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检索条件"任意字段=Proceedings - 24th International Conference on Distributed Computing Systems Workshops"

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thermal Profiling and Modeling of Hadoop Clusters using BigData Applications 24

Thermal Profiling and Modeling of Hadoop Clusters using BigD...

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IEEE 24th international conference on High Performance computing workshops (HiPCW)

作者： Taneja, Shubbhi Chavan, Ajit Zhou, Yi Alghamdi, Mohammed Qin, Xiao Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA Al Baha Univ Dept Comp Sci Al Baha City Saudi Arabia Auburn Univ Samuel Ginn Coll Engn Shelby Ctr Engn Technol Dept Comp Sci & Software Engn Auburn AL 36849 USA

ISBN: (纸本)9781538614396

In this paper, we propose a thermal model called tModel, which projects outlet temperatures from inlet temperatures as well as directly measured multicore temperatures rather than deploying a utilization model. We perform extensive experimentation by varying applications types, their input data sizes, and cluster sizes. We collect inlet, outlet, and multicore temperatures of cluster nodes running a group of big-data applications. the proposed thermal model estimates the outlet air temperature of the nodes to predict cooling costs. We validate the accuracy of our model against data gathered by thermal sensors in our cluster. Our results demonstrate that tModel estimates outlet temperatures of the cluster nodes with much higher accuracy over CPU-utilization based models. We further show that tModel is conducive of estimating the cooling cost of data centers using the predicted outlet temperatures.

关键词： thermal profiling thermal model benchmarking MapReduce applications HPC clusters BigData multicore architecture distributed computing Hadoop

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8th international conference on ICT Innovations, ICT Innovations 2016

8th International Conference on ICT Innovations, ICT Innovat...

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8th international conference on ICT Innovations, ICT Innovations 2016

ISBN: (纸本)9783319688541

the proceedings contain 24 papers. the special focus in this conference is on ICT Innovations. the topics include: Preface;towards multimodal affective stimulation: Interaction between visual, auditory and haptic modalities;performance evaluation of FIR and IIR filtering of ECG signals;influence of fuzzy tolerance metrics on classification and regression tasks for fuzzy-rough nearest neighbour algorithms;on the Kalman filter approach for localization of mobile robots;evaluation of automatically generated conceptual database model based on business process model: Controlled experiment;analysis of protein interaction network for colorectal cancer;using sentiment analysis of Twitter data for determining popularity of city locations;internet addiction: Evaluating the psychometric properties of the IAT in Macedonia;health care domain mobile reminder for taking prescribed medications;enhancing text-based relatedness measures with semantic web data;power consumption analysis of application layer protocols for the Internet of things;cognitive and emotive robotics: Artificial brain computing cognitive actions and emotive evaluations, since 1981;improving medical cases retrieval using an online fact database;improving scalability of web applications by utilizing asynchronous I/O;relevance re-ranking through proximity based term frequency model;information system for biosensors data exchange in healthcare;an automatic tracking system for natural hazard events with satellite remote sensing;metaphors of creativity;socially intelligent robots, the next generation of consumer robots and the challenges;hand gesture recognition using deep convolutional neural networks;computer-based statistical description of phonetical balance for Romanian utterances;distributed private key generator for ID-based public key infrastructure;pattern recognition of a digital ECG.

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Fast Parallel Randomized QR with Column Pivoting Algorithms for Reliable Low-rank Matrix Approximations 24

Fast Parallel Randomized QR with Column Pivoting Algorithms ...

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IEEE 24th international conference on High Performance computing workshops (HiPCW)

作者： Xiao, Jianwei Gu, Ming Langou, Julien Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Univ Colorado Dept Math & Stat Sci Denver CO 80202 USA

ISBN: (纸本)9781538622933

Factorizing large matrices by QR with column pivoting (QRCP) is substantially more expensive than QR without pivoting, owing to communication costs required for pivoting decisions. In contrast, randomized QRCP (RQRCP) algorithms have proven themselves empirically to be highly competitive with high-performance implementations of QR in processing time, on uniprocessor and shared memory machines, and as reliable as QRCP in pivot quality. We show that RQRCP algorithms can be as reliable as QRCP with failure probabilities exponentially decaying in oversampling size. We also analyze efficiency differences among different RQRCP algorithms. More importantly, we develop distributed memory implementations of RQRCP that are significantly better than QRCP implementations in ScaLAPACK. As a further development, we introduce the concept of and develop algorithms for computing spectrum-revealing QR factorizations for low-rank matrix approximations, and demonstrate their effectiveness against leading low-rank approximation methods in both theoretical and numerical reliability and efficiency.

关键词： QR factorization low-rank approximation randomization spectrum-revealing distributed computing

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A DAG Model of Synchronous Stochastic Gradient Descent in distributed Deep Learning

A DAG Model of Synchronous Stochastic Gradient Descent in Di...

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international conference on Parallel and distributed systems (ICPADS)

作者： Shaohuai Shi Qiang Wang Xiaowen Chu Bo Li Hong Kong Baptist University Hong Kong Baptist University Kowloon HK HK The Hong Kong University of Science and Technology

With huge amounts of training data, deep learning has made great breakthroughs in many artificial intelligence (AI) applications. However, such large-scale data sets present computational challenges, requiring training to be distributed on a cluster equipped with accelerators like GPUs. With the fast increase of G PU computing power, the data communications among GPUs have become a potential bottleneck on the overall training performance. In this paper, we first propose a general directed acyclic graph (DAG) model to describe the distributed synchronous stochastic gradient descent (S-SG D) algorithm, which has been widely used in distributed deep learning frameworks. To understand the practical impact of data communications on training performance, we conduct extensive empirical studies on four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet and TensorFlow) over multi-GPU and multi-node environments with different data communication techniques, including PCIe, NVLink, 10GbE, and InfiniBand. through both analytical and experimental studies, we identify the potential bottlenecks and overheads that could be further optimized. At last, we make the data set of our experimental traces publicly available, which could be used to support simulation-based studies.

关键词： Task analysis Computational modeling Training Graphics processing units Data models Deep learning Data communication

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Online Encoding for Erasure-Coded distributed Storage systems 37

Online Encoding for Erasure-Coded Distributed Storage System...

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37th IEEE international conference on distributed computing systems (ICDCS)

作者： Xu, Fangliang Wang, Yijie Ma, Xingkong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781538632932

Many large-scale distributed storage systems deploy erasure coding to protect data from frequent server failures for cost reason. In most of these systems, newly inserted data is first replicated across different storage servers and then migrated to erasure coded. Although this offline encoding manner can improve data access before data is erasure coded for some storage systems, it helps little and wastes many critical network and disk resources for many other storage systems. In this study, we propose an online encoding method, which encodes data as soon as it is inserted into the system. By eliminating the migration process, our online encoding can significantly reduce network transfer and data read;by caching the intermediate parity blocks into memory, our online encoding also significantly reduce data write. Analysis show that our online encoding can reduce data transfer by more than 25%, reduce data write by 57% at least and eliminate all data read, compared to traditional offline encoding. Experiments on a real cluster show online encoding reduces insert time by 20%-24%.

关键词： Encoding Servers distributed databases Redundancy Pipeline processing conferences Data transfer

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Parallel Asynchronous distributed-Memory Maximal Independent Set Algorithm with Work Ordering 24

Parallel Asynchronous Distributed-Memory Maximal Independent...

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IEEE 24th international conference on High Performance computing workshops (HiPCW)

作者： Kanewala, thejaka Zalewski, Marcin Lumsdaine, Andrew Pacific Northwest Natl Lab Seattle WA 98109 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781538622933

the maximal independent set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. the growing scale of graph data suggests the use of distributed memory hardware as a cost-effective approach to providing necessary compute and memory resources. Existing distributed memory parallel MIS algorithms rely on synchronous communication and use techniques such as subgraph computations. In this paper, we present an asynchronous distributed-memory parallel graph algorithm that relies on a virtual directed acyclic graph (DAG) that is created during the algorithm execution. We introduce two additional algorithms that save computations by ordering generated work. the first algorithm applies ordering globally to reduce computations, and the second algorithm applies ordering locally at the level of threads to minimize the synchronization overhead. We use two different implementations of Luby's algorithm variants as baseline to compare the performance of the presented algorithms: (1) vertex-centric Luby A and Luby B implementations, and (2) the CombBLAS linear-algebra Luby A implementation. Results show that proposed algorithms outperform both implementations of Luby algorithms, especially in distributed execution. Furthermore, we show that for low-diameter graphs the algorithm that applies global ordering scales better than other algorithms and for high diameter graphs the original asynchronous algorithm and thread-level ordering algorithm show better performance.

关键词： maximal independent set (MIS) distributed-memory graph algorithms

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distributed Algorithm for High-Utility Subgraph Pattern Mining over Big Data Platforms 24

Distributed Algorithm for High-Utility Subgraph Pattern Mini...

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IEEE 24th international conference on High Performance computing workshops (HiPCW)

作者： Khare, Alind Goyal, Vikram Baride, Srikanth Prasad, Sushil K. McDermott, Michael Shah, Dhara Indrapastha Inst Informat Technol Dept Comp Sci New Delhi India Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781538622933

Frequent subgraph pattern mining (FSM) finds subgraph patterns that occur in a graph database with a frequency that is more than a given threshold. In FSM, the notion of occurrence captures the presence or absence of a node and an edge in a binary fashion and considers relevance of each edge or node as same. However, an edge or a node may have different relevancy score. therefore, the utility of a pattern should be defined using the relevance score of participating edges or nodes. this paper defines the utility notion of a pattern using this idea and presents algorithms to mine high-utility patterns from a given graph database. A significant issue in high-utility pattern mining is that the anti-monotonic property no longer holds contrary to the FSM. Hence pruning of the search space becomes a daunting task. To address this issue, we incorporate a function to estimate an upper-bound utility of a pattern object that also satisfies the anti-monotonic property. this paper presents three optimization heuristics for the solution on a distributed platform, namely, a novel use of bloom filter to avoid exploration of non-candidates, avoidance of sending database information with each pattern, and avoidance of sending pattern embeddings with each pattern. the experimental study on Apache Spark shows the effectiveness of our proposed optimization strategies.

关键词： subgraph mining high-utility pattern weighted graphs big data processing bloom filter based pruning

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proceedings - 24th IEEE international conference on High Performance computing, HiPC 2017

Proceedings - 24th IEEE International Conference on High Per...

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24th IEEE international conference on High Performance computing, HiPC 2017

ISBN: (纸本)9781538622933

the proceedings contain 44 papers. the topics discussed include: exact and parallel triangle counting in dynamic graphs;shared-memory graph truss decomposition;scalable exact parent sets identification in Bayesian networks learning with Apache Spark;approximate computing techniques for iterative graph algorithms;parallel exact dynamic Bayesian network structure learning with application to gene networks;parallel asynchronous distributed-memory maximal independent set algorithm with work ordering;designing registration caching free high-performance MPI library with implicit on-demand paging (ODP) of InfiniBand;last level collective hardware prefetching for data-parallel applications;support for power efficient proactive cooling mechanisms;kernel-assisted communication engine for MPI on emerging manycore processors;redundant arithmetic based high speed carry free hybrid adders with built-in scan chain on FPGAs;Convlight: a convolutional accelerator with memristor integrated photonic computing;provably efficient scheduling of dynamically allocating programs on parallel cache hierarchies;a memory congestion-aware MPI process placement for modern NUMA systems;and Expander: lock-free cache for a concurrent data structure.

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A Case Study of Stakeholder Concerns on EAM 21

A Case Study of Stakeholder Concerns on EAM

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21st IEEE international Enterprise distributed Object computing conference (EDOC)

作者： Hacks, Simon Brosius, Maximilian Aier, Stephan Rhein Westfal TH Aachen Res Grp Software Construct Aachen Germany Univ St Gallen Inst Informat Management St Gallen Switzerland

ISBN: (纸本)9781538615683

As a result of growing complexities in business processes, information systems, and the technical infrastructure, a key challenge for enterprise architecture management (EAM) is to guide stakeholders from different hierarchical levels with heterogeneous concerns. EA deliverables, such as models or frameworks, are often highly comprehensive and standardized. However, these can hardly be applied without greater adaption. Although the literature selectively covers approaches for tailoring EA deliverables closer to the concerns of affected stakeholders, these approaches are often vague or not very differentiated. In the paper at hand, we aim at introducing a stakeholder perspective to EAM research that considers stakeholder concerns on EAM across hierarchical levels. To this end, we conduct a case study: Our results show homogenous concerns among stakeholders on EA deliverables. In turn, we found different concerns on the role of EAM in applying these deliverables, dependent on the hierarchical level of stakeholders. these findings stress the necessity for a more differentiated understanding of stakeholder concerns on EAM. Finally, we discuss the implications of our findings for an exemplary EAM approach.

关键词： enterprise architecture management (EAM) stake-holder concerns hierarchical level case study

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A Memory Congestion-aware MPI Process Placement for Modern NUMA systems 24

A Memory Congestion-aware MPI Process Placement for Modern N...

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IEEE 24th international conference on High Performance computing workshops (HiPCW)

作者： Agung, Mulya Amrizal, Muhammad Alfian Komatsu, Kazuhiko Egawa, Ryusuke Takizawa, Hiroyuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808578 Japan Tohoku Univ Cybersci Ctr Sendai Miyagi Japan

ISBN: (纸本)9781538622933

MPI process placement is an important step to achieve scalable performance on modern non-uniform memory access (NUMA) systems. A recent study on NUMA architectures has shown that, on modern NUMA systems, the memory congestion problem could cause more severe performance degradation than the data locality problem because heavy congestion on memory controllers could cause long latencies. However, conventional work on MPI process placement has focused on locality to minimize the remote-access communication. Moreover, maximizing the locality may actually degrade performance because the load imbalance among nodes in a modern NUMA system may increase. thus, a process placement algorithm must be designed to consider memory congestion. In this paper, a method to reconcile both the locality and the memory congestion on modern NUMA systems is proposed. this method statically analyzes the application communication pattern to optimize the process placement. A data clustering method is applied to the time-series data of the MPI communications in order to identify data traffics that potentially cause memory congestion. the proposed method has been evaluated with the NPB kernels on a real NUMA system and a simulation environment. Experimental results show that the proposed method can achieve 1.6x performance improvement compared with the current state-of-the-art strategy.

关键词： process placement high performance computing NUMA many-core congestion

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