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M3AT: Monitoring Agents Assignment Model for Data-Intensive Applications 28

M3AT: Monitoring Agents Assignment Model for Data-Intensive ...

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28th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Kashansky, Vladislav Kimovski, Dragi Prodan, Radu Agrawalt, Prateek Marozzo, Fabrizio Iuhaszl, Gabriel Justyna, Marek Garcia-Blas, Javier Univ Klagenfurt Inst Informat Technol Klagenfurt Austria South Ural State Univ Sch Elect Engn & Comp Sci Chelyabinsk Russia Lovely Profess Univ Phagwara India Univ Calabria DIMES Dept Calabria Italy West Univ Timisoara Timisoara Romania Poznan Supercomp & Networking Ctr Poznan Poland Univ Carlos III Madrid Dept Comp Sci & Engn Madrid Spain

ISBN: (纸本)9781728165820

Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce an Integer Linear Programming (ILP) model called M3AT for optimized assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model's input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for sub-systems composed of up to 200 monitoring agents with complex I/O policies, while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.

关键词： Monitoring systems high performance computing aggregation systems control data-intensive systems generalized assignment problem SCIP optimization suite

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MetaWBC: POSIX-compliant metadata write-back caching for distributed file systems 22

MetaWBC: POSIX-compliant metadata write-back caching for dis...

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Proceedings of the international conference on High Performance computing, Networking, Storage and Analysis

作者： Yingjin Qian Wen Cheng Lingfang Zeng Marc-André Vef Oleg Drokin Andreas Dilger Shuichi Ihara Wusheng Zhang Yang Wang André Brinkmann Data Direct Networks (DDN) China Huazhong University of Science and Technology (HUST) China Zhejiang Lab China Johannes Gutenberg University Mainz Germany Whamcloud Inc Whamcloud Inc Canada Data Direct Networks (DDN) Japan Tsinghua University China Chinese Academy of Sciences China

In parallel and distributed file systems, caching can improve data performance and metadata operations. Currently, most distributed file systems adopt a write-back data cache for performance and a write-through metadata cache for simplifying consistency. However, with modern file systems scales and workloads, write-through metadata caching can impact overall file system performance, e.g., through lock contention and heavy RPC loads required for namespace synchronization and transaction *** paper proposes a novel metadata write-back caching (MetaWBC) mechanism to improve the performance of metadata operations in distributed environments. To achieve extreme metadata performance, we developed a fast, lightweight, and POSIX-compatible memory file system as a metadata cache. Further, we designed a file caching state machine and included other performance optimizations. We coupled MetaWBC with Lustre and evaluated that MetaWBC can outperform the native parallel file system by up to 8x for metadata-intensive benchmarks, and up to 7x for realistic workloads in throughput.

关键词： file system data-intensive application write-back caching lustre metadata

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distributed Deep Learning on Heterogeneous computing Resources Using Gossip Communication 12th

Distributed Deep Learning on Heterogeneous Computing Resourc...

引用

12th international conference on Large-Scale Scientific Computations (LSSC)

作者： Georgiev, Dobromir Gurov, Todor Bulgarian Acad Sci Inst Informat & Commun Technol Acad G Bonchev StBlock 25A Sofia 1113 Bulgaria

ISBN: (纸本)9783030410322;9783030410315

With the increased usage of deep neural networks, their structures have naturally evolved, increasing in size and complexity. With currently used networks often containing millions of parameters, and hundreds of layers, there have been many attempts to leverage the capabilities of various high-performance computing architectures. Most approaches are focused on either using parameter servers or a fixed communication network, or exploiting particular capabilities of specific computational resources. However, few experiments have been made under relaxed communication consistency requirements and using a dynamic adaptive way of exchanging information. Gossip communication is a peer-to-peer communication approach, that can minimize the overall data traffic between computational agents, by providing a weaker guarantee on data consistency - eventual consistency. In this paper, we present a framework for gossip-based communication, suitable for heterogeneous computing resources, and apply it to the problem of parallel deep learning, using artificial neural networks. We present different approaches to gossip-based communication in a heterogeneous computing environment, consisting of CPUs and MIC-based co-processors, and implement gossiping via both shared and distributed memory. We also provide a simplistic approach to load balancing in a heterogeneous computing environment, that proves efficient for the case of parallel deep neural network training. Further, we explore several approaches to communication exchange and resource allocation, when considering parallel deep learning using heterogeneous computing resources, and evaluate their effect on the convergence of the distributed neural network.

关键词： Deep learning Gossip communication Heterogeneous high-performance computing

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Dual-Way Gradient Sparsification for Asynchronous distributed Deep Learning 20

Dual-Way Gradient Sparsification for Asynchronous Distribute...

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49th international conference on parallel Processing (ICPP)

作者： Yan, Zijie Xiao, Danyang Chen, Mengqiang Zhou, Jieying Wu, Weigang Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781450388160

distributed parallel training using computing clusters is desirable for large scale deep neural networks. One of the key challenges in distributed training is the communication cost for exchanging information, such as stochastic gradients, among training nodes. Recently, gradient sparsification techniques have been proposed to reduce the amount of data exchanged and thus alleviate the network overhead. However, most existing gradient sparsification approaches consider only synchronous parallelism and cannot be applied in asynchronous distributed training. In this paper, we present the dual-way gradient sparsification (DGS) approach for asynchronous distributed training. Different from existing approaches, where workers download the global model from parameter server, our approach lets workers download model difference from the parameter server. With such design, both the gradients sent to server and the model parameters downloaded from server can be sparsified and the dual-way communication cost between server and workers can be significantly reduced. To preserve accuracy under the dual-way sparsification, we design a new momentum, SAMomentum, which is sparsification aware and offers significant optimization boost. Extensive experiments with different scales on ImageNet and CIFAR-10 show that, (1) compared with similar works, DGS with SAMomentum consistently achieves better performance;(2) DGS improves the scalability of asynchronous training, especially with limited networking infrastructure.

关键词： Gradient Sparification Deep Learning distributed Deep Learning Asynchronous Training Asynchronous Gradient Descent

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Collaborative computing in Vehicular networks: A Deep Reinforcement Learning Approach

Collaborative Computing in Vehicular Networks: A Deep Reinfo...

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IEEE international conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Li, Mushu Gao, Jie Zhang, Ning Zhao, Lian Shen, Xuemin (Sherman) Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Texas A&M Univ Corpus Christi Dept Comp Sci Corpus Christi TX USA Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781728150895

Mobile edge computing (MEC) has been recognized as a promising technology to support various emerging services in vehicular networks. With MEC, vehicle users can offload their computation-intensive applications (e.g., intelligent path planning and safety applications) to edge computing servers located at roadside units. In this paper, an efficient computing offloading and server collaboration approach is proposed to reduce computing service delay and improve service reliability for vehicle users. Task partition is adopted, whereby the computation load offloaded by a vehicle can be divided and distributed to multiple edge servers. By the proposed approach, the computation delay can be reduced by parallel computing, and the failure in computing results delivery can also be alleviated via cooperation among edges. The offloading and computing decision-making is formulated as a long-term planning problem, and a deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to achieve the optimal solution of the complex stochastic nonlinear integer optimization problem. Simulation results show that our collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.

关键词： mobile edge computing vehicular network deep reinforcement learning computing offloading

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Construction of Completely Independent Spanning Tree Based on Vertex Degree 1

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21st international conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2020

作者： Liu, Ningning Zhang, Yujie Fan, Weibei School of Computer Science and Technology Soochow University Suzhou China School of Computer Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

Interconnection networks have been extensively studied in the field of parallel computer systems. In the interconnection network, completely independent spanning tree (CISTs) plays an important role in the reliable transmission, parallel transmission, and safe distribution of information. Two spanning trees T1 and T2 of graph G are completely independent if, for any two distinct vertices u and v of G, the two paths from u to v on T1 and T2 are internally disjoint. The spanning trees T1, T2, …, Tk of G are completely independent spanning trees if they are pairwise completely independent. In 2015, Hasunuma proof that G has ⌊n(G)k⌋ CISTs if δ(G) ≥ n(G) - k, 3≤k≤n(G)2 and n(G) ≥ 7. In this paper, we prove that G has ⌊5n(G)12⌋ CISTs if δ(G) ≥ n(G) - 2 and n(G) ≥ 12, and G has (t+ 1 ) -CISTs if δ(G) ≥ n(G) - 3 and n(G) = 3 t- 2 ≥ 23. © 2021, Springer Nature Switzerland AG.

关键词： Interconnection networks (circuit switching)

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Cooperative Caching Strategy Based on Dynamic Cache Value Perception in Edge networks

Cooperative Caching Strategy Based on Dynamic Cache Value Pe...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Yue Guo Guangxu Zhou Junfeng Hou Hongjun Zhang Yaofeng Li Xuchang Branch of Henan Provincial Tobacco Company Xuchang

Caching the content that users will request at the edge of the network can effectively alleviate the pressure of the core network, but the existing research results pay less attention to the cache value of the content, which makes the cache strategy performance low. A dynamic cache value aware collaborative cache strategy is proposed, which is different from the traditional single mode. The user layer caches the content according to the user’s request intention, and the MBS and SBS layers consider the global information for collaborative cache placement. Combined with the user’s movement trajectory, residence time and user’s request intention, a value perception method of dynamic content cache is proposed to determine the content to be cached. A content cache decision algorithm based on deep reinforcement learning is also designed, which uses the cooperative cache placement decision with MBS and SBS layers. Simulation results show that the cache hit rate of the proposed cache strategy is increased by 5.0% 10.8% and 18.9% on average compared with the three baseline schemes, indicating that the proposed strategy can significantly offload core network traffic and improve edge cache performance.

关键词： Deep learning Cooperative caching Simulation Heuristic algorithms Collaboration Telecommunication traffic Reinforcement learning

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BHMVD: Binary Code-based Hybrid Neural Network for Multiclass Vulnerability Detection

BHMVD: Binary Code-based Hybrid Neural Network for Multiclas...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Ningning Cui Liwei Chen Gewangzi Du Tongshuai Wu Chenguang Zhu Gang Shi Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Precise binary code vulnerability detection is a significant research topic in software security. Currently, the majority of software is released in binary form, and the corresponding vulnerability detection approaches for binary code are desired. Existing deep learning-based detection techniques can only detect binary code vulnerabilities but cannot precisely identify the types of vulnerabilities. This paper proposes a Binary code-based Hybrid neural network for Multiclass Vulnerability Detection, dubbed BHMVD. BHMVD generates binary slices according to the control dependence and data dependence of library/ API function calls, and then extracts syntax features from binary slices to generate type slices, which can help identify vulnerability types. This paper uses a hybrid neural network of CNN-BLSTM to extract vulnerability features from binary and type slices. The former extracts local features, while the latter extracts global features. Experiment results on 19 types of vulnerabilities show that BHMVD is effective for binary code-based multiclass vulnerability detection, and using a hybrid neural network can improve detection ability.

关键词： Neural networks Binary codes Syntactics Feature extraction Software Security Data mining

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ReSLB: Load Balanced Workflow for distributed Deep Learning Mass Spectrometry Database

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Li Mocheng Liu Yang Ou Yang Chen Zhiguang Xiao Nong Chen Tao Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences(Beijing) Beijing Institute of Lifeomics Beijing China

The proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. The parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. The heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. This paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. This work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that.

关键词： Deep learning Scalability Neural networks Data preprocessing distributed databases Graphics processing units Clustering algorithms

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WBA: A Warping-based Approach to Generating Imperceptible Adversarial Examples

WBA: A Warping-based Approach to Generating Imperceptible Ad...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chengyao Hua Shigeng Zhang Weiping Wang Zhankai Li Jian Zhang School of Computer Science and Engineering Central South University Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL)

The human can easily recognize the incongruous parts of an image, for example, perturbations unrelated to the image itself, but are poor at spotting the small geometric transformations. However, in terms of the robustness of deep neural networks (DNNs), the ability to properly recognize objects with small geometric transformations is still a challenge. In this work, we investigate the problem from the perspective of adversarial attacks: does the performance of DNNs degrade even when small geometric transformations are applied to images? To this end, we propose a novel adversarial attack method, called WBA, a Warping-Based Adversarial attack method, which does not introduce information independent of the original images but manipulates the existing pixels of the images by elastic warping transformations to generate adversarial examples that are imperceptible to the human eye. At the same time, existing adversarial attacks typically generate adversarial examples by modifying pixels in the spatial domain of the image, the addition of such perturbations introduces extra information unrelated to the image itself and is easily detected by the naked eyes. We demonstrate the effectiveness of WBA by extensive experiments on commonly used datasets, including MNIST, CIFAR10, and ImageNet. The results show that WBA can quickly generate adversarial examples with the highest adversarial strength, consumes less time, and can be comparable to optimization-based adversarial attack methods in image perception evaluation metrics such as LPIPS, SSIM, and far more than gradient direction-based iterative methods.

关键词： Measurement Deep learning Privacy Image recognition Perturbation methods Neural networks Robustness

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