Yanyan Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Natl Engn Lab Informat Secur Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Beijing Univ Post & Telecommun Beijing Peoples R China Peoples Publ Secur Univ China Dept Informat Technol & Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781665435741

Ethereum is the second-largest cryptocurrency, which is an open-source public blockchain platform with smart contract functionality. With the increasing popularity of Ethereum, considerable attention has been paid to its privacy and anonymity. Previous work in Ethereum deanonymization mostly focused on the analysis of its transaction graph and user behaviors. In this paper, for the first time we explored the feasibility of deanonymizing Ethereum users based on P2P network analysis. By measurement and analysis, we observed that the attacker can make connections with approximately 90% mainnet synced full nodes. Based on the well-connected supernode, the deanonymization experiments with basic estimators preliminarily indicate that the anonymity of Ethereum P2P network is pretty limited. To further improve the effect of deanonymization, we implemented and evaluated a machine learning based estimator, which reduces the influence of network delay on deanonymization and thus increases the success rate to 88%. At last, we provide the discussion about the anonymity and efficiency of the propagation mechanisms.

关键词： Ethereum Blockchain Anonymity P2P network

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Efficient 2-Segment Routing Based on Local Search With Failure Recovery

Efficient 2-Segment Routing Based on Local Search With Failu...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Jingjing Chen Xueyun Ling Ying Wang Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Segment Routing (SR) is a flexible and efficient source-routing technology. It can forward traffic along arbitrary paths in the network and has good scalability without the maintenance of routing information at intermediate nodes. These advantages make SR widely used in data centers, WANs, MANs, and other networks, particularly in the context of parallel and distributed processing. However, current SR traffic engineering has some shortcomings in terms of network load balancing, failure resiliency, and complete use of SR characteristics, which prevents better optimization of comprehensive network performance. In this paper, we propose a heuristic traffic engineering algorithm (2-SRLS) based on a two-segment routing model (2-SR), which incorporates a traffic-splitting strategy, adjacency segments and failure resiliency. The algorithm supports the technical characteristic of adjacency segments in SR with a flexible source node traffic splitting strategy and can efficiently recover from single-link failures. Experimental results show that our algorithm is close to the theoretical optimum in terms of the performance of reducing the maximum link utilization and has a 10%-16% improvement in active link coverage compared to existing algorithms. The running time is reduced by 70% compared to existing heuristics. In addition, the proposed algorithm can effectively recover from single-link failures on the basis of guaranteed the stability of the maximum link utilization (MLU).

关键词： distributed processing Heuristic algorithms Scalability Routing Load management Stability analysis Maintenance Thermal stability Optimization Resilience

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High-Level FPGA Accelerator Design for Structured-Mesh-Based Explicit Numerical Solvers 35

High-Level FPGA Accelerator Design for Structured-Mesh-Based...

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

作者： Kamalakkannan, Kamalavasan Mudalige, Gihan R. Reguly, Istvan Z. Fahmy, Suhaib A. Univ Warwick Dept Comp Sci Warwick England Pazmany Peter Catholic Univ Fac Informat Technol & Bion Budapest Hungary King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665440660

This paper presents a workflow for synthesizing near-optimal FPGA implementations of structured-mesh based stencil applications for explicit solvers. It leverages key characteristics of the application class and its computation-communication pattern and the architectural capabilities of the FPGA to accelerate solvers for high-performance computing applications. Key new features of the workflow are (1) the unification of standard state-of-the-art techniques with a number of high-gain optimizations such as batching and spatial blocking/tiling, motivated by increasing throughput for real-world workloads and (2) the development and use of a predictive analytical model to explore the design space, and obtain resource and performance estimates. Three representative applications are implemented using the design workflow on a Xilinx Alveo U280 FPGA, demonstrating near-optimal performance and over 85% predictive model accuracy. These are compared with equivalent highly-optimized implementations of the same applications on modern HPC-grade GPUs (Nvidia V100), analyzing lime to solution, bandwidth, and energy consumption. Performance results indicate comparable runtimes with the V100 GPU, with over 2x energy savings for the largest non-trivial application on the FPGA. Our investigation shows the challenges of achieving high performance on current generation FPGAs compared to traditional architectures. We discuss determinants for a given stencil code to be amenable to FPGA implementation, providing insights into the feasibility and profitability of a design and its resulting performance.

关键词： FPGAs Stencil applications Explicit solvers

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distributed Energy Management for Carbon Neutral Data Centers

Distributed Energy Management for Carbon Neutral Data Center...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Chuyi Liu Jianxiong Wan Leixiao Li Guanyu Ren Xiaolei Wang Inner Mongolia University of Technology Huhhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Huhhot China Research Center of Large-Scale Energy Storage Technologies Huhhot China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the continuous expansion of data centers, their carbon emission becomes a serious issue. A number of studies are committing to reduce the carbon emission of data centers. Carbon trading is a promising emission reduction technique which is, however, seldom applied to data centers. To bridge this gap, we propose a carbon-neutral architecture DC 2 for distributed data centers, where each data center consists of three subsystems, i.e., energy subsystem for energy supply, thermal subsystem for data center cooling and the carbon subsystem for carbon trading. Then, we formulate the energy management problem as a Decentralized Partially Observable Markov Decision Processe (Dec-POMDP) and develop a distributed solution framework using Multi-Agent Deep Deterministic Policy Gradient (MAD-DPG). Finally, simulations on real-world data shows that the DC 2 provides a cost saving of 8.16%, and applying MADDPG saves 10.04% of the overall cost compared to Independent MADDPG.

关键词： Data centers distributed processing Costs Markov decision processes Emissions trading Carbon neutral Energy efficiency Data models Service-oriented architecture Carbon

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Optimization for Node Cooperation in Hierarchical Federated Learning 19

Optimization for Node Cooperation in Hierarchical Federated ...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Shen Xin Li Zhuo Chen Xin Beijing Informat Sci & Technol Univ Beijing Key Lab Internet Culture & Digital Dissem Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665435741

With the popularity of the Internet of things (IoT), a large number of data is generated in the network edge. The combination of edge computing and federated learning has become a key technology to reduce latency and energy consumption in mobile networks. The long delay is caused by the nodes transmitting parameters in Hierarchical Federated Learning (HFL). In this work, we propose a Dynamic Cooperative Cluster Algorithm (DCCA) for the delay minimization problem that is proved NP-hard. Moreover, communication between nodes adopts Device-to-Device (D2D) and opportunistic communication. In DCCA, clusters are generated according to the capabilities of different nodes for collaborative training models to reduce delay. We design the DCCA algorithm in two steps. For the first step, we propose an initial dynamic cooperative cluster algorithm based on similarity. For the second step, based on the computing capacity and transmission capacity of the nodes, another algorithm is proposed to adjust the dynamic cooperative cluster according to the core nodes. Extensive simulations are conducted to verify the performance of DCCA. We also observe a reduction in maximum delay by 12.44% and 18.70% respectively, compared with DOA and FedAvg.

关键词： Hierarchical Federated Learning (HFL) Deviceto-Device (D2D) Node Cooperation

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Software-Hardware Co-Optimization for CNNs Based on Reconfigurable Devices 19

Software-Hardware Co-Optimization for CNNs Based on Reconfig...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Liu, Fang Fan, Zimeng He, Yanxiang Peng, Min Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan City Coll Wuhan Peoples R China Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781665435741

Convolutional Neural Network (CNN) is an efficient recognition algorithm used in pattern recognition, image processing, etc. The problem of how to build an efficient computational system for CNNs has become a pressing one, which is traditionally achieved by software optimization or hardware accelerator design. In contrast, this paper argues that software algorithms and hardware architectures affect each other in neural network applications with a high degree of coupling. In this paper, we propose a software and hardware-based co-optimization design to improve CNNs' processing efficiency by co-optimization of software and hardware. First, a modular analysis and collaborative design are carried out for the ShuffleNetV2 model by implementing quantization and improving the computational unit. Second, the model is optimized based on reconfigurable computing devices for its characteristics. Third, 8 bit quantization is implemented, while the depth-separable convolution operation and channel selection module is redesigned to make the module perform operations in a hardware-friendly form. The experimental work was carried out on the FPGA platform Xilin xzynqxc-7Z045 using High Level Synthesis (HLS). The experimental results show that the optimized model has a significant improvement in terms of resource utilization and latency.

关键词： Software-Hardware Co-Optimization Convolutional Neural Networks ShuffleNetV2 Reconfigurable Computing

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