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CPU/GPU Collaboration Techniques for Transfer Learning on Mobile Devices. 23

CPU/GPU Collaboration Techniques for Transfer Learning on Mo...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Valery, Olivier Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Inst Informat Sci Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781538621295

As mobile devices become more capable, the need for customization of mobile services becomes increasingly important for users. Nowadays, mobile device sensors are able to collect information from users throughout the day, which gives insight into their profiles. The advent of modern System-on-Chip architectures has enabled mobile devices to tackle machine learning-oriented problems heretofore reserved to desktop computers. The recent success of deep learning makes it a method of choice for understanding the complex user patterns on mobile devices. Unfortunately, training a deep neural network is often considered as too computationally intensive on mobile devices. To address this issue, we consider transfer learning, a technique that aims to take advantage of deep learning features that have been previously learned to improve the learning performance of another neural network. In this paper, we propose a deep learning framework TransferCL, that supports transfer learning on mobile devices. Our approach relies on the collaboration of the multicore CPU and the integrated GPU to accelerate deep learning computation on mobile devices. We consider three major issues - performance/portability tradeoff, power efficiency, and memory management, propose our approaches, and conduct experiments to evaluate them.

关键词： OpenCL GPGPU mobile computing heterogeneous system System-on-Chip deep learning transfer learning

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distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 IEEE.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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Revealing Potential Performance Improvements By Utilizing Hybrid Work-Sharing For Resource-Intensive Seismic Applications 23

Revealing Potential Performance Improvements By Utilizing Hy...

引用

23rd Euromicro international conference on Parallel, distributed, and Network-Based Processing (PDP)

作者： Siegl, Patrick Buchty, Rainer Berekovic, Mladen Tech Univ Carolo Wilhelmina Braunschweig Chair Chip Design Embedded Comp C3E Braunschweig Germany

ISBN: (纸本)9781479984909

Heterogeneous system architectures are becoming more and more of a commodity in the scientific community. While it remains challenging to fully exploit such architectures, the benefits in performance and hybrid speed-up, by using a host processor and accelerators in parallel in a non-monolithic matter, are significant. Hereby, the energy efficiency is becoming an increasingly critical challenge for future high-performance computing (HPC) systems, which do want to exceed the Exascale barrier with several competing architecture concepts ranging from high-performance CPUs, combined with GPUs acting as floating-point accelerators, to computationally weak CPUs, paired with dedicated and highly-performant FPGA-based accelerators. In this paper, we realize and evaluate a hybrid computing approach based on a two-dimensional seismic streaming algorithm with several heterogeneous system architectures, including conventional HPC approaches based on powerful CPUs and GPUs. Furthermore, we elaborate the effort on an embedded system platform claiming to be a "mini supercomputer" [1]. Several CPU and accelerator combinations are utilized in a manual work-sharing manner with the aim of achieving significant performance speed-ups and a detailed energy-efficiency study. Based on roofline models and experimental evaluations, the paper provides an insight into the fact that hybrid computing is mostly unconditionally beneficial for balanced systems regarding the performance as well as the energy efficiency, aiding the programmer in the decision whether or not costly, manually tuned, homogeneous implementations are worthwhile.

关键词： narrow stencil computing (embedded) hybrid work-sharing multi GPU processing performance evaluation

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Efficiently Managing Large-Scale Keys in HDFS 23

Efficiently Managing Large-Scale Keys in HDFS

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作者： Jin, Wei Geng, Kui Yu, Mingjie Guo, Yunchuan Li, Fenghua Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Security University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665494571

To transparently encrypt/decrypt data stored in Hadoop distributed File System (HDFS), the Key Management Server (KMS) has been designed to automatically assign encryption zone keys (EZK) to encryption zones. However, the current key-management scheme suffers from performance bottleneck. In this paper, dividing key files for storing key data into metadata file and material file, we propose a memory-disk coordinated key management architecture for Hadoop EZK to manage massive keys, where a tree structure is designed to index key metadata stored in memory, and the inverted index structure is used to record materials stored on hard disk. Further balance-tree-based management algorithms without Java serialization is designed to efficiently query keys (including metadata and materials). A series of theoretical analysis are conducted to show the upper and lower boundary of metadata query. Finally, we implement the above scheme in the Hadoop KMS without modifying the original key-management interfaces. A series of experiments are carried out on the simulated data. Experimental results show that, our scheme can efficiently manage ten billion keys and perform much better than the existing KMS in Hadoop. © 2021 IEEE.

关键词： Java Smart cities File systems Memory management distributed databases Metadata Hard disks

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Bloomfish: A Highly Scalable distributed K-mer Counting Framework 23

Bloomfish: A Highly Scalable Distributed K-mer Counting Fram...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Gao, Ta Guo, Yanfei Wei, Yanjie Wang, Bingqiang Lu, Yutong Cieotti, Pietro Balaji, Pavan Taufer, Michela Univ Delaware Newark DE 19716 USA Argonne Natl Lab Argonne IL 60439 USA San Diego Supercomp Ctr La Jolla CA USA Natl Univ Def Technol Changsha Hunan Peoples R China Natl Supercomp Ctr Guangzhou Guangzhou Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538621295

K-mer counting is a fundamental operation in DNA research and genome analytics;its application includes estimating genome assembly, understanding similarities in genomic samples, and merging a newly processed genome with a reference genome. As the genome dataset becomes larger and larger, designing a highly optimized distributed-memory implementation becomes more and more important. Current distributed-memory solutions have two limitations: they have a high memory footprint, and they do not provide advanced optimizations for loading enormous genome datasets into memory. Based on these observations, we present Bloomfish, a distributed, memory-efficient, scalable solution to the limits of current work. To keep a low memory footprint, Bloomfish leverages the compact hash array design of the single-node Jellyfish system and the optimized workflow of the high-performance MapReduce framework Mimir. We have also codesigned Mimir's I/O to efficiently load enormous datasets. We ran Bloomfish on the Tianhe-2 supercomputer with large sequence datasets (up to 24 TB). Our results show that Bloomfish achieves unprecedented scalability in genome analytics.

关键词： K-mer counting Genome analysis MapReduce Memory efficiency I/O optimization Performance and scalability

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distributed search for balanced energy consumption spanning trees in Wireless Sensor Networks

Distributed search for balanced energy consumption spanning ...

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23rd international conference on Advanced Information Networking and Applications workshops

作者： Gagarin, Andrei Hussain, Sajid Yang, Laurence T. Acadia Univ Wolfville NS B4P 2R6 Canada St Francis Xavier Univ Antigonish NS B2G 2W5 Canada

ISBN: (纸本)9781424439997

Sensor networks are commonly used for security and surveillance applications. As sensor nodes have limited battery paower, computing, and storage resources, the energy efficient security techniques are needed. We provide a new heuristic approach to search for balanced and small weight routing spanning trees in a network. The approach is a modification of Kruskal's minimum spanning tree (MST) search algorithm and is based on a distributed search by hierarchical clusters. It provides spanning trees with a lower maximum degree, a bigger diameter and can be used for balanced energy consumption routing in wireless sensor networks (WSNs). The approach can be implemented in parallel as well as a simple locally distributed algorithm. Simulations of a realistic scenario WSN are done based on the transmission energy matrix. The simulation results show that the proposed approach can extend the functional lifetime of a WSN in 3 - 4 times in terms of sensor transmission energy. Possible preliminary clustering of the input network is considered as well.

关键词： Sensor nodes

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SPIN:: A toolkit for distributed scientific computing 23

SPIN:: A toolkit for distributed scientific computing

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23rd international conference of the Chilean-Computer-Science-Society

作者： González, M Univ Versailles St Quentin Yvelines Lab PRiSM Versailles France

ISBN: (纸本)0769520081

The aim of this paper is to present SPIN, an interactive and integrated software environment for scientific computation. The main goal of SPIN is to allow the access to remote computational and storage resources in an easy to use and transparent way from the user's point of view. This environment, based on a multi-tier architecture, has been developed following a state of the art software engineering process which incorporates the CORBA standard (Common Object Request Broker Architecture) the PAC-Amodeus model, (a multi-agent based model for interactive systems architecture) and the Unified Process (UP) to guide the whole life-cycle of the project.

关键词： Common object request broker architecture (CORBA)

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An LWE-Based Multi-Key Privacy-Preserving distributed Deep Learning∗ 23

An LWE-Based Multi-Key Privacy-Preserving Distributed Deep L...

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作者： Antwi-Boasiako, Emmanuel Zhou, Shijie Liao, Yongjian Danso, Ebenezer Kwaku School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Ghana Institute of Management and Public Administration Computer Science Department Accra Ghana China University for Development Studies Ghana

ISBN: (纸本)9781665494571

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. The use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems. © 2021 IEEE.

关键词： Deep learning Resistance Privacy Protocols Costs Collaboration Cryptography

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distributed Trust Evaluation Mechanism of LEO Satellites for 6G Network 23

Distributed Trust Evaluation Mechanism of LEO Satellites for...

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作者： Li, Hui Shi, DongCong Zhou, Ruijin Liao, Dan Zhang, Ming Zhou, Yuliang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Research Institute University of Electronic Science and Technology of China Chengdu China School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665494571

In 6G network, the LEO satellite communication is popular because of the lower development costs and lower transmission delay. However, due to the instability, openness and exposure of links between satellites, LEO satellite is vulnerable to internal attacks during data routing. Considering that trust management has a better performance in solving the internal attacks between satellite communications, this paper proposed a new distributed trust evaluation mechanism (DTEM) of LEO satellites based on D-S evidence theory. In the proposed trust evaluation mechanism, it evaluates the trust degree of satellite nodes by direct trust, indirect trust and aggregate trust. Then it detects the malicious satellite nodes for 6G networks and takes corresponding isolation measures. The simulations verifies that the proposed trust evaluation mechanism has superior performance in packet delivery rate and packet loss rate for data routing. © 2021 IEEE.

关键词： 6G mobile communication Satellites Smart cities Evidence theory Low earth orbit satellites Packet loss Routing

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KunPeng: Parameter Server based distributed Learning systems and Its Applications in Alibaba and Ant Financial 17

KunPeng: Parameter Server based Distributed Learning Systems...

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23rd ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhou, Jun Li, Xiaolong Zhao, Peilin Chen, Chaochao Li, Longfei Yang, Xinxing Cui, Qing Yu, Jin Chen, Xu Ding, Yi Qi, Yuan Alan Ant Financial Serv Grp Hangzhou Zhejiang Peoples R China Alibaba Cloud Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450348874

In recent years, due to the emergence of Big Data (terabytes or petabytes) and Big Model (tens of billions of parameters), there has been an ever-increasing need of parallelizing machine learning (ML) algorithms in both academia and industry. Although there are some existing distributed computing systems, such as Hadoop and Spark, for parallelizing ML algorithms, they only provide synchronous and coarse-grained operators (e.g., Map, Reduce, and Join, etc.), which may hinder developers from implementing more efficient algorithms. This motivated us to design a universal distributed platform termed KunPeng, that combines both distributed systems and parallel optimization algorithms to deal with the complexities that arise from large-scale ML. Specifically, KunPeng not only encapsulates the characteristics of data/model parallelism, load balancing, model sync-up, sparse representation, industrial fault-tolerance, etc., but also provides easy-to-use interface to empower users to focus on the core ML logics. Empirical results on terabytes of real datasets with billions of samples and features demonstrate that, such a design brings compelling performance improvements on ML programs ranging from Follow-the-Regularized-Leader Proximal algorithm to Sparse Logistic Regression and Multiple Additive Regression Trees. Furthermore, KunPeng's encouraging performance is also shown for several real-world applications including the Alibaba's Double 11 Online Shopping Festival and Ant Financial's transaction risk estimation.

关键词： Parameter Server distributed computing system distributed optimization algorithm

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