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CPU-GPU Collaborative Acceleration of Bulletproofs-A Zero-Knowledge Proof Algorithm 19

CPU-GPU Collaborative Acceleration of Bulletproofs-A Zero-Kn...

引用

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

作者： Huang, Ying Zheng, Xiaoying Zhu, Yongxin Kong, Xiangcong Jing, Xinru Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665435741

Zero-knowledge proofs help to protect the privacy and security of blockchains by keeping the transaction information private. Bulletproofs are the state-of-the-art zero-knowledge technologies that perform confidential transactions without a trusted setup. However, Bulletproofs are still computationally inefficient to be applied in blockchain applications, and it is of great significance to parallelize Bulletproofs on GPUs. In this paper, we present a CPU-GPU collaborative framework to accelerate the inner-product arguments of Bulletproofs. To our best knowledge, it is the first time that Bulletproofs are implemented in a CPU-GPU hybrid system. The experiments show that our implementation achieves an average speedup ratio of 3.7x. The results also demonstrate that the CPU-GPU collaborative acceleration of Bulletproofs has properties of small size, high efficiency, and high scalability.

关键词： zero-knowledge proof blockchain parallel acceleration CPU-GPU co-acceleration inner-product arguments

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Aparallel procedure for dynamic multi-objective TSP

Aparallel procedure for dynamic multi-objective TSP

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2012 10th IEEE international symposium on parallel and distributed processing with applications, ispa 2012

作者： Li, Weiqi Feng, Mingyuan University of Michigan - Flint Flint MI United States Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9780769547015

This paper proposes a new parallel search procedure for dynamic multi-objective traveling salesman problem. We design a multi-objective TSP in a stochastic dynamic environment. The proposed procedure first uses parallel processors to identify the extreme solutions of the search space for each of k objectives individually at the same time. These solutions are merged into a matrix E. The solutions in E are then searched by parallel processors and evaluated for dominance relationship. The proposed procedure was implemented in two different ways: a master-worker architecture and a pipeline architecture. © 2012 IEEE.

关键词： parallel algorithms

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Rethinking Energy-Efficiency of Heterogeneous Computing for CNN-Based Mobile applications 15

Rethinking Energy-Efficiency of Heterogeneous Computing for ...

引用

15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Wang, Zhen Li, Xi Wang, Chao Cheng, Zhinan Song, Jiachen Zhou, Xuehai Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781538637906

Convolutional Neural Networks (CNNs) have become more and more powerful in the computer vision domain, as they achieve the state-of-the-art accuracy. Despite this, it is generally difficult to apply CNNs on mobile platforms. Client server paradigm is a straightforward way to deploy CNNs on mobile phones, but studies have shown that it suffers serious problems, such as privacy leaks. Recently, researchers focus on using heterogeneous local processors (e.g., GPUs, CPUs) to accelerate the inference of CNNs. Utilizing all local processors available can achieve the highest performance, but it might incur energy-inefficiency. Different from previous works, this paper concerns more about energy-efficiency of CNN based mobile applications. We present an adaptive strategy, which is able to compute the energy-efficiency of all local processors, and further to obtain the energy-efficient device processor combination to perform CNN inference in parallel. The strategy is implemented on ODROID platform, where the evaluation results show that our proposed approach provides 3.67 x higher energy-efficiency with only 9.7% performance degradation on average compared with the greedy strategy which tries to use all local processors available.

关键词： distributed processing Conferences Ubiquitous computing

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Flexible Light Curves Generation System for Astronomical Catalogs 15

Flexible Light Curves Generation System for Astronomical Cat...

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15th IEEE international symposium on parallel and distributed processing with applications (ispa) / 16th IEEE international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Li, Kun Yu, Ce Tang, Shanjiang Sun, Chao Huang, Siyuan Kang, Qinlong Zhao, Qing Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Sci & Technol Sch Comp Sci & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781538637906

Light curves are fundamental tools for variable star astronomy. They describe the change of celestial objects light intensity as time goes on. Because of the sharp increase of data in astronomical researches, the general methods are now utilized cannot meet the requirement of time-domain astronomical observation. In this paper, FLCGS, a Flexible Light Curves Generation System is proposed to achieve scalability and parallelism for generating light curves from astronomical catalogs. We design metadata files for light curves generation based on sky partition. Moreover, a new partition strategy is defined to keep workload balance. The function works very well via dynamic programming when the distribution of Big Data is skewed. We focus on the cross-matching between celestial objects from different catalogs and introduce a new method to determine whether they are the same celestial objects for light curves generation. Experimental results show that FLCGS is nearly 11 times faster than using MySQL, especially when the data is massive.

关键词： Astronomical catalogs Cross-match Light curves Partition Function parallel system

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parallel Stochastic Portfolio Search for Constraint Solving 17

Parallel Stochastic Portfolio Search for Constraint Solving

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IEEE Int Conf on parallel and distributed processing with applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ispa/BDCloud/SocialCom/SustainCom)

作者： Liu, Ke Loeffler, Sven Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Dept Math & Comp Sci MINT Cottbus Germany

ISBN: (纸本)9781728143286

It is not uncommon to observe that the performance of constraint solving on a particular problem can be easily influenced by altering the search strategy, restart policy and their parameter settings, etc. In the multicore era, this lack of robustness can be exploited to speed up the constraint solving by designing a parallel portfolio search which simultaneously executes different incarnations of a sequential solver on the same problem. In this paper, we first investigate the techniques of existing single-solver-based portfolio approach in detail. On this basis, we gained insight into how to improve the portfolio approach. We then present the parallel stochastic portfolio search that benefits from the explicit early diversity resulted from randomization and parallelism. Performance evaluation on some classical constraint satisfaction problems benchmarks shows that our technique can solve harder instances compared to the existing portfolio;and not only that, it has advantages of simplicity for implementation and good scalability.

关键词： Constraint programming Constraint satisifaction problem parallel constraint solving Portfolio search

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ECCH: Erasure Coded Consistent Hashing for distributed Storage Systems 19

ECCH: Erasure Coded Consistent Hashing for Distributed Stora...

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

作者： Xiong, Yinglong Zhou, Jiang Su, Lin Wang, Weiping Chen, Yong Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9781665435741

In this paper, we propose ECCH, an Erasure Coded Consistent Hashing scheme to make better data placement in distributed storage systems. It combines the inherent advantages of consistent hashing together with the storage-efficiency of erasure coding technology. Specifically, ECCH divides data block stream of files into groups according to block IDs. In each group, it encoded data blocks with additional parity blocks by erasure coding. All encoded blocks in the same group are stored on different nodes with consistent hashing distribution. For node failure or data loss, ECCH locates required data through the ID of missing block in a same group for fast recovery. To deal with node changes, ECCH introduces a design of multi-version hash rings to manage data layout. It can prevent the impact of data migration on erasure coding, while achieving data balance with little data movement. We have implemented ECCH on the Sheepdog, a distributed object-based storage system. Evaluation results show that ECCH can greatly improve the space utilization of hashing-based storage systems, while achieving efficient fault tolerance.

关键词： Consistent hashing erasure coding replication data distribution fault tolerance

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Grouping Synchronous to Eliminate Stragglers with Edge Computing in distributed Deep Learning 19

Grouping Synchronous to Eliminate Stragglers with Edge Compu...

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

作者： Gui, Zhiyi Yang, Xiang Yang, Hao Li, Wei Zhang, Lei Qi, Qi Wang, Jingyu Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Huawei Beijing Peoples R China

ISBN: (纸本)9781665435741

With the development of artificial intelligence(AI) applications, a large number of data are generated from mobile or IoT devices at the edge of the network. Deep learning tasks are executed to obtain effective information in the user data. However, the edge nodes are heterogeneous and the network bandwidth is limited in this case, which will cause general distributed deep learning to be inefficient. In this paper, we propose Group Synchronous parallel (GSP), which uses a density-based algorithm to group edge nodes with similar training speeds together. In order to eliminate stragglers, group parameter servers are responsible for coordinating communication of nodes in the group with Stale Synchronous parallel and aggregating the gradients of these nodes. And a global parameter server is responsible for aggregating the gradients from the group parameter servers to update the global model. To save network bandwidth, we further propose Grouping Dynamic Sparsification (GDS). It adjusts the gradient sparsification rate of nodes dynamically based on GSP so as to differentiates the communication volume and makes the training speed of all nodes tend to be the same. We evaluate GSP and GDS's performance on LeNet-5, ResNet, VGG, and Seq2Seq with Attention. The experimental results show that GSP speedups the training by 45% similar to 120% with 16 nodes. GDS on top of GSP can make up for some test accuracy loss, up to 0.82% for LeNet-5.

关键词： distributed deep training gradient compression parameter server gradient sparsification

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FGPA: Fine-Grained Pipelined Acceleration for Depthwise Separable CNN in Resource Constraint Scenarios 19

FGPA: Fine-Grained Pipelined Acceleration for Depthwise Sepa...

引用

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

作者： Xiao, Chunhua Xu, Dandan Qiu, Shi Shi, Chen Ning, Kun Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781665435741

Depthwise Separable Convolution can effectively reduce parameters and operations with little loss in precision, which becomes more and more popular in many innovative neural networks such as MobileNet and Xception. Due to limited computing resources and storage space, how to deploy Depthwise Separable CNN(DSCNN) in a more efficient manner for resource constraint scenarios is still an open and important problem to be explored. In this paper, a Fine-Grained Pipelined Acceleration scheme (FGPA) is proposed. Different from existing works, we partition the DSCNN accelerator into multiple computational modules that support fine-grained operations according to resource constraint and data dependency analysis, which is able to support starting next layer computation before previous layer completion. Along with the proposed scheduling strategy Start as Soon as Possible (SSP), we can accelerate DSCNN in a pipelined parallel manner, and all the intermediate data between layers can be kept on-chip memory to avoid the stream-out cost. In this way, we can not only improve the utilization of on-chip memory and computing resources but also reduce the network computational delay. As a case study, MobileNetV1 is implemented on Zynq XC7Z045. Experimental results indicate that the proposed DSCNN accelerator can achieve 99.68 GOPS and 0.17 GOPS/DSP under 250MHz clock frequency.

关键词： depthwise separable CNN fine-grained partition pipelined parallel FPGA

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Dyacon: JointCloud Dynamic Access Control Model of Data Security Based on Verifiable Credentials 19

Dyacon: JointCloud Dynamic Access Control Model of Data Secu...

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

作者： Zhong, Tao Chang, Junsheng Shi, Peichang Li, Linhui Gao, Fei Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab PDL Changsha Peoples R China Natl Univ Def Technol Coll Comp Inst Comp Technol Changsha Peoples R China

ISBN: (纸本)9781665435741

In JointCloud, when multiple entities conduct data collaboration, role-based access control (RBAC) can offer flexible and secure access control policies to ensure security, privacy of data, which is widely utilized in data access of organizations. However, existing RBAC is confronted with problems such as lacking in dynamic role assignment, explosion of role number and uncertainty and unreliability of users' identities, which affect the performance of access control. In this paper, we propose a model to formulate fine-grained access control policy for data and implement dynamic and automatic assignment of roles in hierarchical tree structure based on multi-dimensional attributes. This model adopts decentralized identifier and verifiable credential to describe entities' identity, and leverages blockchain and smart contract to conduct access control policies. This model makes up for the deficiencies of existing RBAC and strengthens the dynamicity of access control policies. Experimental results shows that the model can assign roles to users in a dynamic and automatic way, which verifies the feasibility of the model.

关键词： Role-based access control dynamic decentralized identifier verifiable credential blockchain

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A Sleep Stage Classification Method via Combination of Time and Frequency Domain Features based on Single-Channel EEG 19

A Sleep Stage Classification Method via Combination of Time ...

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

作者： Zhao, Caihong Neng, Wenpeng Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Heilongjiang Univ Coll Elect Engn Heilongjiang Key Lab Database & Parallel Comp Harbin Peoples R China

ISBN: (纸本)9781665435741

Sleep staging is an important method to diagnose and treat insomnia, sleep apnea, and other sleep disorders. Compared with the multi-channel automatic sleep staging system, the single-channel EEG signal contains less information, and the traditional single analysis domain feature parameter extraction algorithm cannot meet the requirement of sleep stage classification accuracy. To solve this problem, we propose an automatic sleep staging method based on the combination of time-domain and frequency-domain features based on single-channel EEG signals. Empirical mode decomposition is used to decompose EEG signals in the time domain to obtain the decomposed signals at different time scales. Multiple local features are extracted from each decomposed signal. The frequency-domain features of EEG signals are obtained by using the frequency domain decomposition of EEG signals in various rhythms. The time-domain and frequency-domain decomposition features are combined into eigenvectors and selected for sleep staging. The experimental results show that the sleep staging method proposed in this paper with time-frequency domain features of single-cyhannel EEG signals can approach the accuracy of sleep staging of multi-channel signals on the same data set, and superior to the sleep staging method with the same single-channel EEG signals.

关键词： sleep stage classification Empirical Mode Decomposition frequency-domain decomposition time-frequency domain features

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