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Improving the processing of decision support queries: the case for a DSS optimizer

Improving the processing of decision support queries: the ca...

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international symposium on Database Engineering and applications (IDEAS)

作者： H. Schwarz R. Wagner B. Mitschang Institute of Parallel and Distributed High-Performance Systems University of Stuttgart Stuttgart Germany

ISBN: (纸本)0769511406

Many decision support applications are built upon data mining and OLAP tools and allow users to answer information requests based on a data warehouse that is managed by a powerful DBMS. We focus on tools that generate sequences of SQL statements in order to produce the requested information. Our thorough analysis revealed that many sequences of queries that are generated by commercial tools are not very efficient. An optimized system architecture is suggested for these applications. The main component is a DSS optimizer that accepts previously generated sequences of queries and remodels them according to a set of optimization strategies, before they are executed by the underlying database system. The advantages of this extended architecture are discussed and a couple of appropriate optimization strategies are identified. Experimental results are given, showing that these strategies are appropriate to optimize query sequences of OLAP applications.

关键词： Computer aided software engineering Decision support systems Data mining Data warehouses Database systems Data analysis Architecture Energy management Power system management Association rules

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HCW 2022 Keynote Speaker: Heterogeneous Computing for Scientific Machine Learning

HCW 2022 Keynote Speaker: Heterogeneous Computing for Scient...

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Laurent White Advanced Micro Devices Inc.

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

More than ever, the semiconductor industry is asked to answer society's call for more computing capacity and capability, which are driven by rapid digitalization, the widespread adoption of artificial intelligence, and the ever-increasing need for high-fidelity scientific simulations. While facing high demand, the supply of computing capability is being technically challenged by the slowdown of Moore's law and the need for high energy efficiency. This tug-of-war has now pushed the industry towards domain-specific accelerators, perhaps likely past the point of no return. The mix of general-purpose CPUs and high-end GPGPUs, which has pervaded data centers over the past few years, is likely to be expanded to a much richer set of application-specific accelerators, including AI engines, reconfigurable hardware, and even perhaps quantum, annealing, and neuromorphic devices. While acceleration and better efficiency may be enabled by using domain-specific accelerators for selected workloads, a much more holistic (i.e., system-wide) approach will have to be adopted to achieve significant performance gains for complex applications that consist of a variety of workloads where each could benefit from a specific accelerator. As an important example, scientific computing, which increasingly incorporates AI training and inference kernels in a tightly-integrated fashion, provides a rich and exciting laboratory for addressing the challenges of efficiently using highly-heterogeneous systems and for ultimately realizing their promises. Those challenges include co-designing the application, which requires domain experts to collaborate with other experts across the stack for workload mapping and data orchestration, and also adopting a decentralized strategy that embeds processing units where the data need them. Finally, the early experience of those co-design efforts should help the industry devise a longer-term strategy for developing programming models that would relieve application e

关键词： Industries distributed processing Conferences Training Scientific computing Quantum annealing Programming

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Finding Potential Empathizers in an Online Mental Health Community: A Deep Graph Embedding Approach

Finding Potential Empathizers in an Online Mental Health Com...

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

作者： Yibo Chai Yahu Cong Rui Sun Fengyang Wu Zhongliang Zhang Yexing Wan Lixin Cui School of Information Central University of Finance and Economics Beijing China School of Finance Central University of Finance and Economics Beijing China School of Culture and Communication Central University of Finance and Economics Beijing China Shenzhen Corerain Technologies Co. Ltd Shenzhen China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Empathic support in online health communities (OHCs) can significantly help patients to feel less suffering and pain, fight against their diseases, and eventually recover. However, effective empathic communications in real-world OHCs are of low percentage. In this paper, we use recommendation techniques to match support-seeking posts with potential empathizers in a real-world online mental health community. Inspired by complex network analysis, we construct information-rich heterogenous network to model OHC. A graph embedding learning algorithm is devised to learn effective user profile embeddings from various information sources in heterogenous network, including node content feature, local neighborhood and sub-community structures. Compared with baseline methods, our approach achieves the best-performing recommendation results and fast computing speed.

关键词： Erbium 6G mobile communication Hafnium distributed processing Big Data Cloud computing Social computing

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A Multi-Task Service Recommendation Model Considering Dynamic and Static QoS

A Multi-Task Service Recommendation Model Considering Dynami...

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

作者： Mingyu Li Qin Lu Mingge Zhang Xinmei Liang School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong Qilu University of Technology Jinan China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Recommending the best Web service for users is a necessary task in the internet environment. At present, many of the proposed recommendations have yielded good results. Among them, recommendation methods based on Quality of Service (QoS) emerge endlessly. However, most service recommendation methods consider static or dynamic QoS separately and do not fully consider the impact of their combination. In this paper, we proposed a multi-task service recommendation model that not only models high-order and low-order features simultaneously but also considers the context information generated by the user invoking the service. Our model integrates Factorization Machine (FM) and Bi-directional Long Short-Term Memory (Bi-LSTM) into a deep neural network structure, leveraging their feature combination and deep mining capabilities. Furthermore, we use a pair of attention mechanisms to focus the task model on finding useful information related to the current output in the service data to improve the results of service recommendations. We have done enormous experiments on real QoS data sets, and the results prove that compared with other mainstream recommendation methods, the recommendation performance of this method is greatly improved.

关键词： Handheld computers Computer science distributed processing Big Data Cloud computing Social computing Quality of service

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A Methodology to Build Decision Analysis Tools Applied to distributed Reinforcement Learning

A Methodology to Build Decision Analysis Tools Applied to Di...

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Cè dric Prigent Loï c Cudennec Alexandru Costan Gabriel Antoniu University of Rennes Inria CNRS IRISA Rennes France DGA Maitrise de l&#x0027 Information Rennes France

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

As Artificial Intelligence-based applications become more and more complex, speeding up the learning phase (which is typically computation-intensive) becomes more and more necessary. distributed machine learning (ML) appears adequate to address this problem. Unfortunately, ML also brings new development frameworks, methodologies and high-level program-ming languages that do not fit to the regular high-performance computing design flow. This paper introduces a methodology to build a decision making tool that allows ML experts to arbitrate between different frameworks and deployment configurations, in order to fulfill project objectives such as the accuracy of the resulting model, the computing speed or the energy consumption of the learning computation. The proposed methodology is applied to an industrial-grade case study in which reinforcement learning is used to train an autonomous steering model for a cargo airdrop system. Results are presented within a Pareto front that lets ML experts choose an appropriate solution, a framework and a deployment configuration, based on the current operational situation. While the proposed approach can effortlessly be applied to other machine learning problems, as for many decision making systems, the selected solutions involve a trade-off between several antagonist evaluation criteria and require experts from different domains to pick the most efficient solution from the short list. Nevertheless, this methodology speeds up the development process by clearly discarding, or, on the contrary, including combinations of frameworks and configurations, which has a significant impact for time and budget-constrained projects.

关键词： Energy consumption Runtime Computational modeling Atmospheric modeling High performance computing Reinforcement learning Learning (artificial intelligence)

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Real-time data mining of multimedia objects

Real-time data mining of multimedia objects

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international symposium on Object-Oriented Real-Time distributed Computing

作者： B. Thuraisingham C. Clifton J. Maurer M.G. Ceruti MITRE Corporation Bedford MA USA Space and Naval Warfare Systems Center San Diego San Diego CA USA

ISBN: (纸本)0769510892

Whereas much of the previous work on data mining has focused on mining data in relational databases, we discuss mining objects. Object models are very popular for representing multimedia data, and therefore we need to mine object databases to extract useful information from the large quantities of multimedia data. We first describe the motivation for multimedia data mining with examples and then discuss object mining with focus on text, image, video and audio mining. We also address the need for real time data mining for multimedia applications.

关键词： Data mining Multimedia databases Relational databases Image databases Visual databases Technology management Machine learning Statistics parallel processing Data visualization

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Fault-Tolerant Cycle Embedding into 3-Ary n-Cubes with Structure Faults

Fault-Tolerant Cycle Embedding into 3-Ary n-Cubes with Struc...

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

作者： Weibei Fan Yang Wang Jing Sun Zhijie Han Peng Li Ruchuan Wang Colledge of Computer Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

3-Ary n-cube network, denoted by Q3 r, as the most important network topology in interconnection networks. It has been widely used because of its regular topology, low degree of nodes and easy implementation on a single chip. We study the cycle embedding into Q3 rwith structure faults, where each fault component is isomorphic to any connected subgraph of H1,3. Let M be a connected graph with M ϵ{W1,W1,1,W1,2,W1,3}. We prove that there is an arbitrary length cycle from 3 to |V (Q3/r - W)| if the faulty component is no more than r-1 and every fault component is isomorphic to any connected subgraph M, where W is the faulty components in an n-dimensional 3-ary n-cube. It is the first work which takes structure faults into account to study fault-tolerant pancyclicity of interconnection networks.

关键词： Multiprocessor interconnection Fault tolerance Fault tolerant systems Telecommunications Network topology parallel processing Program processors

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Performance Comparision of TPU, GPU, CPU on Google Colaboratory Over distributed Deep Learning

Performance Comparision of TPU, GPU, CPU on Google Colaborat...

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IEEE international symposium on Embedded Multicore Socs (MCSoC)

作者： Haklin Kimm Incheon Paik Hanke Kimm East Stroudsburg University East Stroudsburg PA USA School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu City Fukushima Japan Stony Brook University Stony Brook NY USA

ISBN: (纸本)9781728187525

Deep Learning models need massive amounts compute powers and tend to improve performance running on special purpose processors accelerators designed to speed up compute-intensive applications. The accelerators like Tensor processing Units (TPUs) and Graphics processing Units (GPUs) are widely used as deep learning hardware platforms which can often achieve better performance than CPUs, with their massive parallel execution resources and high memory bandwidth. Google Colaboratory known as Colab is a cloud service based on Jupyter Notebook that allows the users to write and execute mostly Python in a browser and admits free access to TPUs and GPUs without extra configuration need, which are widely available cloud hardware platforms. In this paper, we present a through comparison of the hardware platforms on Google Colab that is benchmarked with distributed Bidirectional Long Short-Term Memory (dBLSTM) models upon the number of layers, the number of units each layer, and the numbers of input and output units the datasets. Human Activity Recognition (HAR) data from UCI machine-learning library have been applied to the proposed distributed bidirectional LSTM model to find the performance, strengths, bottlenecks of the hardware platforms of TPU, GPU and CPU upon hyperparameters, execution time, and evaluation metrics: accuracy, precision, recall and F1 score.

关键词： Deep learning Measurement Training Runtime Tensors Graphics processing units Benchmark testing

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DUNES: a performance-oriented system support environment for dependency maintenance in workstation networks

DUNES: a performance-oriented system support environment for...

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international symposium on High Performance distributed Computing

作者： J. Cruz Kihong Park Network Systems Laboratory Department of Computer Sciences Purdue University West Lafayette IN USA

With the proliferation of workstation clusters connected by high-speed networks, providing efficient system support for concurrent applications engaging in nontrivial interaction has become an important problem. Two principal barriers to harnessing parallelism are: efficient mechanisms that achieve transparent dependency maintenance while preserving semantic correctness; and scheduling algorithms that match coupled processes to distributed resources while explicitly incorporating their communication costs. This paper describes a set of performance features, their properties, and implementation in a system support environment called DUNES that achieves transparent dependency maintenance-IPC, file access, memory access, process creation/termination, process relationships-under dynamic load balancing. The two principal performance features are push/pull-based active and passive end-point caching and communication-sensitive load balancing. Collectively, they mitigate the overhead introduced by the transparent dependency maintenance mechanisms. Communication-sensitive load balancing, in addition, affects the scheduling of distributed resources to application processes where both communication and computation costs are explicitly taken into account. The DUNES architecture endows commodity operating systems with distributed operating system functionality while achieving transparency with respect to their existing application base. DUNES also preserves semantic correctness with respect to single processor semantics.

关键词： Load management Operating systems Workstations High-speed networks parallel processing Scheduling algorithm Costs Processor scheduling distributed computing Computational efficiency

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Beam-tracing domain decomposition method for urban acoustic pollution

Beam-tracing domain decomposition method for urban acoustic ...

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13th international symposium on distributed Computing and applications to Business,Engineering & Science（DCABES 2014）

作者： Guillaume Gbikpi-Benissan Frdric Magoulès Ecole Centrale Paris

This paper covers the fast solution of large acoustic problems on low-resources parallel platforms. A domain decomposition method is coupled with a dynamic load balancing scheme to efficiently accelerate a geometrical acoustic method. The geometrical method studied implements a beam-tracing method where intersections are handled as in a ray-tracing method. Beyond the distribution of the global processing upon multiple sub-domains, a second parallelization level is operated by means of multi-threading and shared memory *** experiments show that this method allows to handle large scale open domains for parallel computing purposes on few machines. Urban acoustic pollution arrising from car traffic was simulated on a large model of the Shinjuku district of Tokyo, Japan. The good speed-up results illustrate the performance of this new domain decomposition method.

关键词： Domain decomposition methods parallel and distributed computing Acoustics Ray-tracing methods Beam-tracing methods

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