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NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs 19

NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

引用

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

作者： Huang, Xiandong Wang, Qinglin Lu, Shuyu Hao, Ruochen Mei, Songzhu Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha 410073 Peoples R China Univ Pittsburgh Dept Biomed Informat Pittsburgh PA USA

ISBN: (纸本)9781665435741

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in many-core CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases.

关键词： CNNs Convolution FFT NUMA ARMv8 Many-Core

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A New Architecture for Anonymous Use of Services in distributed Computing Networks 15

A New Architecture for Anonymous Use of Services in Distribu...

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： Ma, Yuxiang Wu, Yulei Ge, Jingguo Li, Jun Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100195 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England

ISBN: (纸本)9781538637906

With the rapid development of the Internet, applications in distributed processing systems have attracted more and more attention. The privacy protection is one of the most important factors to ensure the security of underlying networks in a distributed computing system. Users expect to use a service (e.g., a computing service) anonymously, while service providers want to make sure that the users are trusted and their behavior can be accountable. Given that these two factors are contradictory, how to provide an efficient solution to achieve trusted anonymous access to the services in distributed computing networks is a challenging task. In a distributed computing system, when a task needs to be computed by a worker (or server), the transmitted data over the underlying network to perform this computing service might include many different contents, and even lots of different flows. To this end, this paper presents a new and efficient architecture to help users use services anonymously in distributed computing networks, without loss of trustworthiness. The experimental results show that the proposed architecture introduces acceptable overheads, and can achieve the goal of balancing anonymity and trust in distributed computing networks.

关键词： Computer architecture Cloud computing Servers Service-oriented architecture Privacy IP networks

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HDSVM: A High Efficiency distributed SVM Framework over Data Stream 15

HDSVM: A High Efficiency Distributed SVM Framework over Data...

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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： Hou, Yan Wang, Yijie Ma, Xingkong Cheng, Li Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538637906

The application of Support Vector Machine (SVM) over data stream is growing with the increasing real-time processing requirements in classification field, like anomaly detection and real-time image processing. However, the dynamic live data with high volume and fast arrival rate in data streams make it challenging to apply SVM in data stream processing. Existing SVM implementations are mostly designed for batch processing and hardly satisfy the efficiency requirement of stream processing for its inherent complexity. To address the challenges, we propose a high efficiency distributed SVM framework over data stream (HDSVM), which consists of two main algorithms, incremental learning algorithm and distributed algorithm. Firstly, we propose a partial support vectors reserving incremental learning algorithm (PSVIL). By selecting a subset of support vectors based on their distances to classification hyperplane instead of the universal set to update SVM, the algorithm achieves lower time overhead while ensuring accuracy. Secondly, we propose a distribution remaining partition and fast aggregation distributed algorithm (DRPFA) for SVM. The real-time data is partitioned based on the original distribution with clustering instead of random partition, and historical support vectors are partitioned based on their distances to the classification hyperplane. The global hyperplane can be obtained by averaging the parameters of local hyperplanes due to the above partition strategy. Extensive experiments on Apache Storm show that the proposed HDSVM achieve lower time overhead and similar accuracy compared with the state-of-art. Speed-up ratio is increased by 2-8 times within 1% accuracy deviation.

关键词： distributed SVM Incremental learning Data stream Support vector selection Distance distribution

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parallel and distributed processing and applications 2005

引用

丛书名： Lecture Notes in Computer Science

2005年

作者： Jiannong Cao Laurence T. Yang Minyi Guo Francis Lau

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distributed V2V Computing Offloading Method Based on Delay and Fairness Awareness 19

Distributed V2V Computing Offloading Method Based on Delay a...

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

作者： Yang, Guoliang Xiong, Mengqi Feng, Guangsheng Liu, Yuzheng Huang, Yun Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China China Aerosp Sci & Ind Corp Res Inst 2 Beijing Peoples R China

ISBN: (纸本)9781665435741

The roadside Basic unit (RSU) can relieve the heavy computing and analysis pressure required by vehicles'computing tasks. Due to the mobility of vehicles and limited computing resources, it is difficult to deal extensive vehicle tasks in time. Therefore, we consider the computation task offloading problem between the vehicles, as well as the dynamic topology caused by the vehicle movement and the randomness of task arrival, and propose an online offloading method based on fairness awareness, which can be ensured tolerable delays and the requirement of the quality of experience (QoE) between vehicles by introducing delay-aware and price-constrained virtual queues. By Lyapunov optimization technology, the objective function is transformed into a drift-plus-penalty profit minimum bound problem, achieveing all the offloading decisions by the realtime resource requirements and topology state. Finally, extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

关键词： Mobile computing offloading vehicle-to-vehicle communication delay tolerance fairness Lyapunov optimization

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Introduction of Improved Repairing Locality into Secret Sharing Schemes with Perfect Security 15

Introduction of Improved Repairing Locality into Secret Shar...

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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： Fu, Yue Sun, Shuhao Li, Dagang Liu, Peng Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peking Univ Inst Big Data Technol Shenzhen Grad Sch Shenzhen Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538637906

Repairing locality is an appreciated feature for distributed storage, in which a damaged or lost data share can be repaired by accessing a subset of other shares much smaller than is required for decoding the complete data. However for Secret Sharing (SS) schemes, it has been proven theoretically that local repairing can not be achieved with perfect security for the majority of threshold SS schemes, where all the shares are equally regarded in both secret recovering and share repairing. In this paper we make an attempt on decoupling the two processes to make secure local repairing possible. Dedicated repairing redundancies only for the repairing process are generated, which are random numbers to the original secret Through this manner a threshold SS scheme with improved repairing locality is achieved on the condition that security of repairing redundancies is ensured, or else our scheme degenerates into a perfect access structure that is equivalent to the best existing schemes can do. To maximize security of the repairing redundancies, a random placement mechanism is also proposed.

关键词： distributed storage repairable secret sharing repairing locality perfect security

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Profiling HPC applications with Low Overhead and High Accuracy 19

Profiling HPC Applications with Low Overhead and High Accura...

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

作者： Zhao, Jingyuan Liu, Xin Liu, Yao Jiao, Penglong Liu, Jinshuo Xue, Wei East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China Natl Res Ctr Parallel Comp Engn & Technol Beijing Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665435741

As the parallel scale of HPC applications represented by earth system models becomes larger and the computing cost becomes higher, the performance of HPC applications is increasingly critical. Profiling HPC applications accurately helps to model the applications and find the performance bottlenecks. However, due to the complexity of HPC applications, the diversity of programming languages, the differences of individual programming habits, and multiple architectures, accurate profiling becomes very tough. In this paper, we propose LPerf: a low-overhead and high-accuracy profiler for HPC applications. To reduce the profiling overhead and improve the profiling accuracy, we propose a preprocessing method which can automatically instrument with tunable granularity thus significantly reducing the run-time overhead of profiling, an aggregated caller-callee relationship which is used to locate relationship of functions efficiently, and a profiling-aware method which can precisely calculate running time of functions. The experimental results show that the error rate of profiling reaches 0.02%, and the overhead reaches 1.6%, in the earth system model named CAS-ESM. Compared with the baselines, the precision, accuracy, and overhead of LPerf have reached the state of the art.

关键词： LLVM instrumentation profiling performance modeling

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PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching 19

PCGraph: Accelerating GNN Inference on Large Graphs via Part...

引用

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

作者： Zhang, Lizhi Lai, Zhiquan Tang, Yu Li, Dongsheng Liu, Feng Luo, Xiaochun Natl Univ Def Technol Comp Coll Natl Lab Parallel & Distributed Proc PDL Changsha Hunan Peoples R China PLA News Media Ctr Beijing Peoples R China

ISBN: (纸本)9781665435741

Graph neural networks (GNNs) have been emerging as powerful learning tools for unstructured data and successfully applied to many graph-based application domains. Sampling-based GNN inference is commonly adopted in existing graph learning frameworks to handle large-scale graphs. However, this approach is restricted by the problems of redundant vertex embedding computation in GPU and inefficient loading of vertex features from CPU to GPU. In this paper, we propose PC-Graph, a system that supports adaptive GNN inference and feature partition caching. PCGraph significantly reduces the vertex embedding computation time by adaptive GNN inference technology, which selects the optimal inference algorithm and minimizes vertex embedding computation. PCGraph also reduces the redundant data transfer between the CPU and GPU by partition the target vertices and caching their corresponding partitions in turn. We evaluate PCGraph against two state-of-the-art industrial GNN frameworks, i.e., PyG and DGL, on a diverse array of benchmarks. Experimental results show that PCGraph reduces up to 99% vertex embedding computation and 98.5% data loading time, and achieves up to 360x performance speedup over the state-of-the-art baselines.

关键词： graph neural networks inference embedding computation feature caching pipeline parallel

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MDDRSPF: A Model Driven distributed Real-time Stream processing Framework 17

MDDRSPF: A Model Driven Distributed Real-time Stream Process...

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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： Wen, Yijun Zhang, Li Wang, Cheng Tsinghua Univ Sch Software Beijing Peoples R China Naval Res Acad NVRA Beijing Peoples R China

ISBN: (纸本)9781728143286

Some enterprises have demands for distributed stream processing framework to manage and process diverse business services. But their business is usually user-oriented, the access data sources and outputs are heterogeneous and need to be configurable, and the user-oriented service mode requires the platform have the ability to deploy new algorithm dynamically and provide on-demand services. As we know, there is no system that can meet all these requirements, hence a Model Driven distributed Real-time Stream processing Framework is proposed. By building project and task models, the functional model is separated from the implementation platform. The framework support multi-source heterogeneous data input and configurable output, on-demand services based on user's request, and algorithm dynamic loading. Service oriented architecture is deployed to lower the threshold of algorithm development and deployment. Furthermore, the functionality and performance is evaluated with a practical case to verify whether the system can meet the needs of practical application.

关键词： Model Driven Architecture distributed Real-time Stream processing Framework project task

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An inframarginal analysis based resource allocation method in distributed computing

An inframarginal analysis based resource allocation method i...

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international symposium on parallel and distributed processing with applications, ispa

作者： Liu, Jun Wang, En Ze Qiao, Jian Zhong Lin, Shu Kuan Department of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of Software System Application and Development Generic Technology Shenyang China

ISBN: (纸本)9780769541907

the key of distributed computing is to fully utilize computing resources. However, the efficiency of distributed computing can always be affected by volatility of available nodes and uncertainty of network environment. In order to enhance resource allocation efficiency in distributed computing, this paper presents an inframarginal analysis based resource allocation method, which distributes computing task to the node with more comparative advantage carrying on the task. Simulation results proved that it is effective on resource allocation in the distributed computing environment. © 2010 IEEE.

关键词： Resource allocation

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