The uptake of distributed infrastructures by scientific applications has been limited by the availability of extensible, pervasive and simple-to-use abstractions which are required at multiple levels -- development, deployment and execution stages of scientific applications. The Pilot-Job abstraction has been shown to be an effective abstraction to address many requirements of scientific applications. Specifically, Pilot-Jobs support the decoupling of workload submission from resource assignment, this results in a flexible execution model, which in turn enables the distributed scale-out of applications on multiple and possibly heterogeneous resources. Most Pilot-Job implementations however, are tied to a specific infrastructure. In this paper, we describe the design and implementation of a SAGA-based Pilot-Job, which supports a wide range of application types, and is usable over a broad range of infrastructures, i.e., it is general-purpose and extensible, and as we will argue is also interoperable with Clouds. We discuss how the SAGA-based Pilot-Job is used for different application types and supports the concurrent usage across multiple heterogeneous distributed infrastructure, including concurrent usage across Clouds and traditional Grids/Clusters. Further, we show how Pilot-Jobs can help to support dynamic execution models and thus, introduce new opportunities for distributed applications. We also demonstrate for the first time that we are aware of, the use of multiple Pilot-Job implementations to solve the same problem, specifically, we use the SAGA-based Pilot-Job on high-end resources such as the TeraGrid and the native Condor Pilot-Job (Glide-in) on Condor resources. Importantly both are invoked via the same interface without changes at the development or deployment level, but only an execution (run-time) decision.
{"title":"SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems","authors":"André Luckow, Lukasz Lacinski, S. Jha","doi":"10.1109/CCGRID.2010.91","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.91","url":null,"abstract":"The uptake of distributed infrastructures by scientific applications has been limited by the availability of extensible, pervasive and simple-to-use abstractions which are required at multiple levels -- development, deployment and execution stages of scientific applications. The Pilot-Job abstraction has been shown to be an effective abstraction to address many requirements of scientific applications. Specifically, Pilot-Jobs support the decoupling of workload submission from resource assignment, this results in a flexible execution model, which in turn enables the distributed scale-out of applications on multiple and possibly heterogeneous resources. Most Pilot-Job implementations however, are tied to a specific infrastructure. In this paper, we describe the design and implementation of a SAGA-based Pilot-Job, which supports a wide range of application types, and is usable over a broad range of infrastructures, i.e., it is general-purpose and extensible, and as we will argue is also interoperable with Clouds. We discuss how the SAGA-based Pilot-Job is used for different application types and supports the concurrent usage across multiple heterogeneous distributed infrastructure, including concurrent usage across Clouds and traditional Grids/Clusters. Further, we show how Pilot-Jobs can help to support dynamic execution models and thus, introduce new opportunities for distributed applications. We also demonstrate for the first time that we are aware of, the use of multiple Pilot-Job implementations to solve the same problem, specifically, we use the SAGA-based Pilot-Job on high-end resources such as the TeraGrid and the native Condor Pilot-Job (Glide-in) on Condor resources. Importantly both are invoked via the same interface without changes at the development or deployment level, but only an execution (run-time) decision.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123193505","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}
Desktop Grids have become very popular nowadays, with projects that include hundred of thousands computers. Desktop grid scheduling faces two challenges. First, the platform is volatile, since users may reclaim their computer at any time, which makes centralized schedulers inappropriate. Second, desktop grids are likely to be shared among several users, thus we must be particularly careful to ensure a fair sharing of the resources. In this paper, we propose a decentralized scheduler for bag-of-tasks applications on desktop grids, which ensures a fair and efficient use of the resources. It aims to provide a similar share of the platform to every application by minimizing their maximum stretch, using completely decentralized algorithms and protocols.
{"title":"A Fair Decentralized Scheduler for Bag-of-Tasks Applications on Desktop Grids","authors":"Javier Celaya, L. Marchal","doi":"10.1109/CCGRID.2010.13","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.13","url":null,"abstract":"Desktop Grids have become very popular nowadays, with projects that include hundred of thousands computers. Desktop grid scheduling faces two challenges. First, the platform is volatile, since users may reclaim their computer at any time, which makes centralized schedulers inappropriate. Second, desktop grids are likely to be shared among several users, thus we must be particularly careful to ensure a fair sharing of the resources. In this paper, we propose a decentralized scheduler for bag-of-tasks applications on desktop grids, which ensures a fair and efficient use of the resources. It aims to provide a similar share of the platform to every application by minimizing their maximum stretch, using completely decentralized algorithms and protocols.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122657640","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}
Coupled Multi-Physics simulations, such as hybrid CFD-MD simulations, represent an increasingly important class of scientific applications. Often the physical problems of interest demand the use of high-end computers, such as TeraGrid resources, which are often accessible only via batch-queues. Batch-queue systems are not developed to natively support the coordinated scheduling of jobs – which in turn is required to support the concurrent execution required by coupled multi-physics simulations. In this paper we develop and demonstrate a novel approach to overcome the lack of native support for coordinated job submission requirement associated with coupled runs. We establish the performance advantages arising from our solution, which is a generalization of the Pilot-Job concept – which in of itself is not new, but is being applied to coupled simulations for the first time. Our solution not only overcomes the initial co-scheduling problem, but also provides a dynamic resource allocation mechanism. Support for such dynamic resources is critical for a load balancing mechanism, which we develop and demonstrate to be effective at reducing the total time-to-solution of the problem. We establish that the performance advantage of using Big Jobs is invariant with the size of the machine as well as the size of the physical model under investigation. The Pilot-Job abstraction is developed using SAGA, which provides an infrastructure agnostic implementation, and which can seamlessly execute and utilize distributed resources.
{"title":"Efficient Runtime Environment for Coupled Multi-physics Simulations: Dynamic Resource Allocation and Load-Balancing","authors":"S. Ko, Nayong Kim, Joohyun Kim, A. Thota, S. Jha","doi":"10.1109/CCGRID.2010.107","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.107","url":null,"abstract":"Coupled Multi-Physics simulations, such as hybrid CFD-MD simulations, represent an increasingly important class of scientific applications. Often the physical problems of interest demand the use of high-end computers, such as TeraGrid resources, which are often accessible only via batch-queues. Batch-queue systems are not developed to natively support the coordinated scheduling of jobs – which in turn is required to support the concurrent execution required by coupled multi-physics simulations. In this paper we develop and demonstrate a novel approach to overcome the lack of native support for coordinated job submission requirement associated with coupled runs. We establish the performance advantages arising from our solution, which is a generalization of the Pilot-Job concept – which in of itself is not new, but is being applied to coupled simulations for the first time. Our solution not only overcomes the initial co-scheduling problem, but also provides a dynamic resource allocation mechanism. Support for such dynamic resources is critical for a load balancing mechanism, which we develop and demonstrate to be effective at reducing the total time-to-solution of the problem. We establish that the performance advantage of using Big Jobs is invariant with the size of the machine as well as the size of the physical model under investigation. The Pilot-Job abstraction is developed using SAGA, which provides an infrastructure agnostic implementation, and which can seamlessly execute and utilize distributed resources.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124977535","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}
A. V. Dastjerdi, Sayed Gholam Hassan Tabatabaei, R. Buyya
Cloud computing is a computing paradigm which allows access of computing elements and storages on-demand over the Internet. Virtual Appliances, pre-configured, ready-to-run applications are emerging as a breakthrough technology to solve the complexities of service deployment on Cloud infrastructure. However, an automated approach to deploy required appliances on the most suitable Cloud infrastructure is neglected by previous works which is the focus of this work. In this paper, we propose an effective architecture using ontology-based discovery to provide QoS aware deployment of appliances on Cloud service providers. In addition, we test our approach on a case study and the result shows the efficiency and effectiveness of the proposed work.
{"title":"An Effective Architecture for Automated Appliance Management System Applying Ontology-Based Cloud Discovery","authors":"A. V. Dastjerdi, Sayed Gholam Hassan Tabatabaei, R. Buyya","doi":"10.1109/CCGRID.2010.87","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.87","url":null,"abstract":"Cloud computing is a computing paradigm which allows access of computing elements and storages on-demand over the Internet. Virtual Appliances, pre-configured, ready-to-run applications are emerging as a breakthrough technology to solve the complexities of service deployment on Cloud infrastructure. However, an automated approach to deploy required appliances on the most suitable Cloud infrastructure is neglected by previous works which is the focus of this work. In this paper, we propose an effective architecture using ontology-based discovery to provide QoS aware deployment of appliances on Cloud service providers. In addition, we test our approach on a case study and the result shows the efficiency and effectiveness of the proposed work.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116813584","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}
Current large distributed systems allow users to share and trade resources. In cloud computing, users purchase different types of resources from one or more resource providers using a fixed pricing scheme. Federated clouds, a topic of recent interest, allows different cloud providers to share resources for increased scalability and reliability. However, users and providers of cloud resources are rational and maximize their own interest when consuming and contributing shared resources. In this paper, we present a dyanmic pricing scheme suitable for rational users requests containing multiple resource types. Using simulations, we compare the efficiency of our proposed strategy-proof dynamic scheme with fixed pricing, and show that user welfare and the percentage of successful requests is increased by using dynamic pricing.
{"title":"Dynamic Resource Pricing on Federated Clouds","authors":"Marian Mihailescu, Y. M. Teo","doi":"10.1109/CCGRID.2010.123","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.123","url":null,"abstract":"Current large distributed systems allow users to share and trade resources. In cloud computing, users purchase different types of resources from one or more resource providers using a fixed pricing scheme. Federated clouds, a topic of recent interest, allows different cloud providers to share resources for increased scalability and reliability. However, users and providers of cloud resources are rational and maximize their own interest when consuming and contributing shared resources. In this paper, we present a dyanmic pricing scheme suitable for rational users requests containing multiple resource types. Using simulations, we compare the efficiency of our proposed strategy-proof dynamic scheme with fixed pricing, and show that user welfare and the percentage of successful requests is increased by using dynamic pricing.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130743104","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}
Grids and clouds management and administration becomes a challenge as they become more complicated. This paper together with associated poster present ideas and solutions related to visualization of usage and performance data. The presented system can be used by system or network administrators as well as grid researchers to improve grid functionality or to test new concepts of grid organization.
{"title":"In Search of Visualization Metaphors for PlanetLab","authors":"Andrew J. Zaliwski","doi":"10.1109/CCGRID.2010.35","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.35","url":null,"abstract":"Grids and clouds management and administration becomes a challenge as they become more complicated. This paper together with associated poster present ideas and solutions related to visualization of usage and performance data. The presented system can be used by system or network administrators as well as grid researchers to improve grid functionality or to test new concepts of grid organization.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066304","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}
To bridge the gap between computing grid environment and users, various Grid Widgets are developed by the Grid development team in the National Center for High-performance Computing (NCHC). These widgets are implemented to provide users with seamless and scalable access to Grid resources. Currently, this effort integrates the de facto Grid middleware, Web-based Operating System (WebOS), and automatic resource allocation mechanism to form a virtual computer in distributed computing environment. With the capability of automatic resource allocation and the feature of dynamic load prediction, the Resource Broker (RB) improves the performance of the dynamic scheduling over conventional scheduling policies. With this extremely lightweight and flexible approach to acquire Grid services, the barrier for users to access geographically distributed heterogeneous Grid resources is largely reduced. The Grid Widgets can also be customized and configured to meet the demands of the users.
{"title":"The Lightweight Approach to Use Grid Services with Grid Widgets on Grid WebOS","authors":"Yi-Lun Pan, Chang-Hsing Wu, Chia-Yen Liu, Hsi-En Yu, Weicheng Huang","doi":"10.1109/CCGRID.2010.25","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.25","url":null,"abstract":"To bridge the gap between computing grid environment and users, various Grid Widgets are developed by the Grid development team in the National Center for High-performance Computing (NCHC). These widgets are implemented to provide users with seamless and scalable access to Grid resources. Currently, this effort integrates the de facto Grid middleware, Web-based Operating System (WebOS), and automatic resource allocation mechanism to form a virtual computer in distributed computing environment. With the capability of automatic resource allocation and the feature of dynamic load prediction, the Resource Broker (RB) improves the performance of the dynamic scheduling over conventional scheduling policies. With this extremely lightweight and flexible approach to acquire Grid services, the barrier for users to access geographically distributed heterogeneous Grid resources is largely reduced. The Grid Widgets can also be customized and configured to meet the demands of the users.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121904228","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}
The emergence of streaming multicore processors with multi-SIMD architectures and ultra-low power operation combined with real-time compute and I/O reconfigurability opens unprecedented opportunities for executing sophisticated signal processing algorithms faster and within a much lower energy budget. Here, we present an unconventional FFT implementation scheme for the IBM Cell, named transverse vectorization. It is shown to outperform (both in terms of timing or GFLOP throughput) the fastest FFT results reported to date in the open literature.
{"title":"Multi-FFT Vectorization for the Cell Multicore Processor","authors":"J. Barhen, T. Humble, P. Mitra, M. Traweek","doi":"10.1109/CCGRID.2010.78","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.78","url":null,"abstract":"The emergence of streaming multicore processors with multi-SIMD architectures and ultra-low power operation combined with real-time compute and I/O reconfigurability opens unprecedented opportunities for executing sophisticated signal processing algorithms faster and within a much lower energy budget. Here, we present an unconventional FFT implementation scheme for the IBM Cell, named transverse vectorization. It is shown to outperform (both in terms of timing or GFLOP throughput) the fastest FFT results reported to date in the open literature.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122303248","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}
D. Krefting, R. Lützkendorf, Kathrin Peter, J. Bernarding
Analysis of diffusion weighted magnetic resonance images serves increasingly for non-invasive tracking of nerve fibers in the human brain, both in clinical diagnosis and basic research. Diffusion-tensor imaging (DTI) enables in-vivo research on the internal structure of the central nervous system, an estimation of the interconnection of functional areas and diagnosis of brain tumors and de-myelinating diseases. But modeling the local diffusion parameters is computationally expensive and on standard desktop computers runtimes of up to days are common. A workflow based grid implementation of the algorithm with slice-based parallelization has shown significant speedup. However, in production use, the implementation frequently delayed and even failed, discouraging the medical collaborators to take up the management of the data processing themselves. Therefore a comprehensive analysis of possible sources for errors and delays as well as their real impact in the respective infrastructure is vital to enable clinical researchers to fully exploit the benefits of the Healthgrid application. In this manuscript, we tested different implementations of the DTI analysis with respect to robustness and runtime. Based on the results, concrete application improvements as well as general suggestions for the layout and maintenance of Healthgrids are concluded.
{"title":"Performance Analysis of Diffusion Tensor Imaging in an Academic Production Grid","authors":"D. Krefting, R. Lützkendorf, Kathrin Peter, J. Bernarding","doi":"10.1109/CCGRID.2010.21","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.21","url":null,"abstract":"Analysis of diffusion weighted magnetic resonance images serves increasingly for non-invasive tracking of nerve fibers in the human brain, both in clinical diagnosis and basic research. Diffusion-tensor imaging (DTI) enables in-vivo research on the internal structure of the central nervous system, an estimation of the interconnection of functional areas and diagnosis of brain tumors and de-myelinating diseases. But modeling the local diffusion parameters is computationally expensive and on standard desktop computers runtimes of up to days are common. A workflow based grid implementation of the algorithm with slice-based parallelization has shown significant speedup. However, in production use, the implementation frequently delayed and even failed, discouraging the medical collaborators to take up the management of the data processing themselves. Therefore a comprehensive analysis of possible sources for errors and delays as well as their real impact in the respective infrastructure is vital to enable clinical researchers to fully exploit the benefits of the Healthgrid application. In this manuscript, we tested different implementations of the DTI analysis with respect to robustness and runtime. Based on the results, concrete application improvements as well as general suggestions for the layout and maintenance of Healthgrids are concluded.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122733833","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}
In this research, we investigate and address the challenges of asymmetry in High-End Computing (HEC) systems comprising heterogeneous architectures with varying I/O and computation capacities. We focus on developing a flexible, scalable and easy-to-use programming model that automatically adapts to the capabilities of the system resources on largescale asymmetric clusters. Furthermore, we aim to develop innovative and efficient workload distribution techniques that bridge the asymmetry between system components. In particular, we intent to design tools and technologies that enable quick and efficient utilization of high-end asymmetric clusters in large-scale settings for modern scientific and enterprise computing.
{"title":"A Capabilities-Aware Programming Model for Asymmetric High-End Systems","authors":"M. M. Rafique","doi":"10.1109/CCGRID.2010.131","DOIUrl":"https://doi.org/10.1109/CCGRID.2010.131","url":null,"abstract":"In this research, we investigate and address the challenges of asymmetry in High-End Computing (HEC) systems comprising heterogeneous architectures with varying I/O and computation capacities. We focus on developing a flexible, scalable and easy-to-use programming model that automatically adapts to the capabilities of the system resources on largescale asymmetric clusters. Furthermore, we aim to develop innovative and efficient workload distribution techniques that bridge the asymmetry between system components. In particular, we intent to design tools and technologies that enable quick and efficient utilization of high-end asymmetric clusters in large-scale settings for modern scientific and enterprise computing.","PeriodicalId":444485,"journal":{"name":"2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126469491","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}
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