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Scalable irregular parallelism with GPUs: getting CPUs out of the way 22

Scalable irregular parallelism with GPUs: getting CPUs out o...

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

作者： Yuxin Chen Benjamin Brock Serban Porumbescu Aydın Buluç Katherine Yelick John D. Owens University of California Lawrence Berkeley National Laboratory

We present Atos, a dynamic scheduling framework for multi-node-GPU systems that supports PGAS-style lightweight one-sided memory operations within and between nodes. Atos's lightweight GPU-to-GPU communication enables latency hiding and can smooth the interconnection usage for bisection-limited problems. These benefits are significant for dynamic, irregular applications that often involve fine-grained communication at unpredictable times and without predetermined patterns. Some principles for high performance: (1) do not involve the CPU in the communication control path; (2) allow GPU communication within kernels, addressing memory consistency directly rather than relying on synchronization with the CPU; (3) perform dynamic communication aggregation when interconnections have limited bandwidth. By lowering the overhead of communication and allowing it within GPU kernels, we support large, high-utilization GPU kernels but with more frequent communication. We evaluate Atos on two irregular problems: Breadth-First-Search and PageRank. Atos outperforms the state-of-the-art graph libraries Gunrock, Groute and Galois on both single-node-multi-GPU and multi-node-GPU settings.

关键词： irregular application asynchronous PGAS distributed GPUs

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Severity Classification of Mental Health Related Tweets

Severity Classification of Mental Health Related Tweets

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IEEE distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Praatibh Surana Mirza Yusuf Sanjay Singh Manipal Institute of Technology MAHE Manipal India

ISBN: (纸本)9781665429528

The use of social media has drastically gone up over the last decade. With this comes more opportunity and also more problems. There is a rise in the number of mental health-related issues, and it is to some extent possible to detect such cases via user posts and tweets (in our case). Previous research has focused on classifying mental health diseases such as depression, bipolar disorder, schizophrenia, etc., from already filtered data. However, not much has been done to filter out tweets that might be sarcastic, which are generally misclassified, or tweets that might not be intended in a harmful way and, in general, classify tweets based on their severity with regards to mental health. This paper uses multiple models to classify tweets based on their severity and classify them into three classes that help determine whether they help people with mental conditions or sarcasm. We use famous neural network architectures such as Bidirectional LSTMs, GRUs, and a custom HYBRID model to carry out the classification. The models could detect sarcasm in tweets and identify tweets that were helpful despite having words like “depression” and “anxiety.” We obtained F1 scores of 74% on completely unseen data, which is a good starting point considering the limited available data. This paper should serve as a utility for future research in this area and act as a primary data collection and segregation filter.

关键词： Social networking (online) Neural networks distributed databases Mental health Very large scale integration Depression Data models

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A distributed Game Theoretic Approach for Blockchain-based Offloading Strategy

A Distributed Game Theoretic Approach for Blockchain-based O...

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

作者： Liu, Weikang Cao, Bin Zhang, Lei Peng, Mugen Daneshmand, Mahmoud Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

ISBN: (纸本)9781728150895

Keeping patients' sensitive information secured and untampered in the e-Health system is of paramount importance. Emerging as a promising technology to build a secure and reliable distributed ledger, blockchain can protect data from being falsified, which has attracted much attention from both academia and industry. However, with limited computational resources, medical IoT devices do not have efficient ability to fulfill the functionalities as a full node in wireless blockchain network (WBN). Facing this dilemma, Mobile Edge computing (MEC) brings us dawn and hope through offloading the high resource demanding blockchain functionalities at the IoT devices to the MEC. However, aiming to maximize the mining profit, most of existing offloading strategies have ignored the other needs of wireless devices, e.g., faster transaction writing. In this paper, according to different needs, blockchain nodes are firstly divided into two categories. One is blockchain users whose needs are faster transaction uploading, the other is blockchain miners whose goals are maximum revenue. Then, to maximize both the utilities of blockchain users and blockchain miners, a Stackelberg game is introduced to formulate the interaction between them. From the simulation results, this game is proved to converge to a unique optimal equilibrium.

关键词： Blockchain

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Predicting Steering for Autonomous Vehicles Based on Crowd Sensing and Deep Learning 1

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6th international conference on Internet of Vehicles and the 9th international Symposium on Cloud and Service computing, IOV/SC2 2019

作者： Liu, Taiyu Li, Jinglin Yuan, Quan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9783030386511

ISBN: (纸本)9783030386504

The challenge in ensuring the reliability of autonomous vehicles is full awareness of the surrounding environment and high-precision steering control. The latest solutions to this challenge include deep learning technologies that provide end-to-end solutions to predict steering angles directly from environmental cognition information with high accuracy. Under the background of 5G technology, edge device has certain computing power, which can reduce the load of on-board computing equipment. In this paper, we present a new distributed perception-decision network model. This model allows the network’s computing tasks to be offloaded to the edge computing devices to reduce the consumption of vehicle-mounted computing devices. The feasibility of the model is verified by experiments. Compared with the existing methods, the model also has a higher accuracy of steering prediction. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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distributed Task Splitting and Offloading in Mobile Edge computing 14th

Distributed Task Splitting and Offloading in Mobile Edge Com...

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14th EAI international conference on Communications and networking in China, ChinaCom 2019

作者： Ren, Yanling Weng, Zhihui Li, Yuanjiang Xie, Zhibin Song, Kening Sun, Xiaolei Jiangsu University of Science and Technology Zhenjiang China PLA AF 95829 Xiaogan China PLA Navy Submarine Academy Qingdao China

ISBN: (纸本)9783030411138

With the rapid development of the mobile internet, many emerging compute-intensive and data-intensive tasks are extremely sensitive to latency and cannot be implemented on mobile devices (MDs). To solve this problem, mobile edge computing (MEC) appears to be a promising solution. In this paper, we propose a distributed task splitting and offloading algorithm (DSOA) for the scenario of multi-device and multi-MEC servers in ultra-dense networks (UDN). In the proposed scheme, the MDs can perform their tasks locally or offload suitable percentage of tasks to the MEC server. The optimization goal is to minimize the overall task computation time. Since the MDs are selfish, we propose a game theory approach to achieve optimal global computation time. Finally, the numerical simulation results verify that the algorithm can effectively reduce global computation time. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Game theory

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Benchmarking Tool for Modern distributed Stream Processing Engines 33

Benchmarking Tool for Modern Distributed Stream Processing E...

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33rd international conference on Information networking (ICOIN)

作者： Hanif, Muhammad Yoon, Hyeongdeok Lee, Choonhwa Hanyang Univ Div Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781538683507

There is an upsurge in the usage and adaptation of streaming applications in the recent years by both industry and academia. At the core of these applications is streaming data processing engines that perform resource management and allocation in order to support continuous track of queries over distributed data streams. Several stream processing engines exists to handle these distributed streaming applications. In this paper, we present different challenges of the stream processing systems, in particular to stateful operators and implement Linear Road benchmark to examine the characteristic and performance metrics of the streaming system, in particular Apache Flink. Furthermore, we examine that Apache Flink can be used as a core for an efficient Linear Road application implementation for distributed environments without breaching the SLA requirements of the application.

关键词： Streaming Benchmarking SLA distributed computing Cloud computing

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Chroma Prediction for Low-Complexity distributed Video Encoding 4

Chroma Prediction for Low-Complexity Distributed Video Encod...

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4th international conference on Multimedia computing, networking and Applications, MCNA 2020

作者： Langen, Kai Makaroff, Dwight Mayer-Patel, Ketan University of Saskatchewan Department of Computer Science SaskatoonSKS7N 3C9 Canada University of North Carolina Department of Computer Science Chapel HillNC27599-3175 United States

ISBN: (数字)9781728183732

ISBN: (纸本)9781728183732

Inter-frame encoding is an integral technology in modern video codec design, used to achieve high compression efficiency by reducing temporal redundancy. An important step in inter-frame encoding is the temporal prediction loop, involving computationally expensive block-matching algorithms. When the video server is concerned with customer Quality of Service for on demand or live-streaming, the encoder is the appropriate location for most of the compression computation to enable low client decoding time. However, emerging use-cases such as Wireless Video Sensor Networks (WVSNs) and remote surveillance present new technical challenges in compression. For these scenarios, the video capture device may be power-constrained with limited resources, and it is better that the computational burden fall on resource rich decoders/transcoders available at data centres. In this paper, we present a video coding scheme with low encoding complexity that passes the computational burden to the decoder. Bit-rate reduction is achieved by removing Chroma information from specific frames and then re-colouring them at the decoder using a technique called Motion Compensated Recolouring (MCR). MCR can reduce bit-rate by as much 16% compared to Intra-only video coding, with only a slight drop in objective quality. We also demonstrate that MCR out-performs other Chroma prediction techniques at higher quality and for videos with complex motion. © 2020 IEEE.

关键词： Image coding

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PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching

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

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

作者： Lizhi Zhang Zhiquan Lai Yu Tang Dongsheng Li Feng Liu Xiaochun Luo National Laboratory for Parallel and Distributed Processing (PDL) Computer College National University of Defence Technology Changsha China PLA News Media Center Beijing China

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 PCGraph, 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 360× performance speedup over the state-of-the-art baselines.

关键词： Adaptive systems Loading Graphics processing units Benchmark testing Data transfer Inference algorithms Graph neural networks

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international conference on computing in Engineering and Technology, ICCET 2019

International Conference on Computing in Engineering and Tec...

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international conference on computing in Engineering and Technology, ICCET 2019

ISBN: (纸本)9789813295148

The proceedings contain 84 papers. The special focus in this conference is on international conference on computing in Engineering and Technology . The topics include: Sensing Matrices in Compressed Sensing;election-Quorum-Based Coordinator Election Algorithm for distributed Systems;a Comparative Approach to Secure Data Storage Model in Hadoop Framework;learning Preferences Analysis by Case-Based Reasoning;optimal Solution for Fuzzy Assignment Problem and Applications;light Scattering Study on Protocols and Simulators Used in Automotive Application(s);An Energy-Efficient MAC Protocols for Wireless Sensor Networks;statistical Approach to Predict Road Accidents in India;Enhancing GPU Performance Using Thread Geometry Analysis for Irregular Workloads;IRNSS Constellation Optimization: A Multi-objective Genetic Algorithm Approach;Cloud of Everything (CLeT): The Next-Generation computing Paradigm;Towards Feature Selection for Detection of DDoS Attack;credit Card Fraud Detection;enhanced Strict Binary Logical Key Hierarchy Algorithm for Secure Group Communication;game Theory Based Offload and Migration-Enabled Smart Gateway for Cloud of Things in Fog computing;prioritized ViU Departure at Traffic Intersection Using Internet of Things;secure and Efficient Outsourcing of Large Scale Linear Fractional Programming;malware Detection in Android Using Machine Learning on Chip;Real-Time DDoS Detection Based on Entropy Using Hadoop Framework;Secure Request Response (SRR): A Framework to Classify Trust/Distrust Relationships in Social networking;Design and Development of Composite Clock-Based Reliable Timing Reference for IRNSS;disease Risk Prediction from Clinical Texts;bug Severity Classification Using Semantic Feature with Convolution Neural Network;design and Analysis of Sign Language Gesture Recognition as Text: A Redesigned Artificial Neural Network Approach.

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Enabling a Secured Communication in distributed IoT Using the Smart Systems Service Infrastructure

Enabling a Secured Communication in Distributed IoT Using th...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Jiahang Chen Michael Schluse Jürgen Roßmann Institute for Man-Machine Interaction RWTH Aachen University Aachen Germany

ISBN: (纸本)9781665404242;9781665447249

The Internet of Things builds a digital world where all assets including people, machines, human-machine interfaces (HMIs), processes, services, etc. are internetworked. Digital Twins provide 1-to-1 digital replica of physical assets and enable their networking and their goal-directed and standardized communication. The combination of assets and their corresponding Digital Twins are the “things”, the nodes of the IoT. With the rapid development of IoT and its pertinent technologies, more and more things will be mutually interconnected over the Internet. Hence, the security issues that come with it are gradually becoming sharper and sharper. In this paper, we focus on the topic of the secured communication among things - or more precisely their Digital Twins - and propose an approach related to OAuth2, asymmetrically encryption and digital signing to secure the communication within the newly developed Smart Systems Service Infrastructure (S³I).

关键词： Pervasive computing Digital twin conferences Machine learning Forestry Encryption Internet of Things

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