In this systems paper,we present MillenniumDB:a novel graph database engine that is modular,persistent,and open source.MillenniumDB is based on a graph data model,which we call domain graphs,that provides a simple abs...In this systems paper,we present MillenniumDB:a novel graph database engine that is modular,persistent,and open source.MillenniumDB is based on a graph data model,which we call domain graphs,that provides a simple abstraction upon which a variety of popular graph models can be supported,thus providing a flexible data management engine for diverse types of knowledge graph.The engine itself is founded on a combination of tried and tested techniques from relational data management,state-of-the-art algorithms for worst-case-optimal joins,as well as graph-specific algorithms for evaluating path queries.In this paper,we present the main design principles underlying MillenniumDB,describing the abstract graph model and query semantics supported,the concrete data model and query syntax implemented,as well as the storage,indexing,query planning and query evaluation techniques used.We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph,where we find that it outperforms other popular persistent graph database engines(including both enterprise and open source alternatives)that support similarqueryfeatures.展开更多
Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions.The evolution of computer architectures(multi-core and many...Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions.The evolution of computer architectures(multi-core and many-core)towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm.In the last decade,the graphics processing unit,or GPU,has gained an important place in the field of high performance computing(HPC)because of its low cost and massive parallel processing power.Super-computing has become,for the first time,available to anyone at the price of a desktop computer.In this paper,we survey the concept of parallel computing and especially GPU computing.Achieving efficient parallel algorithms for the GPU is not a trivial task,there are several technical restrictions that must be satisfied in order to achieve the expected performance.Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it.Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model.In particular,we show how this new technology can help the field of computational physics,especially when the problem is data-parallel.We present four examples of computational physics problems;n-body,collision detection,Potts model and cellular automata simulations.These examples well represent the kind of problems that are suitable for GPU computing.By understanding the GPU architecture and its massive parallelism programming model,one can overcome many of the technical limitations found along the way,design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.展开更多
We present the results of an investigation into the behavior of the unsteady flow of a Casson Micropolar nanofluid over a shrinking/stretching curved surface,together with a heat transfer analysis of the same problem....We present the results of an investigation into the behavior of the unsteady flow of a Casson Micropolar nanofluid over a shrinking/stretching curved surface,together with a heat transfer analysis of the same problem.The body force acting perpendicular to the surface wall is in charge of regulating the fluid flow rate.Curvilinear coordinates are used to account for the considered curved geometry and a set of balance equations for mass,momentum,energy and concentration is obtained accordingly.These are turned into ordinary differential equations using a similarity transformation.We show that these equations have dual solutions for a number of different combinations of various parameters.The stability of such solutions is investigated by applying perturbations on the steady states.It is found that high values of the Micropolar and Casson parameters cause the flow to move more slowly.However,when compared to a shrunken surface,a stretched surface produces a greater Micro-rotation flux.展开更多
Reproducible wafer-scale growth of two-dimensional(2D)materials using the Chemical Vapor Deposition(CVD)process with precise control over their properties is challenging due to a lack of understanding of the growth me...Reproducible wafer-scale growth of two-dimensional(2D)materials using the Chemical Vapor Deposition(CVD)process with precise control over their properties is challenging due to a lack of understanding of the growth mechanisms spanning over several length scales and sensitivity of the synthesis to subtle changes in growth conditions.A multiscale computational framework coupling Computational Fluid Dynamics(CFD),Phase-Field(PF),and reactive Molecular Dynamics(MD)was developed–called the CPM model–and experimentally verified.Correlation between theoretical predictions and thorough experimental measurements for a Metal-Organic CVD(MOCVD)-grown WSe_(2)model material revealed the full power of this computational approach.Large-area uniform 2D materials are synthesized via MOCVD,guided by computational analyses.The developed computational framework provides the foundation for guiding the synthesis of wafer-scale 2D materials with precise control over the coverage,morphology,and properties,a critical capability for fabricating electronic,optoelectronic,and quantum computing devices.展开更多
Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods.The optimization of the parameters is complex ...Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods.The optimization of the parameters is complex and requires the development of new techniques.Here,we propose an INitial-DEsign Enhanced Deep learning-based OPTimization(INDEEDopt)framework to accelerate and improve the quality of the ReaxFF parameterization.The procedure starts with a Latin Hypercube Design(LHD)algorithm that is used to explore the parameter landscape extensively.The LHD passes the information about explored regions to a deep learning model,which finds the minimum discrepancy regions and eliminates unfeasible regions,and constructs a more comprehensive understanding of physically meaningful parameter space.We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field.We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.展开更多
华语诗歌,在人类文明的历史长河中悠久、灿烂而辉煌。为了繁荣发展全球华语诗歌文化,增进同世界各国华语诗人的联系和交流,咏颂大爱精神以及人与自然和谐相处的人文情怀,《诗歌月刊》上半月刊联合深圳文宝文化公司举办一年一届的＂D C ...华语诗歌,在人类文明的历史长河中悠久、灿烂而辉煌。为了繁荣发展全球华语诗歌文化,增进同世界各国华语诗人的联系和交流,咏颂大爱精神以及人与自然和谐相处的人文情怀,《诗歌月刊》上半月刊联合深圳文宝文化公司举办一年一届的＂D C C杯＂国际华文诗歌大奖赛活动。展开更多
基金supported by the The Young Teacher Research Capacity Advancement Program of Northwest Normal University(SKQ-NYB12009)Open Research Fund of the Beijing Key Lab of Applied Experimental Psychology and Science education programs in Gansu Province(GS[2013]GHBZ086)
基金supported by ANID-Millennium Science Initiative Program-Code ICN17_002。
文摘In this systems paper,we present MillenniumDB:a novel graph database engine that is modular,persistent,and open source.MillenniumDB is based on a graph data model,which we call domain graphs,that provides a simple abstraction upon which a variety of popular graph models can be supported,thus providing a flexible data management engine for diverse types of knowledge graph.The engine itself is founded on a combination of tried and tested techniques from relational data management,state-of-the-art algorithms for worst-case-optimal joins,as well as graph-specific algorithms for evaluating path queries.In this paper,we present the main design principles underlying MillenniumDB,describing the abstract graph model and query semantics supported,the concrete data model and query syntax implemented,as well as the storage,indexing,query planning and query evaluation techniques used.We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph,where we find that it outperforms other popular persistent graph database engines(including both enterprise and open source alternatives)that support similarqueryfeatures.
基金supported by Fondecyt Project No.1120495.Finally,thanks to Renato Cerro for improving the English of this manuscript.
文摘Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions.The evolution of computer architectures(multi-core and many-core)towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm.In the last decade,the graphics processing unit,or GPU,has gained an important place in the field of high performance computing(HPC)because of its low cost and massive parallel processing power.Super-computing has become,for the first time,available to anyone at the price of a desktop computer.In this paper,we survey the concept of parallel computing and especially GPU computing.Achieving efficient parallel algorithms for the GPU is not a trivial task,there are several technical restrictions that must be satisfied in order to achieve the expected performance.Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it.Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model.In particular,we show how this new technology can help the field of computational physics,especially when the problem is data-parallel.We present four examples of computational physics problems;n-body,collision detection,Potts model and cellular automata simulations.These examples well represent the kind of problems that are suitable for GPU computing.By understanding the GPU architecture and its massive parallelism programming model,one can overcome many of the technical limitations found along the way,design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.
文摘We present the results of an investigation into the behavior of the unsteady flow of a Casson Micropolar nanofluid over a shrinking/stretching curved surface,together with a heat transfer analysis of the same problem.The body force acting perpendicular to the surface wall is in charge of regulating the fluid flow rate.Curvilinear coordinates are used to account for the considered curved geometry and a set of balance equations for mass,momentum,energy and concentration is obtained accordingly.These are turned into ordinary differential equations using a similarity transformation.We show that these equations have dual solutions for a number of different combinations of various parameters.The stability of such solutions is investigated by applying perturbations on the steady states.It is found that high values of the Micropolar and Casson parameters cause the flow to move more slowly.However,when compared to a shrunken surface,a stretched surface produces a greater Micro-rotation flux.
基金This project is partly supported by the University of Alabama,the NSF-CAREER under the NSF cooperative agreement CBET-20426832D Crystal Consortium–Material Innovation Platform(2DCC-MIP)under NSF cooperative agreements DMR-1539916 and DMR-2039351+1 种基金the I/UCRC Center for Atomically Thin Multifunctional Coatings(ATOMIC)seed project SP001-17Y.Z.J.and L.Q.C.also acknowledge the generous support by the Hamer Foundation through a Hamer Professorship.
文摘Reproducible wafer-scale growth of two-dimensional(2D)materials using the Chemical Vapor Deposition(CVD)process with precise control over their properties is challenging due to a lack of understanding of the growth mechanisms spanning over several length scales and sensitivity of the synthesis to subtle changes in growth conditions.A multiscale computational framework coupling Computational Fluid Dynamics(CFD),Phase-Field(PF),and reactive Molecular Dynamics(MD)was developed–called the CPM model–and experimentally verified.Correlation between theoretical predictions and thorough experimental measurements for a Metal-Organic CVD(MOCVD)-grown WSe_(2)model material revealed the full power of this computational approach.Large-area uniform 2D materials are synthesized via MOCVD,guided by computational analyses.The developed computational framework provides the foundation for guiding the synthesis of wafer-scale 2D materials with precise control over the coverage,morphology,and properties,a critical capability for fabricating electronic,optoelectronic,and quantum computing devices.
基金The authors acknowledge partial funding support from U.S.National Science Foundation under Award No.DMR-1842922,DMR-1842952,DMR-1539916,and MRI-1626251.
文摘Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods.The optimization of the parameters is complex and requires the development of new techniques.Here,we propose an INitial-DEsign Enhanced Deep learning-based OPTimization(INDEEDopt)framework to accelerate and improve the quality of the ReaxFF parameterization.The procedure starts with a Latin Hypercube Design(LHD)algorithm that is used to explore the parameter landscape extensively.The LHD passes the information about explored regions to a deep learning model,which finds the minimum discrepancy regions and eliminates unfeasible regions,and constructs a more comprehensive understanding of physically meaningful parameter space.We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field.We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.
