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3D Coded SUMMA: Communication-Efficient and Robust parallel Matrix Multiplication 26th

3D Coded SUMMA: Communication-Efficient and Robust Parallel ...

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26th International conference on parallel and distributed Computing (Euro-Par)

作者： Jeong, Haewon Yang, Yaoqing Gupta, Vipul Engelmann, Christian Low, Tze Meng Cadambe, Viveck Ramchandran, Kannan Grover, Pulkit Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Berkeley Berkeley CA USA Oak Ridge Natl Lab Oak Ridge TN USA Penn State Univ State Coll PA USA

ISBN: (纸本)9783030576752;9783030576745

In this paper, we propose a novel fault-tolerant parallel matrix multiplication algorithm called 3D Coded SUMMA that achieves higher failure-tolerance than replication-based schemes for the same amount of redundancy. This work bridges the gap between recent developments in coded computing and fault-tolerance in high-performance computing (HPC). The core idea of coded computing is the same as algorithm-based fault-tolerance (ABFT), which is weaving redundancy in the computation using error-correcting codes. In particular, we show that MatDot codes, an innovative code construction for parallel matrix multiplications, can be integrated into three-dimensional SUMMA (Scalable Universal Matrix Multiplication Algorithm [30]) in a communication-avoiding manner. To tolerate any two node failures, the proposed 3D Coded SUMMA requires similar to 50% less redundancy than replication, while the overhead in execution time is only about 5-10%.

关键词： parallel matrix multiplication Fault-tolerant algorithms Algorithm-based fault tolerance Coded computing Communication-efficient algorithms Error detection and correction

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Prompt: Dynamic Data-Partitioning for distributed Micro-batch Stream processing Systems 20

Prompt: Dynamic Data-Partitioning for Distributed Micro-batc...

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ACM SIGMOD International conference on Management of Data (SIGMOD)

作者： Abdelhamid, Ahmed S. Mahmood, Ahmed R. Daghistani, Anas Aref, Walid G. Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781450367356

Advances in real-world applications require high-throughput processing over large data streams. Micro-batching has been proposed to support the needs of these applications. In micro-batching, the processing and batching of the data are interleaved, where the incoming data tuples are first buffered as data blocks, and then are processed collectively using parallel function constructs (e.g., Map-Reduce). The size of a micro-batch is set to guarantee a certain response-time latency that is to conform to the application's service-level agreement. In contrast to tuple-at-a-time data stream processing, micro-batching has the potential to sustain higher data rates. However, existing micro-batch stream processing systems use basic data-partitioning techniques that do not account for data skew and variable data rates. Load-awareness is necessary to maintain performance and to enhance resource utilization. A new data partitioning scheme, termed Prompt is presented that leverages the characteristics of the micro-batch processing model. In the batching phase, a frequency-aware buffering mechanism is introduced that progressively maintains run-time statistics, and provides online key-based sorting as data tuples arrive. Because achieving optimal data partitioning is NP-Hard in this context, a workload-aware greedy algorithm is introduced that partitions the buffered data tuples efficiently for the Map stage. In the processing phase, a load-aware distribution mechanism is presented that balances the size of the input to the Reduce stage without incurring inter-task communication overhead. Moreover, Prompt elastically adapts resource consumption according to workload changes. Experimental results using real and synthetic data sets demonstrate that Prompt is robust against fluctuations in data distribution and arrival rates. Furthermore, Prompt achieves up to 200% improvement in system throughput over state-of-the-art techniques without degradation in latency.

关键词： distributed Data processing Micro-batch Stream processing Data Partitioning Elastic Stream processing

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parallelization of Variable Rate Decompression through Metadata

Parallelization of Variable Rate Decompression through Metad...

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euromicro conference on parallel, distributed and network-based processing

作者： Lennart Noordsij Steven van der Vlugt Mohamed A. Bamakhrama Zaid Al-Ars Peter Lindstrom ASML Netherlands Synopsys Corporation Netherlands TU Delft Netherlands Lawrence Livermore National Laboratory United States of America

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Data movement has long been identified as the biggest challenge facing modern computer systems' designers. To tackle this challenge, many novel data compression algorithms have been developed. Often variable rate compression algorithms are favored over fixed rate. However, variable rate decompression is difficult to parallelize. Most existing algorithms adopt a single parallelization strategy suited for a particular HW platform. Such an approach fails to harness the parallelism found in diverse modern HW architectures. We propose a parallelization method for tiled variable rate compression algorithms that consists of multiple strategies that can be applied interchangeably. This allows an algorithm to apply the strategy most suitable for a specific HW platform. Our strategies are based on generating metadata during encoding, which is used to parallelize the decoding process. To demonstrate the effectiveness of our strategies, we implement them in a state-of-the-art compression algorithm called ZFP. We show that the strategies suited for multicore CPUs are different from the ones suited for GPUs. On a CPU, we achieve a near optimal decoding speedup and an overhead size which is consistently less than 0.04% of the compressed data size. On a GPU, we achieve average decoding rates of up to 100 GiB/s. Our strategies allow the user to make a trade-off between decoding throughput and metadata size overhead.

关键词： Decoding Metadata Compression algorithms Encoding Data transfer Acceleration Multicore processing

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A Large-Scale parallel network Intrusion Detection Model based on K-Means in Security Audit System 6th

A Large-Scale Parallel Network Intrusion Detection Model Bas...

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6th International conference on Artificial Intelligence and Security,ICAIS 2020

作者： Qiao, Xueming Zhang, Yuan Liu, Yanhong Hu, Hao Zhu, Dongjie Qiu, Zhi Liu, Chenglin State Grid Weihai Power Supply Company Weihai China School of Computer Science and Technology Harbin Institute of Technology Weihai China State Grid Rushan County Electric Power Supply Company Weihai China

ISBN: (纸本)9789811580857

With the gradual entry of 5G communication technology into the commercial stage, the era of the Internet of Thing is coming, bringing great convenience to people’s lives. However, there are potential threats to security audit data interaction in the IoT environment. Traditional network intrusion detection methods cannot meet the requirements of parallel processing of massive data. Therefore, network intrusion detection should consider parallelizing data processing and predicting abnormal behavior ahead. In this paper, we propose a large-scale parallel network intrusion detection method based on k-means. Firstly, the Isolation Forest algorithm is used to filter and segment the sample data;Secondly, the clusters is obtained by the k-means algorithm in a distributed manner;Finally, the generated cluster is weighted and voted for abnormality determination. The experimental results of KDDCUP dataset shows that the algorithm improves the recognition accuracy and speeds up the training time and prediction time compared with the traditional k-means algorithm. © 2020, Springer Nature Singapore Pte Ltd.

关键词： network security

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Efficient. Scalable and Robust Data Shuffle Service for distributed MapReduce Computing on Cloud

Efficient. Scalable and Robust Data Shuffle Service for Dist...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Rong Gu Xu Huang Haipeng Dai Xiaoyu Geng Xiaofei Chen Yihua Huang Fu Xiao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement.

关键词： Machine learning parallel processing Big Data Data models Stability analysis Sparks Task analysis

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Fog Computing based Traffic and Car Parking Intelligent System 19th

Fog Computing Based Traffic and Car Parking Intelligent Syst...

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19th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Alajali, Walaa Gao, Shang Alhusaynat, Abdulrahman D. Deakin Univ Melbourne Vic 3217 Australia Thi Qar Univ Nasiriyah 64001 Iraq

ISBN: (纸本)9783030389611;9783030389604

Internet of Things (IoT) has attracted the attention of researchers from both industry and academia. Smart city, as one of the IoT applications, includes several sub-applications, such as intelligent transportation system (ITS), smart car parking and smart grid. Focusing on traffic flow management and car parking systems because of their correlation, this paper aims to provide a framework solution to both systems using online detection and prediction based on fog computing. Online event detection plays a vital role in traffic flow management, as circumstances, such as social events and congestion resulting from accidents and roadworks, affect traffic flow and parking availability. We developed an online prediction model using an incremental decision tree and distributed the prediction process on fog nodes at each intersection traffic light responsible for a connecting road. It effectively reduces the load on the communication network, as the data is processed, and the decision is made locally, with low storage requirements. The spatially correlated fog nodes can communicate if necessary to take action for an emergency. The experiments were conducted using the Melbourne city open data.

关键词： Traffic flow Prediction ITS Smart car parking

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Multiprecision Block-Jacobi for Iterative Triangular Solves 26th

Multiprecision Block-Jacobi for Iterative Triangular Solves

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26th International conference on parallel and distributed Computing (Euro-Par)

作者： Goebel, Fritz Anzt, Hartwig Cojean, Terry Flegar, Goran Quintana-Orti, Enrique S. Karlsruhe Inst Technol Steinbuch Ctr Comp Karlsruhe Germany Univ Tennessee Innovat Comp Lab ICL Knoxville TN USA Univ Jaume 1 Dept Ingn & Ciencia Comp Castellon de La Plana Spain Univ Politecn Valencia Dept Informat Sistemas & Comp Valencia Spain

ISBN: (纸本)9783030576752;9783030576745

Recent research efforts have shown that Jacobi and blockJacobi relaxation methods can be used as an effective and highly parallel approach for the solution of sparse triangular linear systems arising in the application of ILU-type preconditioners. Simultaneously, a few independent works have focused on designing efficient high performance adaptive-precision block-Jacobi preconditioning (block-diagonal scaling), in the context of the iterative solution of sparse linear systems, on manycore architectures. In this paper, we bridge the gap between relaxation methods based on regular splittings and preconditioners by demonstrating that iterative refinement can be leveraged to construct a relaxation method from the preconditioner. In addition, we exploit this insight to construct a highly-efficient sparse triangular system solver for graphics processors that combines iterative refinement with the block-Jacobi preconditioner available in the Ginkgo library.

关键词： Sparse linear algebra Incomplete factorization preconditioning Graphics processing units (GPUs) Multiprecision Block-Jacobi

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Attention-based Encoder-Decoder Recurrent Neural networks for HTTP Payload Anomaly Detection

Attention-based Encoder-Decoder Recurrent Neural Networks fo...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Shang Wu Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Attack payloads are often short segments hidden in HTTP requests; thus they can be found by HTTP payload anomaly detection. Deep learning models learn data features during training without manual feature extraction, and better performance has received more attention. Recurrent Neural network models process sequences directly, which are widely used in payload anomaly detection. However, due to the gradient vanishing problem, RNN has limits on processing the long sequences. Meanwhile, RNN uses its final hidden state for detection and pays more attention to the content of the end of the payload. Besides, deep learning generally lacks interpretability. The paper proposes an unsupervised deep learning model for HTTP payload Anomaly Detection, namely Attention-based Encoder-Decoder Recurrent Neural networks Anomaly Detection model (AEDRAD). AEDRAD utilizes the encoder-decoder RNN and attention mechanism to detect anomalies by reconstructing the original sequences. AEDRAD filters the fields of HTTP protocol that cannot exist anomalies, focusing on the suspicious segments. Through the encoder-decoder network, the normal payload can be well-reconstructed while the anomaly payload fails. With the attention mechanism, AEDRAD generates practical features for further anomaly detection from a global perspective. Meanwhile, it marks abnormal fragments visually, which is conducive to a subsequent analysis by experts. The experimental results show that AEDRAD significantly outperforms three state-of-the-art unsupervised algorithms on two real datasets.

关键词： Deep learning Training Recurrent neural networks Protocols Focusing Manuals Feature extraction

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Relation Prediction of N-ary Knowledge Graph based on Multi-information Propagation

Relation Prediction of N-ary Knowledge Graph Based on Multi-...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Peijie Wang Jianrui Chen Luheng Yang Julong Li School of Comp. Sci. Shaanxi Normal University Xi’an China

Knowledge Graph (KG) is currently the most popular graph structure. For graphs, however, they are frequently incomplete due to the lack of most information in the real world. Knowledge Graph Completion (KGC) has become the most interesting topic for researchers. At the same time, the relationships in social networks have expanded from binary relationships to more complex high-order relationships. However, the current research methods on multi-relationship still lack the interaction between entities, and the corresponding entity information under each relationship is different. based on this, we propose a high-order GCN-based multi-relation prediction model (denoted as MHGCN). Firstly, an entity adjacent matrix is constructed for each relation, and a high-order graph convolutional network (GCN) is used to propagate the neighbor information among entities within the relation. Secondly, a probabilistic calculation method that integrates entity information is designed to judge the existence of facts. Experimental analysis is carried out on three representative datasets, which illustrate the effectiveness of our proposed algorithm. In particular, for FB-AUTO dataset with unfixed number of entities, the MRR of MHGCN reaches 0.883. The MRR of the fixed entity number dataset JF17K-4 is up to 0.828. It further shows that MHGCN is not only applicable to datasets with unfixed number of entities, but also applicable to datasets with fixed number of entities.

关键词： Knowledge engineering Social networking (online) Knowledge graphs Predictive models Probability Ubiquitous computing Probabilistic logic

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Chinese Entity Synonym Extraction from the Web

Chinese Entity Synonym Extraction from the Web

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International conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Ma, Xiuxia Luo, Xiangfeng Huang, Subin Guo, Yike Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Imperial Coll London USA Dept Comp London England

ISBN: (纸本)9783030152352;9783030152345

Entity synonyms play an important role in natural language processing applications, such as query expansion and question answering. There are three main distribution characteristics in texts on the web: (1) appearing in parallel structures;(2) occurring with specific patterns in sentences;and (3) distributed in similar contexts. These characteristics are largely complementary. Existing methods, such as pattern-based and context-based methods, only consider one characteristic for synonym extraction and ignore the complementarity among them. For increasing accuracy and recall, we propose a novel method that integrates the three characteristics for extracting synonyms from the web, where Entity Synonym network (ESN) is built to incorporate synonymous knowledge. To further improve accuracy, we treat synonym detection as a ranking problem and use the Spreading Activation model as a ranking means to detect the hard noise in ESN. Experimental results show our method achieves better accuracy and recall than the state-of-the-art methods.

关键词： Synonym extraction Entity Synonym network Spreading activation

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