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Steal-A-GC: Framework to Trigger GC during Idle Periods in distributed systems

Steal-A-GC: Framework to Trigger GC during Idle Periods in D...

引用

international conference on High Performance computing

作者： Sujoy Saraswati Soumitra Chatterjee Ranganath Ramachandra Hewlett Packard Enterprise Bangalore India

ISBN: (纸本)9781509054121

distributed applications for data analytics have emerged to be a significant growth domain both in terms of technology as well as business relevance. With the advent of Hadoop MapReduce, several distributed execution frameworks have been developed for data analytics and machine learning applications. Many of these frameworks are written in Java, Scala or Python and run on top of the Java Virtual Machine (JVM), leaving them vulnerable to the performance impact of garbage collection (GC) operations. Application pauses induced by the Java GC are notorious in being one of the most important contributors of runtime performance bottleneck for a single process execution. These pauses are present in the distributed scenario as well, though in this case, it is significantly more complex to measure the impact of a GC pause for the overall execution runtime. Typically, in a distributed workload, a GC can result in delayed tasks impacting the overall completion time of the application. We present herein a novel way to preemptively trigger Java GC, stealing idle cycles, so as to prevent future tasks from being impacted by a GC pause during peak execution periods, resulting in improved overall application throughput.

关键词： Sparks Java Runtime distributed databases Throughput Data analysis Business

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#ChennaiFloods: Leveraging Human and Machine Learning for Crisis Mapping during Disasters Using Social Media

#ChennaiFloods: Leveraging Human and Machine Learning for Cr...

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IEEE international conference on High Performance computing workshops (HiPCW)

作者： Bhuvaneswari Anbalagan C. Valliyammai Department of Computer Technology MIT Anna University Chennai India

ISBN: (纸本)9781509057740

The recent emergence of ubiquitous smart communication devices accelerate people to post the current trending topics in real time as micro blogs, tweets, posts and multimedia content on social media sites along with geographical location tags (geo-tags). Specifically, during recent floods in Tamilnadu 2015, the early warnings about flooded areas emerged to get posted in popular social media with geo-parsed hash tags continuously. In the humanitarian view, the real-time crisis sparked great interest in designing an innovative methodology using big social media data analysis along with supervised machine learning techniques to actuate immediate disaster response and rescue efforts in near future. The proposed system performs disaster tweet collection based on trending disaster hash tags. Our system performs Naive-Bayesian (multinomial) and SSVM classification on collected tweets to identify the severity of the disaster. Based on location-to-interpolation cluster proximity, disaster geographic map is generated for the affected area. Our approach detects the tweets fitted into correct classifier label, and generate an output with detection rate of 79% to 91% of the time. The predicted disaster mapping results are highly accurate up to 89% for real time geo-parsed tweets that matched with actual location at-risk during the flood.

关键词： Twitter Floods Earthquakes Real-time systems Support vector machines Computational modeling

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An Alternative Approach of the SPIKE Preconditioner for Finite Element Analysis

An Alternative Approach of the SPIKE Preconditioner for Fini...

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international conference on High Performance computing

作者： Leonardo Muniz De Lima Brenno Albino Lugon Lucia Catabriga Universidade Federal do Espirito Santo Vitoria ES BR Universidade Federal do Esp??rito Santo Vitoria Brazil

ISBN: (纸本)9781509054121

This paper describes an alternative approach to using the SPIKE preconditioner in the context of finite element analysis. Commonly, SPIKE operations evaluate finite element matrices in sequential form with the resulting matrices distributed in parallel partitions. The goal is to obtain these matrices literally in parallel, avoiding excessive communication at the beginning of the process. This study offers a comparison of two SPIKE versions. One version considers a domain decomposition of the mesh as a preprocess for the finite element calculations. The other version considers a domain decomposition of the resulting linear system. With numerical experiments, this paper shows that the first version demonstrates advantages related to CPU time and memory usage.

关键词： Linear systems Program processors Sparse matrices Finite element analysis Matrix decomposition Iterative methods Partitioning algorithms

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Dynamic Data Layout Optimization for High Performance Parallel I/O

Dynamic Data Layout Optimization for High Performance Parall...

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international conference on High Performance computing

作者： Everett Neil Rush Bryan Harris Nihat Altiparmak Ali Şaman Tosun Department of Computer Eng. & Computer Science University of Louisville KY Department of Computer Science University of Texas at San Antonio TX

Storage performance bottlenecks are one of the major threats limiting the scalability of I/O intensive applications. Parallel storage systems have the potential to alleviate I/O bottlenecks through concurrent operation of independent storage components if a parallelism-aware data layout can be continuously guaranteed. Existing systems use one-layout-fits-all data placement strategy that frequently results in sub-optimal I/O parallelism. Guided by association rule mining, graph coloring, bin packing, and network flow techniques, this paper proposes a general framework for self-optimizing parallel storage systems, with the goal of continuously providing a high-degree of I/O parallelism that is robust to changes in the parallel access patterns of applications and the coexistence of applications with different parallel access characteristics. Evaluation results indicate that the proposed framework is highly successful in adjusting to skewed parallel access patterns for both traditional hard disk drive (HDD) based storage arrays and solid-state drive (SSD) based all-flash arrays. In addition to the storage arrays, the proposed framework is sufficiently generic to be tailored to various other parallel storage scenarios including but not limited to key-value stores, parallel/distributed file systems, and internal parallelism of SSDs.

关键词： Correlation Layout Parallel processing Monitoring Optimization Data mining distributed databases

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Read Consistency in distributed Database Based on DMVCC

Read Consistency in Distributed Database Based on DMVCC

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international conference on High Performance computing

作者： Jie Shao Boxue Yin Bujiao Chen Guangshu Wang Lin Yang Jianliang Yan Jianying Wang Weidong Liu Baidu Inc Tsinghua University

ISBN: (纸本)9781509054121

In a traditional distributed database system, the partitions use two-phase locking (2PL) as the concurrency control protocol to ensure distributed read consistency. But the read-lock acquired by a read operation is incompatible with a write-lock, which undermines the performance of the system. While in a system at the snapshot isolation level, where partitions use Multi-Version Concurrent Control (MVCC) as the concurrent control protocol, distributed read inconsistency may occur. To achieve read consistency and guarantee the performance at the same time, we propose distributed Multi-Version Concurrent Control (DMVCC). With DMVCC, the system can support snapshot reads, which do not block write operations, and ensure distributed read consistency. In this protocol, a transaction obtains a set of consistent snapshot version numbers at the startup time. The transaction then uses those numbers to read the corresponding data stored on each partition. The correctness of the protocol is strictly proved. We conduct a series of experiments to compare the performance of the system when using and not using DMVCC with a scaled TPC-C benchmark. We observe that our DMVCC based system outperforms the system using 2PL at both medium (up to 1.53x speed up) and high contention (up to 2.0x speed up) levels. Furthermore, when read/write ratio goes up to 1:1, the throughput of the DMVCC based system is 290% higher than that of the system using 2PL. The scalability of the system is also presented.

关键词： Protocols Concurrency control distributed databases Throughput Database systems Concurrent computing

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Fault Tolerant Frequent Pattern Mining

Fault Tolerant Frequent Pattern Mining

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international conference on High Performance computing

作者： Sameh Shohdy Abhinav Vishnu Gagan Agrawal Department of Computer Science and Engineering The Ohio State University Columbus OH High Performance Computing Group Pacific Northwest National Laboratory Richland WA

ISBN: (纸本)9781509054121

FP-Growth algorithm is a Frequent Pattern Mining (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing, though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.

关键词： Fault tolerance Fault tolerant systems Checkpointing Algorithm design and analysis Programming Sparks Data structures

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Scalable Parallel Algorithms for Shared Nearest Neighbor Clustering

Scalable Parallel Algorithms for Shared Nearest Neighbor Clu...

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international conference on High Performance computing

作者： Sonal Kumari Saurabh Maurya Poonam Goyal Sundar S Balasubramaniam Navneet Goyal Department of Computer Science & Information Systems Advanced Data Analytics & Parallel Technologies Laboratory INDIA

ISBN: (纸本)9781509054121

Clustering is a popular data mining technique which discovers structure in unlabeled data by grouping objects together on the basis of a similarity criterion. Traditional similarity measures lose their meaning as the number of dimensions increases and as a consequence, distance or density based clustering algorithms become less meaningful. Shared Nearest Neighbor (SNN) is a solution to clustering high-dimensional data with the ability to find clusters of varying density. SNN assigns objects to a cluster, which share a large number of their nearest neighbors. However, SNN is compute and memory intensive for data of large size and/or dimensionality. Nearest neighbor queries are responsible for a major proportion of computations in SNN, resulting in lower efficiency for higher value of number of nearest neighbors (k). The main motivation of this work is to improve the efficiency of SNN and to parallelize it so that it can be used for clustering large high-dimensional datasets and for large values of k. Existing SNN algorithms become inefficient in these situations. In this paper, we present a new sequential SNN algorithm, R-SNN, which uses R-tree for executing neighborhood queries efficiently and exploiting spatial locality to minimize memory usage. R-SNN is benchmarked against the best available implementation of SNN and is found up to 77 times faster when tested on various real datasets. R-SNN is parallelized for distributed memory, shared memory, and hybrid systems. Significant speedup and scalability achieved can be attributed to parallelization and good load balancing strategies and also to exploitation of spatial locality. Experimental results demonstrate the same for datasets of varying dimensionality and size. The maximum speedup achieved for shared, distributed, and hybrid models are 427.19 using 48 threads, 394.24 using 32 processes, and 1380.69 on 32 nodes (with each node spawning 4 threads), respectively. Super-linear speedup for some datasets is attributed

关键词： Clustering algorithms Algorithm design and analysis Time complexity Spatial databases Parallel algorithms Memory management Density measurement

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Integrating Data-Intensive computing systems with Biological Data Analysis Frameworks 23

Integrating Data-Intensive Computing Systems with Biological...

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23rd Euromicro international conference on Parallel, distributed, and Network-Based Processing (PDP)

作者： Pedersen, Edvard Raknes, Inge Alexander Ernstsen, Martin Bongo, Lars Ailo Univ Tromso Dept Comp Sci N-9001 Tromso Norway Univ Tromso Ctr Bioinformat N-9001 Tromso Norway Univ Tromso Dept Chem Norstruct N-9001 Tromso Norway

ISBN: (纸本)9781479984909

Biological data analysis is typically implemented using a pipeline that combines many data analysis tools and meta-databases. These pipelines must scale to very large datasets, and therefore often require parallel and distributed computing. There are many infrastructure systems for data-intensive computing. However, most biological data analysis pipelines do not leverage these systems. An important challenge is therefore to integrate biological data analysis frameworks with data-intensive computing infrastructure systems. In this paper, we describe how we have extended data-intensive computing systems to support unmodified biological data analysis tools. We also describe four approaches for integrating the extended systems with biological data analysis frameworks, and discuss challenges for such integration on production platforms. Our results demonstrate how biological data analysis pipelines can benefit from infrastructure systems for data-intensive computing.

关键词： biological data processing data-intensive computing integration

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Evaluation of the energy consumption and the packet loss in WSNs using Deterministic Stochastic Petri Nets

Evaluation of the energy consumption and the packet loss in ...

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international Workshop on Bio and Intelligent computing

作者： Amel Berrachedi Malika Boukala-Ioualalen MOVEP Laboratory Computing Science Department USTHB

ISBN: (纸本)9781509024629

When developing critical and complex systems, the analysis and the performance evaluation of the designed system is a challenging task. Wireless Sensor Networks (WSNs) are examples of such systems. A WSN consists of a large amount of distributed nodes which monitor physical or environmental conditions. In this paper, we address the problem of how WSNs must be designed to have good performances in particular the energy consumption and the packet loss ratio. To do so, we use an expressive kind of Petri Nets called Deterministic Stochastic Petri Nets. To show the applicability of the proposed model, one of routing techniques dedicated for WSNs is presented.

关键词： WSN DSPN Energy Packet Loss

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Analysis of Software Developer Activity on a distributed Version Control System

Analysis of Software Developer Activity on a Distributed Ver...

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international Workshop on Bio and Intelligent computing

作者： Shu Li Hayato Tsukiji Kosuke Takano Department of Information and Computer Sciences Kanagawa Institute of Technology

ISBN: (纸本)9781509024629

With the widespread use of distributed version control systems for software development, many software development projects are being shared and managed in a Web environment. This allows many developers, including project leaders and supporters, those who write important functions, those who resolve bugs and errors, and those who provide fundamental source code to relieve bottlenecks, to collaboratively progress their projects. In this paper, for the analysis of various characteristics of software developers, such as their skills and project roles, we present a method of feature extraction based on those developers' history of collaborative development using a distributed version control system. Real Git projects on GitHub were experimentally analyzed, and the experimental results demonstrate the feasibility of our proposed method.

关键词： GitHub Collaborative development distributed version control system Developer Feature extraction

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