The Experts below are selected from a list of 20628 Experts worldwide ranked by ideXlab platform
Giovanni Aloisio - One of the best experts on this subject based on the ideXlab platform.
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a workflow enabled big data analytics Software Stack for escience
International Conference on High Performance Computing and Simulation, 2015Co-Authors: Cosimo Palazzo, Donatello Elia, Sandro Fiore, Andrea Mariello, Alessandro Danca, Dean N Williams, Giovanni AloisioAbstract:The availability of systems able to process and analyse big amount of data has boosted scientific advances in several fields. Workflows provide an effective tool to define and manage large sets of processing tasks. In the big data analytics area, the Ophidia project provides a cross-domain big data analytics framework for the analysis of scientific, multi-dimensional datasets. The framework exploits a server-side, declarative, parallel approach for data analysis and mining. It also features a complete workflow management system to support the execution of complex scientific data analysis, schedule tasks submission, manage operators dependencies and monitor jobs execution. The workflow management engine allows users to perform a coordinated execution of multiple data analytics operators (both single and massive - parameter sweep) in an effective manner. For the definition of the big data analytics workflow, a JSON schema has been properly designed and implemented. To aid the definition of the workflows, a visual design language consisting of several symbols, named Data Analytics Workflow Modelling Language (DAWML), has been also defined.
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Ophidia: A full Software Stack for scientific data analytics
2014 International Conference on High Performance Computing & Simulation (HPCS), 2014Co-Authors: Sandro Fiore, Donatello Elia, Alessandro D'anca, Cosimo Palazzo, Ian Foster, Dean Williams, Giovanni AloisioAbstract:The Ophidia project aims to provide a big data analytics platform solution that addresses scientific use cases related to large volumes of multidimensional data. In this work, the Ophidia Software infrastructure is discussed in detail, presenting the entire Software Stack from level-0 (the Ophidia data store) to level-3 (the Ophidia web service front end). In particular, this paper presents the big data cube primitives provided by the Ophidia framework, discussing in detail the most relevant and available data cube manipulation operators. These primitives represent the proper foundations to build more complex data cube operators like the apex one presented in this paper. A massive data reduction experiment on a 1TB climate dataset is also presented to demonstrate the apex workflow in the context of the proposed framework.
George Stamoulis - One of the best experts on this subject based on the ideXlab platform.
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managing big linked and open earth observation data using the teleios leo Software Stack
IEEE Geoscience and Remote Sensing Magazine, 2016Co-Authors: Manolis Koubarakis, Kostis Kyzirakos, Dimitrianos Savva, Konstantina Bereta, Charalampos Nikolaou, George Garbis, Roi Dogani, Stella Giannakopoulou, Panayiotis Smeros, George StamoulisAbstract:Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the Software Stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this Stack of tools can be used to implement an operational wildfire-monitoring service.
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managing big linked and open earth observation data using the teleios leo Software Stack
IEEE Geoscience and Remote Sensing Magazine, 2016Co-Authors: Manolis Koubarakis, Kostis Kyzirakos, Dimitrianos Savva, Konstantina Bereta, Charalampos Nikolaou, George Garbis, Roi Dogani, Stella Giannakopoulou, Panayiotis Smeros, George StamoulisAbstract:Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the Software Stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this Stack of tools can be used to implement an operational wildfire-monitoring service.
Yunji Chen - One of the best experts on this subject based on the ideXlab platform.
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Machine Learning Computers With Fractal von Neumann Architecture
IEEE Transactions on Computers, 2020Co-Authors: Zhao Yongwei, Zhe Fan, Zhi Tian, Liu Shaoli, Tianshi Chen, Yunji ChenAbstract:Machine learning techniques are pervasive tools for emerging commercial applications and many dedicated machine learning computers on different scales have been deployed in embedded devices, servers, and data centers. Currently, most machine learning computer architectures still focus on optimizing performance and energy efficiency instead of programming productivity. However, with the fast development in silicon technology, programming productivity, including programming itself and Software Stack development, becomes the vital reason instead of performance and power efficiency that hinders the application of machine learning computers. In this article, we propose Cambricon-F , which is a series of homogeneous, sequential, multi-layer, layer-similar, and machine learning computers with same ISA. A Cambricon-F machine has a fractal von Neumann architecture to iteratively manage its components: it is with von Neumann architecture and its processing components (sub-nodes) are still Cambricon-F machines with von Neumann architecture and the same ISA. Since different Cambricon-F instances with different scales can share the same Software Stack on their common ISA, Cambricon-Fs can significantly improve the programming productivity. Moreover, we address four major challenges in Cambricon-F architecture design, which allow Cambricon-F to achieve a high efficiency. We implement two Cambricon-F instances at different scales, i.e., Cambricon-F100 and Cambricon-F1. Compared to GPU based machines (DGX-1 and 1080Ti), Cambricon-F instances achieve 2.82x, 5.14x better performance, 8.37x, 11.39x better efficiency on average, with 74.5, 93.8 percent smaller area costs, respectively. We further propose Cambricon-FR, which enhances the Cambricon-F machine learning computers to flexibly and efficiently support all the fractal operations with a reconfigurable fractal instruction set architecture. Compared to the Cambricon-F instances, Cambricon-FR machines achieve 1.96x, 2.49x better performance on average. Most importantly, Cambricon-FR computers are able to save the code length with a factor of 5.83, thus significantly improving the programming productivity.
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cambricon f machine learning computers with fractal von neumann architecture
International Symposium on Computer Architecture, 2019Co-Authors: Yongwei Zhao, Tianshi Chen, Qi Guo, Shaoli Liu, Yunji ChenAbstract:Machine learning techniques are pervasive tools for emerging commercial applications and many dedicated machine learning computers on different scales have been deployed in embedded devices, servers, and data centers. Currently, most machine learning computer architectures still focus on optimizing performance and energy efficiency instead of programming productivity. However, with the fast development in silicon technology, programming productivity, including programming itself and Software Stack development, becomes the vital reason instead of performance and power efficiency that hinders the application of machine learning computers. In this paper, we propose Cambricon-F, which is a series of homogeneous, sequential, multi-layer, layer-similar, machine learning computers with the same ISA. A Cambricon-F machine has a fractal von Neumann architecture to iteratively manage its components: it is with von Neumann architecture and its processing components (sub-nodes) are still Cambricon-F machines with von Neumann architecture and the same ISA. Since different Cambricon-F instances with different scales can share the same Software Stack on their common ISA, Cambricon-Fs can significantly improve the programming productivity. Moreover, we address four major challenges in Cambricon-F architecture design, which allow Cambricon-F to achieve a high efficiency. We implement two Cambricon-F instances at different scales, i.e., Cambricon-F100 and Cambricon-F1. Compared to GPU based machines (DGX-1 and 1080Ti), Cambricon-F instances achieve 2.82x, 5.14x better performance, 8.37x, 11.39x better efficiency on average, with 74.5%, 93.8% smaller area costs, respectively.
Manolis Koubarakis - One of the best experts on this subject based on the ideXlab platform.
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managing big linked and open earth observation data using the teleios leo Software Stack
IEEE Geoscience and Remote Sensing Magazine, 2016Co-Authors: Manolis Koubarakis, Kostis Kyzirakos, Dimitrianos Savva, Konstantina Bereta, Charalampos Nikolaou, George Garbis, Roi Dogani, Stella Giannakopoulou, Panayiotis Smeros, George StamoulisAbstract:Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the Software Stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this Stack of tools can be used to implement an operational wildfire-monitoring service.
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managing big linked and open earth observation data using the teleios leo Software Stack
IEEE Geoscience and Remote Sensing Magazine, 2016Co-Authors: Manolis Koubarakis, Kostis Kyzirakos, Dimitrianos Savva, Konstantina Bereta, Charalampos Nikolaou, George Garbis, Roi Dogani, Stella Giannakopoulou, Panayiotis Smeros, George StamoulisAbstract:Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the Software Stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this Stack of tools can be used to implement an operational wildfire-monitoring service.
Sandro Fiore - One of the best experts on this subject based on the ideXlab platform.
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a workflow enabled big data analytics Software Stack for escience
International Conference on High Performance Computing and Simulation, 2015Co-Authors: Cosimo Palazzo, Donatello Elia, Sandro Fiore, Andrea Mariello, Alessandro Danca, Dean N Williams, Giovanni AloisioAbstract:The availability of systems able to process and analyse big amount of data has boosted scientific advances in several fields. Workflows provide an effective tool to define and manage large sets of processing tasks. In the big data analytics area, the Ophidia project provides a cross-domain big data analytics framework for the analysis of scientific, multi-dimensional datasets. The framework exploits a server-side, declarative, parallel approach for data analysis and mining. It also features a complete workflow management system to support the execution of complex scientific data analysis, schedule tasks submission, manage operators dependencies and monitor jobs execution. The workflow management engine allows users to perform a coordinated execution of multiple data analytics operators (both single and massive - parameter sweep) in an effective manner. For the definition of the big data analytics workflow, a JSON schema has been properly designed and implemented. To aid the definition of the workflows, a visual design language consisting of several symbols, named Data Analytics Workflow Modelling Language (DAWML), has been also defined.
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Ophidia: A full Software Stack for scientific data analytics
2014 International Conference on High Performance Computing & Simulation (HPCS), 2014Co-Authors: Sandro Fiore, Donatello Elia, Alessandro D'anca, Cosimo Palazzo, Ian Foster, Dean Williams, Giovanni AloisioAbstract:The Ophidia project aims to provide a big data analytics platform solution that addresses scientific use cases related to large volumes of multidimensional data. In this work, the Ophidia Software infrastructure is discussed in detail, presenting the entire Software Stack from level-0 (the Ophidia data store) to level-3 (the Ophidia web service front end). In particular, this paper presents the big data cube primitives provided by the Ophidia framework, discussing in detail the most relevant and available data cube manipulation operators. These primitives represent the proper foundations to build more complex data cube operators like the apex one presented in this paper. A massive data reduction experiment on a 1TB climate dataset is also presented to demonstrate the apex workflow in the context of the proposed framework.
