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international conference on Computer and applications (ICCA)

作者： Heba Mohammed Fadhil Zainab Abdulnasser Shahad Mohammed Department of Information and Communication Al-Khwarizmi College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9781665452953

People's reliance on computers and the computing power they provide is growing by the minute. An ever-increasing amount of data is being created each day, and the power to analyze this data requires the use of cluster computers to process and calculate data. It has been discovered that data clustering is a beneficial data mining approach. There have been a number of recent attempts to cluster data mining methods. Using a Raspberry Pi cluster, this study employs the Apriori algorithm, which is the most generally used algorithm, to extract frequent itemsets from large data sets. The fundamental aim is to build a cluster and provide data analysis capabilities based on an examination of the major clustering phases in order to illustrate the power of cluster computing and the applications of data analytics. Each Raspberry Pi uses the MPI standard and Python multiprocessing to share a large task and then coordinate their findings among a group of four or more MPICH systems at the conclusion of the processing. At the data partitioning stage, the issue of load balancing must be taken into account. According to our testing results, clustering accelerates sequential classification by a factor of 10. There is a noticeable increase in performance when there are additional processors installed. Additionally, we discovered that item count had a bigger effect on clustering performance than transaction count.

关键词： Clustering algorithms Cluster computing Life estimation parallel processing Load management Supercomputers Data mining

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Bucket MapReduce: Relieving the Disk I/O Intensity of Data-Intensive applications in MapReduce Frameworks

Bucket MapReduce: Relieving the Disk I/O Intensity of Data-I...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Kai-Hsun Chen Hsin-Yuan Chen Chien-Min Wang Academia Sinica Institute of Information Science Taipei Taiwan

Hadoop MapReduce is a software framework for processing vast amounts of data in parallel on large clusters. As data size increases, a need arises to resolve the significant increase in the disk I/O of reducer nodes, which may cause runtime bottlenecks. In response, we propose Bucket MapReduce, a system that utilizes bucket sort and pipeline parallelism to improve the performance of Hadoop MapReduce. Several parameters may influence the performance in Bucket MapReduce, and thus we will further discuss parameter tuning. In this paper, we perform experiments on TeraSort benchmark. Bucket MapReduce successfully reduces local disk I/O by 61% and improves the runtime by 1. 39× in 800GB TeraSort benchmark.

关键词： Runtime Pipelines parallel processing Benchmark testing Software Tuning

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EQueue: Elastic Lock-Free FIFO Queue for Core-to-Core Communication on Multi-Core Processors

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IEEE ACCESS 2020年 8卷 98729-98741页

作者： Wang, Junchang Tian, Yangfeng Fu, Xiong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210003 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

In recent years, the number of CPU cores in a multi-core processor keeps increasing. To leverage the increasing hardware resource, programmers need to develop parallelized software programs. One promising approach to parallelizing high-performance applications is pipeline parallelism, which divides a task into a serial of subtasks and then maps these subtasks to a group of CPU cores, making the communication scheme between the subtasks running on different cores a critical component for the parallelized programs. One widely-used implementation of the communication scheme is software-based, lock-free first-in-first-out queues that move data between different subtasks. The primary design goal of the prior lock-free queues was higher throughput, such that the technique of batching data was heavily used in their enqueue and dequeue operations. Unfortunately, a lock-free queue with batching heavily depends on the assumption that data arrive at a constant rate, and the queue is in an equilibrium state. Experimentally we found that the equilibrium state of a queue rarely happens in real, high-performance use cases (e.g., 10Gbps & x002B;network applications) because data arriving rate fluctuates sharply. As a result, existing queues suffer from performance degradation when used in real applications on multi-core processors. In this paper, we present EQueue, a lock-free queue to handle this robustness issue in existing queues. EQueue is lock-free, efficient, and robust. EQueue can adaptively (1) shrink its queue size when data arriving rate is low to keep its memory footprint small to utilize CPU cache better, and (2) enlarge its queue size to avoid overflow when data arriving rate is in burstiness. Experimental results show that when used in high-performance applications, EQueue can always perform an enqueue/dequeue operation in less than 50 CPU cycles, which outperforms FastForward and MCRingBuffer, two state-of-the-art queues, by factors 3 and 2, respectively.

关键词： parallel processing Task analysis Multicore processing Pipelines Robustness Throughput Licenses Lock-free queue pipeline parallelism multi-core processors

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GreenBFS: Space-Efficient BFS Engine for Power-aware Graph processing

GreenBFS: Space-Efficient BFS Engine for Power-aware Graph P...

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

作者： Xinbiao Gan Peilin Guo Guang Wu Tiejun Li National University of Defense Technology

BFS (Breadth-First Search) is a promising killer engine for graph processing and currently has an extremely important role in real-time processing scenarios in everyday life. Unfortunately, the current BFS engine often delivers poor efficiency owing to complex graph representation, and high energy/memory cost. This paper presents GreenBFS, a space-efficient BFS engine for power-aware graph processing. GreenBFS (i) adopts Folding@CSR, a novel compressed sparse row(CSR) format based on folding edges for sorted CSR. Folding@CSR can store graph data using 50% smaller memory compared to the state-of-the-art graph compression; and (ii) proposes a mixed-conservation mode based on hardware and software integration that can attain power conservation by 35.7%. We use both benchmark and real-world graphs to demonstrate the effectiveness of GreenBFS. Firstly, GreenGraph500, a widely accepted benchmark to rank graph processing capability with energy, is used to validate GreenBFS. GreenBFS-based Tianhe Exa-node has won both No.1 in the latest GreenGraph500 ranking (June, 2022) in big data and small data categories. We finally apply GreenBFS to construct graphs, which is much more efficient on memory footprint than that of the state-of-the-art graph representation with lower electric supply.

关键词： Costs Green buildings Memory management Big Data Benchmark testing Software Real-time systems

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High-Performance parallel Fault Simulation for Multi-Core Systems

High-Performance Parallel Fault Simulation for Multi-Core Sy...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Masoomeh Karami Mohammad-hashem Haghbayan Masoumeh Ebrahimi Hamid Nejatollahi Hannu Tenhunen Juha Plosila University of Turku Turku Finland Royal Institute of Technology (KTH) Kista Sweden. University of California Irvine CA USA.

Fault simulation is a time-consuming process that requires customized methods and techniques to accelerate it. Multi-threading and Multi-core approaches are two promising techniques that can be exploited to accelerate the fault simulation process by using different parts of the hardware at the same time. However, an efficient parallelization is obtained only by the refinement of software with respect to the hardware platform. In this paper, a parallel multi-thread fault simulation technique is proposed to accelerate the simulation process on multi-core platforms. In this approach, the gate input values are independently assigned to each thread. Each input value carries the information of several parallel simulation processes. This provides a multithread parallel fault simulation environment. The experimental results show that the proposed technique can efficiently use the hardware platform. In a single-core platform, the proposed technique can reduce the time by 25% while in a dual-core increasing the thread approximately halves the execution time.

关键词： Multicore processing Instruction sets Very large scale integration Logic gates Hardware Software Circuit faults

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A Fast Robustness Quantification Method for Evaluating Typical Deep Learning Models by Generally Image processing 18

A Fast Robustness Quantification Method for Evaluating Typic...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Li, Haocong Cheng, Yunjia Ren, Wei Zhu, Tianqing China Univ Geosci Sch Comp Sci Wuhan Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Chinese Acad Sci CAS Inst Informat Engn Key Lab Network Assessment Technol Beijing 100093 Peoples R China

ISBN: (纸本)9781665414852

Deep learning especially image recognition techniques have been extensively used in various applications such as unmanned driving. The robustness of deep learning models is of critical importance since fault image recognition results may result in serious incidents. In this paper, we propose a fast quantifying method by general image processing to evaluate the robustness of five typical deep learning models under the Keras framework (i.e., VGG16, InceptionV3, ResNet50, DenseNet, and MobileNet). We analyze six metrics in terms of accuracy, precision, recall, F1, recognition time, and impact factor. The evaluation data is publicly accessible image data sets from Kaggle. In our evaluation, the adversary samples are generated by generally image processing methods such as gray-scaling, color-reversing, and image-flipping, which is ordinary operations and easily launched. The different models over various image processing methods are evaluated and compared comprehensively. The evaluation results show that DenseNet performs best over three conditions such as baseline, gray-scaling and horizontal flipping. MobileNet costs the shortest delay in decision over all image processing methods. F1 score varies with different attack intensity. InceptionV3 presents overall robustness in most conditions.

关键词： robustness model evaluation deep learning image recognition

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Intrusion Detection for Marine Meteorological Sensor Network

Intrusion Detection for Marine Meteorological Sensor Network

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

作者： Xin Su Guifu Zhang Minxue Zhang Baoliu Ye Hongyan Xing College of IOT Engineering Hohai University Changzhou Jiangsu China Information Department Hohai University Nanjing Jiangsu China Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation Nanjing University of Information Science and Technology Nanjing China Jiangsu Key Laboratory of Meteorological Detection and Information Processing Nanjing University of Information Science and Technology Nanjing China

The rapid development of intelligent devices and wireless communication has greatly promoted the intelligence of the maritime Internet of Things (MIoT) in recent years, and the marine meteorological sensor network (MMSN) is an essential part of MIoT, providing accurate meteorological data for MIoT services. However, the frequent information interaction between the device and the external world also causes information leakage risks for MMSN. Intrusion detection technology based on deep learning is an effective defensive mechanism. Thus, we propose a lightweight one-dimensional convolutional neural network (LCNN) intrusion detection model to adapt to the MMSN environment characteristics and device heterogeneity. In addition, an improved supervised adversarial autoencoder (ISAAE) is proposed for synthesizing samples to address the problem that minority class attack samples are difficult to collect and detect. Finally, the simulation experimental results on the NSL-KDD dataset verify the effectiveness of the proposed method.

关键词： Training Wireless communication Adaptation models Convolution Computational modeling Simulation Intrusion detection

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Perturbation of Image-based Malware Detection with Smali level morphing techniques

Perturbation of Image-based Malware Detection with Smali lev...

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

作者： Federico Gerardi Giacomo Iadarola Fabio Martinelli Antonella Santone Francesco Mercaldo Geckosoft Pisa Italy Institute of Informatics and Telematics National Research Council of Italy (CNR) Pisa Italy University of Molise Campobasso Italy

Considering the weaknesses of signature-based approaches adopted by current antimalware, from both academic and industrial side there is a boost in the development of techniques exploiting artificial intelligence, where one of the most promising are based on the representation of application under analysis as image. In order to understand whether these approaches can be effectively adopted in the real-world, starting from a detector based on deep learning, in this paper we evaluate the resilience of these approaches when morphed samples are considered. We present DexWave, a tool aimed to automatically inject perturbations techniques targeting the smali code representation of Android applications. The experimental analysis demonstrate that image-based malware classifier are vulnerable to simple perturbations attack.

关键词： Deep learning Codes Perturbation methods Detectors Malware Artificial intelligence Resilience

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SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Distribution Structure

SMVAR: A Novel RNN Accelerator Based on Non-blocking Data Di...

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

作者： Jinwei Xu Jingfei Jiang Shiyao Xu Lei Gao Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha Hunan China

Recurrent neural networks (RNNs) have become common models in the field of artificial intelligence to process temporal sequence task, such as speech recognition, text analysis, natural language processing, etc. To speedup RNNs inference, previous research proposed model sparse pruning techniques. However, the pruning rate of existing sparse pruning algorithms will be affected by pruning granularity and hardware friendliness. In order to approximate nonstructured pruning algorithm, this paper proposes Large Region Balanced Sparse (LRBS) pruning method, which does not limit sub-matrix shape and effectively improves pruning rate. Furthermore, we propose Sparse Matrix Vector Multiplication Accelerator for RNNs (SMVAR), which adopt non-blocking data distribution structure to solve the problem of efficient execution of large region irreg-ular matrix multiplication. To further improve the accelerator performance, SMVAR fine-grained adjusts the pipeline between macro-operations to reduce the idle of compute components. In addition, according to the coarse-grained block characteristics of LRBS algorithm, we develop the coarse-grained parallelism of accelerator with multiply compute units(CUs) structure. Experiments show that the pruning rate of our proposed LRBS is 1.25x-2.5x higher than that of the existing pruning algorithms. Compared with the existing work, the execution efficiency is improved by more than 2.02x-35.9x in the same application scenario.

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Rhino: Efficient Management of Very Large distributed State for Stream processing Engines 20

Rhino: Efficient Management of Very Large Distributed State ...

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ACM SIGMOD international conference on Management of Data (SIGMOD)

作者： Del Monte, Bonaventura Zeuch, Steffen Rabl, Tilmann Markl, Volker Tech Univ Berlin Berlin Germany DFKI GmbH Kaiserslautern Germany Univ Potsdam HPI Potsdam Germany

ISBN: (纸本)9781450367356

Scale-out stream processing engines (SPEs) are powering large big data applications on high velocity data streams. Industrial setups require SPEs to sustain outages, varying data rates, and low-latency processing. SPEs need to transparently reconfigure stateful queries during runtime. However, state-of-the-art SPEs are not ready yet to handle on-the-fly reconfigurations of queries with terabytes of state due to three problems. These are network overhead for state migration, consistency, and overhead on data processing. In this paper, we propose Rhino, a library for efficient reconfigurations of running queries in the presence of very large distributed state. Rhino provides a handover protocol and a state migration protocol to consistently and efficiently migrate stream processing among servers. Overall, our evaluation shows that Rhino scales with state sizes of up to TBs, reconfigures a running query 15 times faster than the state-of-the-art, and reduces latency by three orders of magnitude upon a reconfiguration.

关键词： stateful stream processing distributed and parallel databases

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