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WholeGraph: A Fast Graph Neural Network Training Framework with Multi-GPU distributed Shared Memory Architecture

WholeGraph: A Fast Graph Neural Network Training Framework w...

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Supercomputing conference

作者： Dongxu Yang Junhong Liu Jiaxing Qi Junjie Lai NVIDIA Beijing China

ISBN: (纸本)9781665454452

Graph neural networks (GNNs) are prevalent to deal with graph-structured datasets, encoding graph data into low dimensional vectors. In this paper, we present a fast training graph neural network framework, i.e., WholeGraph, based on a multi-GPU distributed shared memory architecture. Whole-Graph consists of partitioning the graph and corresponding node or edge features to multi-GPUs, eliminating the bottleneck of communication between CPU and GPUs during the training process. And the communication between different GPUs is implemented by GPUDirect Peer-to-Peer (P2P) memory access technology. Furthermore, WholeGraph provides several optimized computing operators. Our evaluations show that on large-scale graphs WholeGraph outperforms state-of-the-art GNN frameworks, such as Deep Graph Library (DGL) and Pytorch Geometric (PyG). The speedups of WholeGraph are up to $57.32\mathrm{x}$ and $242.98\mathrm{x}$ compared with DGL and PyG on a single machine multi-GPU node, respectively. The ratio of GPU utilization can sustain above 95% during GNN training process.

关键词： Training Computational modeling High performance computing Memory architecture Graphics processing units distributed databases Graph neural networks

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Deinsum: Practically I/O Optimal Multi-Linear Algebra

Deinsum: Practically I/O Optimal Multi-Linear Algebra

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Supercomputing conference

作者： Alexandros Nikolaos Ziogas Grzegorz Kwasniewski Tal Ben-Nun Timo Schneider Torsten Hoefler Department of Computer Science ETH Zurich Zurich Switzerland NextSilicon Tel Aviv Israel

ISBN: (纸本)9781665454452

Multilinear algebra kernel performance on modern massively-parallel systems is determined mainly by data movement. However, deriving data movement-optimal distributed schedules for programs with many high-dimensional inputs is a notoriously hard problem. State-of-the-art libraries rely on heuristics and often fall back to suboptimal tensor folding and BLAS calls. We present Deinsum, an automated framework for distributed multilinear algebra computations expressed in Einstein notation, based on rigorous mathematical tools to address this problem. Our framework automatically derives data movement-optimal tiling and generates corresponding distributed schedules, further optimizing the performance of local computations by increasing their arithmetic intensity. To show the benefits of our approach, we test it on two important tensor kernel classes: Matricized Tensor Times Khatri-Rao Products and Tensor Times Matrix chains. We show performance results and scaling on the Piz Daint supercomputer, with up to 19x speedup over state-of-the-art solutions on 512 nodes.

关键词： Schedules Tensors Algebra High performance computing distributed databases Supercomputers Libraries

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FedIM: An Anti-attack Federated Learning Based on Agent Importance Aggregation 20

FedIM: An Anti-attack Federated Learning Based on Agent Impo...

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20th IEEE international conference on Trust, Security and Privacy in computing and Communications (IEEE TrustCom)

作者： Gao, Zhipeng Qiu, Chenhao Zhao, Chen Yang, Yang Mo, Zijia Lin, Yijing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781665416580

Federated learning (FL) is a distributed framework for machine learning (ML) model training. Training agents upload local model parameters rather than original training data, and the central server performs parameter aggregation. FL can protect user data privacy and break the information island when training the ML model. Federated Average (FedAvg) is an aggregation method commonly used in the FL training task. The central server calculates the mean value of the local model parameters to obtain the new global parameters. FedAvg assumes that all the training agents are honest, which means the central server lacks terminal agents' knowability. When there are attackers in the training agents, the global model's performance may be deeply affected, and the training task cannot be completed normally. To solve this problem, we propose a Federated Learning method with aggregation based on the Importance of training agent (FedIM), in which the central server performs preevaluation on the agent parameters before aggregation, calculates the weights of parameters according to the historical behavior records of training terminals and performs federated aggregation to improve the anti-poisoning ability of learning task. Experiments show that our method can effectively improve the global model's anti-poisoning ability and accelerate the training speed compared with the FedAvg method when malicious agents are involved.

关键词： Federated learning Aggregation method Privacy protection

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On the Aggregated Resource Management for Satellite Edge computing

On the Aggregated Resource Management for Satellite Edge Com...

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IEEE international conference on Communications (ICC)

作者： Xu, Xiaobin Zhao, Hui Liu, Chang Fan, Cunqu Liang, Zhongjun Wang, Shangguang Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China Natl Satellite Meteorol Ctr Atmospher Remote Sensing Satellite Data Ctr Beijing Peoples R China Natl Meteorol Informat Ctr Data Serv Dept Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781728171227

Geosynchronous Earth Orbit (GEO) satellites, which can relay image data for Low Earth Orbit (LEO) satellites, play an important role in remote sensing. With the development of satellite technologies, the significantly improved computation capabilities of GEO satellites have enabled space service computing, through which GEO satellites can provide data processing services before forwarding to reduce the quantity of transmitted data. In the presence of multiple LEO satellites, how to make effective use of limited communication and computation resources in GEO satellites has become crucial. At present, the research on satellite resource management typically focuses on either communication or computation resources. Existing resource management algorithms are usually of slow convergence speed, which limits their applicability in real-time remote sensing scenarios. Therefore, we propose an aggregated resource management method for remote sensing applications. We first propose models for transmission tasks and processing tasks of remote sensing images. Then we formulate the aggregated resource management for satellite edge computing as a hybrid Stackelberg game and simplify the problem to speed up its convergence speed. Then we propose a distributed resource management algorithm to determine the optimal strategies. Simulation results show that the proposed method can quickly obtain the optimal resource allocation strategy and outperforms typical dynamic iterative algorithms in terms of service quantity and throughput.

关键词： Remote sensing mobile edge computing resource management Stackelberg game

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Accelerating DNN Inference by Edge-Cloud Collaboration

Accelerating DNN Inference by Edge-Cloud Collaboration

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40th IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Chen, Jianan Qi, Qi Wang, Jingyu Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781665443319

Deep neural networks (DNN) have become indispensable tools for intelligent applications today. The demand for deploying DNN on the edge devices increases dramatically. Unfortunately, it is challenging because the DNN inference is computation-intensive, but edge devices are always resourceconstraint. Prior solutions attempted to address these challenges with collaboration between cloud and edge devices, but they do not take the inference request rate into account. However, the inference delay will increase dramatically while the request rate becomes higher. In this paper, we propose a scheme to dynamic partition DNN into two or three parts and distribute them at the edge and cloud, achieving the lowest delay with the change of request rate. The scheme selects the optimal partition points of DNN with a layer evaluation model (LEM) and a total delay prediction model (DPM) under different request rates. The experiments of distributed deploying AlexNet, VGG, NiN and ResNet DNN models on image classification dataset ImageNet show that the proposed scheme significantly reduces the total end-to-end latency by fully using both the edge and cloud resources. It reduces the inference delay by 1.3 to 1.6 times and improves the throughput 1.2 to 1.7 times compared to the state of art partition approach.

关键词： DNN inference edge devices dynamic partition intelligent application

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Simplifying Graph-Parallel Computation in Apache Spark with GraphX

Simplifying Graph-Parallel Computation in Apache Spark with ...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Lasya Vyakaranam Ramakrishnan Raman Harshal Shah Nilamadhab Mishra R. Meenakshi S. Murugan Symbiosis Law School (SLS) Pune Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. The goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. The eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX’s performance, expanding its features, and integrating it with Spark’s ecosystem are all part of this process. The expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. This will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. The graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. The degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores.

关键词： Histograms Technological innovation Social networking (online) Scalability Data visualization Cluster computing Distance measurement Sparks Recommender systems Convergence

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Evaluating Multi-Level Checkpointing for distributed Deep Neural Network Training

Evaluating Multi-Level Checkpointing for Distributed Deep Ne...

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IEEE/ACM 7th international Workshop on Heterogeneous High- Performance Reconfigurable computing (H2RC) Part of international conference for High Performance computing, networking, Storage and Analysis (SC)

作者： Anthony, Quentin Dai, Donglai X ScaleSolut LLC Columbus OH 43221 USA Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781665483889

Deep learning (DL) applications are becoming one of the most important applications for HPC and cloud systems. The massive datasets and deep neural networks (DNN) used by DL applications introduce many HPC challenges. Therefore, HPC checkpoint/restart tools are an attractive choice. However, most data-parallel DL training jobs use a naive scheme, called root checkpointing, which is subject to blocking semantics and straggling forward progress. In this work, we apply a multi-level checkpointing tool (SCR-Exa) to distributed DL applications. We examine the performance of two DNN models at scale on Lassen (a leading TOP500 system), while ensuring the DNN's accuracy is maintained after restart from simulated system failures. Our test results show that multi-level checkpointing schemes are able to achieve nearly constant overhead at scale. To the best our knowledge, this study presents the first evaluation to demonstrate strong scalability of a checkpointing scheme for distributed DL without making framework-specific changes.

关键词： Deep Learning Neural Networks Faulttolerance Checkpointing High Performance computing Performance Characterization MVAPICH2 MPI PyTorch Horovod

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Artificial Intelligence: Trends and Challenges

Artificial Intelligence: Trends and Challenges

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international conference on Parallel distributed and Grid computing (PDGC)

作者： Av. Karthick S. Gopalsamy Department of Computational Logistics Alagappa University Karaikudi India Department of International Business Alagappa University Karaikudi India

ISBN: (纸本)9781665454025

Artificial Intelligence is an interdisciplinary field that combines Machine Learning, Big Data, Cloud computing, and Information Theory. The major advantage of machine learning is that it builds on prior experience, so machines are self-learning and can predict the future without error. It is a kind of decision-making approach that is applied in various domains like social media, health care, finance, etc. In Big Data and Cloud computing, the volume of data is sustainable and increasing, which is processed with low power consumption with the help of machine learning. It is possible to support the next generation of smart grid by providing the platform that integrates, in addition to the communication infrastructure, the AI and IoT support, providing a multitenant system, thanks to the future Massive Internet of Things (MIoT), one of the pillars of the 5G/6G network factory. Rapid development in Deep Learning, Artificial Intelligence (AI) and Machine Learning (ML) will give better results for future applications. This paper focuses on the trends and challenges related to AI is discussed.

关键词： Cloud computing Statistical analysis Social networking (online) Decision making Big Data Market research Real-time systems

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Analysis of Machine Learning for Securing Software-Defined networking 18

Analysis of Machine Learning for Securing Software-Defined N...

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18th international Learning and Technology conference, L and T 2021

作者： Alamri, Hassan A. Thayananthan, Vijey Faculty of Computing IT King Abdulaziz University Jeddah21589 Saudi Arabia College of Computing in Al-Qunfudhah Umm Al-Qura University Makkah21955 Saudi Arabia

Advancements in Software-Defined networking (SDN) are growing with the emerging network architectures which face the security issues and challenges when adversary launches severe attacks such as distributed Denial of Service (DDoS) attacks. Therefore, a considerable number of solutions have been devised to alleviate DDoS attacks in SDN using a machine learning approach. Thus, the aim of this study is to review and analyze the machine learning-based schemes for securing the SDN environment targeted by DDoS attacks. The schemes' method, performance metrics, datasets, and other remarks such as benchmarks, strengths, and weaknesses are discussed. The CIC-DDoS 2019 dataset was utilized to evaluate the performance of a set of classification algorithms that are widely used in machine learning-based DDoS attack detection in the SDN environment. Finally, challenges and future directions in the development of machine learning-based detection schemes in SDN are discussed. © 2021 The Authors. Published by Elsevier B.V.

关键词： Software defined networking

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Performance prediction for distributed graph computing

Performance prediction for distributed graph computing

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作者： Ji, Shuo Zhao, Yinliang Li, Yuxiang School of Information Science and Technology Northwest University Xi'an China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Information Engineering Henan University of Science and Technology Luoyang China

Performance prediction for executing graph applications on distributed systems is a prerequisite to improve system performance. Especially for distributed systems optimized by sacrificing the accuracy of results to improve runtime performance, performance prediction can be used to determine accuracy-related system parameters to achieve a tradeoff between the runtime and inaccuracy of results. This article presents an approach that predicts the runtime and inaccuracy of executing graph algorithms on bulk synchronous parallel (BSP)-based systems to optimize system parameters by using an artificial neural network (ANN). The proposed approach samples different scales of subgraphs from the input graph by maintaining that the features of subgraphs are similar to the features of the input graph. Then it executes graph algorithm on each subgraph and extracts their runtime features. An ANN-based performance prediction model is trained off-line based on the extracted features and is used to predict the performance of executing graph algorithm on the complete input graph. We have validated the proposed approach by conducting single-source shortest path, connected component, and PageRank on the BSP-based distributed systems. The experimental results demonstrate that the prediction method can effectively predict the runtime and the inaccuracy with a relative error rate under 14% and under 25%, respectively, compared with the actual performance results. © 2020 John Wiley & Sons Ltd.

关键词： Forecasting

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