2019 International Conference on High Performance Computing & Simulation (HPCS)最新文献_第2页

Model to assess the Economic Profitability of Predictive Maintenance Projects 评估预测性维修项目经济盈利能力的模型

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188221

Christian Wolf, Andreas Kirmse, Maximilian Burkhalter, Max Hoffmann, Tobias Meisen

{"title":"Model to assess the Economic Profitability of Predictive Maintenance Projects","authors":"Christian Wolf, Andreas Kirmse, Maximilian Burkhalter, Max Hoffmann, Tobias Meisen","doi":"10.1109/HPCS48598.2019.9188221","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188221","url":null,"abstract":"Due to recent developments in data-driven technologies, predictive maintenance has become a promising alternative, especially in comparison to traditional maintenance strategies such as corrective and preventive maintenance. Even though it is currently difficult to assess if the usage of forecasting technologies in the sector of maintenance is able reduce the total cost effectively, answering this question is needed before rolling out algorithms with the aim of adapting predictive maintenance solutions on a larger scale.This paper proposes a profit and cost model that intends to realize an easy application on various processes that involve the assessment of predictive maintenance solutions. The approach divides these solutions into five steps. For each step, technological options are discussed and their costs are quantified. The resulting model can assess the profitability of a single predictive maintenance approach, but can also be applied to evaluate and compare the profitability of different predictive maintenance projects. This approach has been evaluated at a real-world industrial automotive company, where it is currently used to determine future predictive maintenance strategies.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129642807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Automatic Configuration of OpenFlow in Wireless Mobile Ad hoc Networks OpenFlow在无线移动自组织网络中的自动配置

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188200

Sachin Sharma, A. Nag, Paul Stynes, M. Nekovee

引用次数: 6

Performance-Oriented Neural Architecture Search 面向性能的神经结构搜索

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188213

Andrew Anderson, Jing Su, Rozenn Dahyot, David Gregg

引用次数: 14

A Throughput Model for Data Stream Processing on Fog Computing 基于雾计算的数据流处理吞吐量模型

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188146

Felipe Rodrigo de Souza, M. Assunção, E. Caron

{"title":"A Throughput Model for Data Stream Processing on Fog Computing","authors":"Felipe Rodrigo de Souza, M. Assunção, E. Caron","doi":"10.1109/HPCS48598.2019.9188146","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188146","url":null,"abstract":"Today’s society faces an unprecedented deluge of data that requires processing and analysis. Data Stream Processing (DSP) applications are often employed to extract valuable information in a timely manner as they can handle data as it is generated. The typical approach for deploying these applications explores the Cloud computing paradigm, which has limitations when data sources are geographically distributed, hence introducing high latency and achieving low processing throughput. To address these problems, current work attempts to take the computation closer to the edges of the Internet, exploring Fog computing. The effective adoption of this approach is achieved with proper throughput modeling that accounts for characteristics of the DSP application and Fog infrastructure, including the location of devices, processing and bandwidth requirements of the application, as well as selectivity and parallelism level of operators. In this work, we propose a throughput model for DSP applications embracing these characteristics. Results show that the model estimates the application throughput with less than 1% error.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"132 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121340706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Transforming non textually aligned SPMD programs into textually aligned SPMD programs by using rewriting rules 使用重写规则将非文本对齐的SPMD程序转换为文本对齐的SPMD程序

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188223

Wadoud Bousdira

引用次数: 0

Extended investigation of performance-energy trade-offs under power capping in HPC environments 高性能计算环境中功率上限下性能-能量权衡的扩展研究

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188149

Adam Krzywaniak, P. Czarnul, J. Proficz

引用次数: 8

Fast and Highly Optimizing Separate Compilation for Automatic Parallelization 用于自动并行化的快速和高度优化的独立编译

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188148

Tohma Kawasumi, Ryota Tamura, Yuya Asada, Jixin Han, Hiroki Mikami, K. Kimura, H. Kasahara

{"title":"Fast and Highly Optimizing Separate Compilation for Automatic Parallelization","authors":"Tohma Kawasumi, Ryota Tamura, Yuya Asada, Jixin Han, Hiroki Mikami, K. Kimura, H. Kasahara","doi":"10.1109/HPCS48598.2019.9188148","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188148","url":null,"abstract":"Automatic parallelization by a compiler is a promising approach for fully utilizing a multicore processor. Without compiler support, a programmer must simultaneously take into account parallelism in a program and memory hierarchy utilization. However, the possibility of parallelization and optimization across multiple compilation units is limited due to the lack of interprocedural analysis information at the compile time. This is a serious challenge surrounding parallelizing practical programs because they usually consist of multiple compilation units and employ separate compilation to ensure program maintainability and reduce the recompilation time. In this paper, for automatic parallelization by a compiler, we propose a separate compilation method that enables parallelization across multiple compilation units and minimizes recompilation time by providing information about the analysis along with an object file for each compilation unit at the compile time. We also propose an automatically parallelizing compilation flow with analysis information. The experimental evaluation using large size real control system programs from industry shows the proposed technique can obtain 29% better performance than the separate compilation without the proposed method, and reduce compilation time by up to 90% with only 1% of performance loss, compared with the compilation for the fully unified source code into a single compilation unit.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126563935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discriminating accurate results in nonlinear models 判别非线性模型的准确结果

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188178

Oriol Tintó Prims, M. Acosta, M. Castrillo, S. P. Ticco, K. Serradell, A. Cortés, F. Doblas-Reyes

{"title":"Discriminating accurate results in nonlinear models","authors":"Oriol Tintó Prims, M. Acosta, M. Castrillo, S. P. Ticco, K. Serradell, A. Cortés, F. Doblas-Reyes","doi":"10.1109/HPCS48598.2019.9188178","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188178","url":null,"abstract":"Non-linear models are challenging when it is time to verify that a certain HPC optimization does not degrade the accuracy of a model. Any apparently insignificant change in the code, in the software stack, or in the HPC system used can prevent bit-to-bit reproducibility, which added to the intrinsic nonlinearities of the model can lead to differences in the results of the simulation. Being able to deduce whether the different results can be explained by the internal variability of the model can help to decide if a specific change is acceptable. This manuscript presents a method that consists in estimating the uncertainty of the model outputs by doing many almost-identical simulations slightly modifying the model inputs. The statistical information extracted from these simulations can be used to discern if the results of a given simulation are indistinguishable or instead there are significant differences. Two illustrative usage examples of the method are provided, the first one studying whether a Lorenz system model can use less numerical precision and the second one studying whether the state-of-the art ocean model NEMO can safely use certain compiler optimization flags.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121909213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Interprocessor Communication and Performance Scalability for Distributed Deep Learning Systems 分布式深度学习系统的处理器间通信建模和性能可扩展性

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188168

Yi-Hong Lyu, C. Liu, Chen-Pang Lee, Chia-Heng Tu, Shih-Hao Hung

{"title":"Modeling Interprocessor Communication and Performance Scalability for Distributed Deep Learning Systems","authors":"Yi-Hong Lyu, C. Liu, Chen-Pang Lee, Chia-Heng Tu, Shih-Hao Hung","doi":"10.1109/HPCS48598.2019.9188168","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188168","url":null,"abstract":"While deep learning applications become popular, the design of deep learning systems is a critical task to unleash the computing power of underlying systems. Aside from the computing hardware, the computer networking is also a key factor that affects the delivered performance. When considering a large and complex model, the scalability of the system highly depends on the design of the networks, as well as the software behaviors. In this paper, we propose a profile-data-guided performance prediction method to estimate the performance of the system with desired high-speed interconnects, based on the profiling data obtained in a previous run. In particular, we leverage the open-source profiling tool, SOFA, for characterizing the software activities of deep learning software running in a computer cluster, and the characterized information is used to build the performance model for the model training process. When estimating the performance, SOFA is used to capture the performance critical factors for the model to make the predictions. To evaluate the proposed method, four popular deep learning models are adopted in our experiments, ResNet50, Inception3, Alexnet, and VGG16, where a computer cluster formed by four nodes is used to profile the training of the above models on TensorFlow. We ran the scalability analysis to analyze the size of the cluster, and the suitable computer networks for the models. By comparing the predicted data and those measured on the cluster, our model achieves up to 95% accuracy in most of the cases, with the maximum error rate of 10%.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131931787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RA3D: Reputation-based Adaptive 3D Video Delivery in Heterogeneous Wireless Networks RA3D:异构无线网络中基于声誉的自适应3D视频传输

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188117

Ting Bi, Longhao Zou, Shengyang Chen, Zehou Zhang, R. Trestian, Gabriel-Miro Muntean

{"title":"RA3D: Reputation-based Adaptive 3D Video Delivery in Heterogeneous Wireless Networks","authors":"Ting Bi, Longhao Zou, Shengyang Chen, Zehou Zhang, R. Trestian, Gabriel-Miro Muntean","doi":"10.1109/HPCS48598.2019.9188117","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188117","url":null,"abstract":"The continuous innovations in both high-end mobile devices and wireless communication technologies, fueled by the growing interest of mobile device users, have driven the latest development of mobile 3D video services. One of the critical challenges for mobile 3D video delivery is the limited bandwidth provided by the wireless communications between mobile devices. Nowadays, network operators are trying to cope with significant increase of data traffic amount and adopt diverse solutions to expand their network capacity. Among long-term solutions is network convergence, which involves close interworking of existing 2.5G/3G/4G networks with the new generation networks in terms of handover, network selection and network integration with other networks (e.g., WLAN, terrestrial microwave network, satellite network, etc.). In this context, the diversification in mobile devices and heterogeneity of the wireless environment make provision of always best connectivity of mobile users a challenge for the service providers. This paper proposes RA3D, a reputation-based adaptive 3D video delivery solution in heterogeneous wireless networks, which supports Always Best Experience to the mobile users by making use of multipath content delivery technologies.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132243524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0