A microtubule gliding assay is a biological experiment observing the dynamics of microtubules driven by motor proteins fixed on a glass surface. When appropriate microtubule interactions are set up on gliding assay ex...A microtubule gliding assay is a biological experiment observing the dynamics of microtubules driven by motor proteins fixed on a glass surface. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics on computers, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation paranleters in a real-time fashion. One of the technical challenges when creating a real-time 3D simulation is balancing the 3D rendering and the computing performance. Graphics processor unit （GPU） programming plays an essential role in balancing the millions of tasks, and makes this real-time 3D simulation possible. By the use of general-purpose computing on graphics processing units （GPGPU） programming we are able to run the simulation in a massively parallel fashion, even when dealing with more complex interactions between microtubules such as overriding and snuggling. Due to performance being an important factor, a performance n, odel has also been constructed from the analysis of the microtubule simulation and it is consistent with the performance measurements on different GPGPU architectures with regards to the number of cores and clock cycles.展开更多
Bioturbation is one of the important processes that affect the structure and function of sedimentary environments.The particle mixing and element migration processes caused by bioturbation can interfere with the circu...Bioturbation is one of the important processes that affect the structure and function of sedimentary environments.The particle mixing and element migration processes caused by bioturbation can interfere with the circulation of matter and the explanation of sedimentary records.Therefore,the quantitative characterization of bioturbation structures in the sedimentary sequence is of great significance in the field of sedimentology.Estuaries,where fresh and saltwater mix,exhibit high ecological heterogeneity and biodiversity,making them ideal places to explore bioturbation.This paper targets the subaqueous Yellow River Delta to quantitatively characterize bioturbation structures and their spatial distribution patterns using computed tomography(CT)scanning and three-dimensional reconstruction technology.By combining sediment characteristics and sedimentary environment analysis,the main factors affecting bioturbation structures are elucidated.The results show that bioturbation structures in the subaqueous Yellow River Delta can be divided into four types based on their morphology:uniaxial type,biaxial type,triaxial type,and multiaxial type.Skolithos,Palaeophycus in the uniaxial type,and Thalassinoides in the multiaxial type are the most developed structures.Different types of bioturbation may be constructed by trace-making organisms belonging to the same category or functional group.The intensity of bioturbation in this area ranges from 0 to 4%,with a decreasing trend from nearshore to offshore.There is a downward decreasing trend in the intensity of bioturbation overall in the sedimentary cores,with three vertical distribution patterns:exponential decay pattern,fluctuating decay pattern,and impulsive pattern.The impulsive pattern of bioturbation in a core may indicate the abrupt change in sedimentary environment induced by the Yellow River channel shift in 1996.These results suggest that factors affecting the development of bioturbation include grain size,porosity,consolidation,organic matter content of sediments,and sedimentation rate that is mainly influenced by local hydrodynamic conditions.The environment with clayey silt(average grain size 10μm)and moderate sedimentation rate(around 0.5 cm yr^(-1))is the most suitable area for the development of bioturbation in the Yellow River subaqueous delta.展开更多
Artificial neural networks with internal dynamics exhibit remarkable capability in processing information.Reservoir computing(RC)is a canonical example that features rich computing expressivity and compatibility with ...Artificial neural networks with internal dynamics exhibit remarkable capability in processing information.Reservoir computing(RC)is a canonical example that features rich computing expressivity and compatibility with physical implementations for enhanced efficiency.Recently,a new RC paradigm known as next generation reservoir computing(NGRC)further improves expressivity but compromises its physical openness,posing challenges for realizations in physical systems.Here we demonstrate optical NGRC with computations performed by light scattering through disordered media.In contrast to conventional optical RC implementations,we directly and solely drive our optical reservoir with time-delayed inputs.Much like digital NGRC that relies on polynomial features of delayed inputs,our optical reservoir also implicitly generates these polynomial features for desired functionalities.By leveraging the domain knowledge of the reservoir inputs,we show that the optical NGRC not only predicts the short-term dynamics of the low-dimensional Lorenz63 and large-scale Kuramoto-Sivashinsky chaotic time series,but also replicates their long-term ergodic properties.Optical NGRC shows superiority in shorter training length and fewer hyperparameters compared to conventional optical RC based on scattering media,while achieving better forecasting performance.Our optical NGRC framework may inspire the realization of NGRC in other physical RC systems,new applications beyond time-series processing,and the development of deep and parallel architectures broadly.展开更多
A non-photorealistic rendering technique is a method to show various effects different from those of realistic image generation.Of the various techniques,flow-based image abstraction displays the shape and color featu...A non-photorealistic rendering technique is a method to show various effects different from those of realistic image generation.Of the various techniques,flow-based image abstraction displays the shape and color features well and performs a stylistic visual abstraction.But real-time rendering is impossible when CPU is used because it applies various filtering and iteration methods.In this paper,we present real-time processing methods of video abstraction using open open computing language(OpenCL),technique of general-purpose computing on graphics processing units(GPGPU).Through the acceleration of general-purpose computing(GPU),16 frame-per-second(FPS)or greater is shown to process video abstraction.展开更多
This article explores the changing nature of the interaction between computer science and the natural and social sciences. After briefly tracing the history of scientific computation, the article presents the concept ...This article explores the changing nature of the interaction between computer science and the natural and social sciences. After briefly tracing the history of scientific computation, the article presents the concept of computational lens, a metaphor for a new relationship that is emerging between the world of computation and the world of the sciences. Our main thesis is that, in many scientific fields, the processes being studied can be viewed as computational in nature, in the sense that the processes perform dynamic transformations on information represented as digital data. Viewing natural or engineered systems through the lens of their computational requirements or capabilities provides new insights and ways of thinking. A number of examples are discussed in support of this thesis. The examples are from various fields, including quantum computing, statistical physics, the World Wide Web and the Internet, mathematics, and computational molecular biology.展开更多
A moisture advection scheme is an essential module of a numerical weather/climate model representing the horizontal transport of water vapor.The Piecewise Rational Method(PRM) scalar advection scheme in the Global/Reg...A moisture advection scheme is an essential module of a numerical weather/climate model representing the horizontal transport of water vapor.The Piecewise Rational Method(PRM) scalar advection scheme in the Global/Regional Assimilation and Prediction System(GRAPES) solves the moisture flux advection equation based on PRM.Computation of the scalar advection involves boundary exchange,and computation of higher bandwidth requirements is complicated and time-consuming in GRAPES.Recently,Graphics Processing Units(GPUs) have been widely used to solve scientific and engineering computing problems owing to advancements in GPU hardware and related programming models such as CUDA/OpenCL and Open Accelerator(OpenACC).Herein,we present an accelerated PRM scalar advection scheme with Message Passing Interface(MPI) and OpenACC to fully exploit GPUs’ power over a cluster with multiple Central Processing Units(CPUs) and GPUs,together with optimization of various parameters such as minimizing data transfer,memory coalescing,exposing more parallelism,and overlapping computation with data transfers.Results show that about 3.5 times speedup is obtained for the entire model running at medium resolution with double precision when comparing the scheme’s elapsed time on a node with two GPUs(NVIDIA P100) and two 16-core CPUs(Intel Gold 6142).Further,results obtained from experiments of a higher resolution model with multiple GPUs show excellent scalability.展开更多
The Moving Particle Semi-implicit (MPS) method performs well in simulating violent free surface flow and hence becomes popular in the area of fluid flow simulation. However, the implementations of searching neighbouri...The Moving Particle Semi-implicit (MPS) method performs well in simulating violent free surface flow and hence becomes popular in the area of fluid flow simulation. However, the implementations of searching neighbouring particles and solving the large sparse matrix equations (Poisson-type equation) are very time-consuming. In order to utilize the tremendous power of parallel computation of Graphics Processing Units (GPU), this study has developed a GPU-based MPS model employing the Compute Unified Device Architecture (CUDA) on NVIDIA GTX 280. The efficient neighbourhood particle searching is done through an indirect method and the Poisson-type pressure equation is solved by the Bi-Conjugate Gradient (BiCG) method. Four different optimization levels for the present general parallel GPU-based MPS model are demonstrated. In addition, the elaborate optimization of GPU code is also discussed. A benchmark problem of dam-breaking flow is simulated using both codes of the present GPU-based MPS and the original CPU-based MPS. The comparisons between them show that the GPU-based MPS model outperforms 26 times the traditional CPU model.展开更多
基金supported by a Grant-in-Aid for Scientific Research on Innovation Areas "Molecular Robotics"(No.24104004) of the Ministry of Education,Culture,Sports,Science,and Technology,Japan
文摘A microtubule gliding assay is a biological experiment observing the dynamics of microtubules driven by motor proteins fixed on a glass surface. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics on computers, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation paranleters in a real-time fashion. One of the technical challenges when creating a real-time 3D simulation is balancing the 3D rendering and the computing performance. Graphics processor unit （GPU） programming plays an essential role in balancing the millions of tasks, and makes this real-time 3D simulation possible. By the use of general-purpose computing on graphics processing units （GPGPU） programming we are able to run the simulation in a massively parallel fashion, even when dealing with more complex interactions between microtubules such as overriding and snuggling. Due to performance being an important factor, a performance n, odel has also been constructed from the analysis of the microtubule simulation and it is consistent with the performance measurements on different GPGPU architectures with regards to the number of cores and clock cycles.
基金supported by the National Natural Science Foundation of China(No.42176077)。
文摘Bioturbation is one of the important processes that affect the structure and function of sedimentary environments.The particle mixing and element migration processes caused by bioturbation can interfere with the circulation of matter and the explanation of sedimentary records.Therefore,the quantitative characterization of bioturbation structures in the sedimentary sequence is of great significance in the field of sedimentology.Estuaries,where fresh and saltwater mix,exhibit high ecological heterogeneity and biodiversity,making them ideal places to explore bioturbation.This paper targets the subaqueous Yellow River Delta to quantitatively characterize bioturbation structures and their spatial distribution patterns using computed tomography(CT)scanning and three-dimensional reconstruction technology.By combining sediment characteristics and sedimentary environment analysis,the main factors affecting bioturbation structures are elucidated.The results show that bioturbation structures in the subaqueous Yellow River Delta can be divided into four types based on their morphology:uniaxial type,biaxial type,triaxial type,and multiaxial type.Skolithos,Palaeophycus in the uniaxial type,and Thalassinoides in the multiaxial type are the most developed structures.Different types of bioturbation may be constructed by trace-making organisms belonging to the same category or functional group.The intensity of bioturbation in this area ranges from 0 to 4%,with a decreasing trend from nearshore to offshore.There is a downward decreasing trend in the intensity of bioturbation overall in the sedimentary cores,with three vertical distribution patterns:exponential decay pattern,fluctuating decay pattern,and impulsive pattern.The impulsive pattern of bioturbation in a core may indicate the abrupt change in sedimentary environment induced by the Yellow River channel shift in 1996.These results suggest that factors affecting the development of bioturbation include grain size,porosity,consolidation,organic matter content of sediments,and sedimentation rate that is mainly influenced by local hydrodynamic conditions.The environment with clayey silt(average grain size 10μm)and moderate sedimentation rate(around 0.5 cm yr^(-1))is the most suitable area for the development of bioturbation in the Yellow River subaqueous delta.
基金supported by Swiss National Science Foundation(SNF)projects LION,ERC SMARTIES and Institut Universitaire de France.H.W.acknowledges China Scholarship Council and National Natural Science Foundation of China(623B2064 and 62275137)J.H.acknowledges SNF fellowship(P2ELP2_199825)+3 种基金Y.B.acknowledges the support from Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2022R1A6A3A03072108)European Union’s Horizon Europe research and innovation program(N.101105899)Q.L.acknowledges National Natural Science Foundation of China(62275137)the Tsinghua University(Department of Precision Instrument)-North Laser Research Institute Co.,Ltd Joint Research Center for Advanced Laser Technology(20244910194).
文摘Artificial neural networks with internal dynamics exhibit remarkable capability in processing information.Reservoir computing(RC)is a canonical example that features rich computing expressivity and compatibility with physical implementations for enhanced efficiency.Recently,a new RC paradigm known as next generation reservoir computing(NGRC)further improves expressivity but compromises its physical openness,posing challenges for realizations in physical systems.Here we demonstrate optical NGRC with computations performed by light scattering through disordered media.In contrast to conventional optical RC implementations,we directly and solely drive our optical reservoir with time-delayed inputs.Much like digital NGRC that relies on polynomial features of delayed inputs,our optical reservoir also implicitly generates these polynomial features for desired functionalities.By leveraging the domain knowledge of the reservoir inputs,we show that the optical NGRC not only predicts the short-term dynamics of the low-dimensional Lorenz63 and large-scale Kuramoto-Sivashinsky chaotic time series,but also replicates their long-term ergodic properties.Optical NGRC shows superiority in shorter training length and fewer hyperparameters compared to conventional optical RC based on scattering media,while achieving better forecasting performance.Our optical NGRC framework may inspire the realization of NGRC in other physical RC systems,new applications beyond time-series processing,and the development of deep and parallel architectures broadly.
文摘A non-photorealistic rendering technique is a method to show various effects different from those of realistic image generation.Of the various techniques,flow-based image abstraction displays the shape and color features well and performs a stylistic visual abstraction.But real-time rendering is impossible when CPU is used because it applies various filtering and iteration methods.In this paper,we present real-time processing methods of video abstraction using open open computing language(OpenCL),technique of general-purpose computing on graphics processing units(GPGPU).Through the acceleration of general-purpose computing(GPU),16 frame-per-second(FPS)or greater is shown to process video abstraction.
基金supported in part by the National Science Foundation of USA for SGER under Grant No. CCF-0652536 "Planning for a Cross-Cutting Initiative in Computational Discovery" and Einstein Professorship of Chinese Academy of Sciences
文摘This article explores the changing nature of the interaction between computer science and the natural and social sciences. After briefly tracing the history of scientific computation, the article presents the concept of computational lens, a metaphor for a new relationship that is emerging between the world of computation and the world of the sciences. Our main thesis is that, in many scientific fields, the processes being studied can be viewed as computational in nature, in the sense that the processes perform dynamic transformations on information represented as digital data. Viewing natural or engineered systems through the lens of their computational requirements or capabilities provides new insights and ways of thinking. A number of examples are discussed in support of this thesis. The examples are from various fields, including quantum computing, statistical physics, the World Wide Web and the Internet, mathematics, and computational molecular biology.
基金supported by the decision support project of response to climate change of China,the National Natural Science Foundation of China (Nos.41674085, 41604009, and 41621091)the Natural Science Foundation of Qinghai Province (No. 2019-ZJ-7034)the Open Project of State Key Laboratory of Plateau Ecology and Agriculture,Qinghai University (No. 2020-zz-03)。
文摘A moisture advection scheme is an essential module of a numerical weather/climate model representing the horizontal transport of water vapor.The Piecewise Rational Method(PRM) scalar advection scheme in the Global/Regional Assimilation and Prediction System(GRAPES) solves the moisture flux advection equation based on PRM.Computation of the scalar advection involves boundary exchange,and computation of higher bandwidth requirements is complicated and time-consuming in GRAPES.Recently,Graphics Processing Units(GPUs) have been widely used to solve scientific and engineering computing problems owing to advancements in GPU hardware and related programming models such as CUDA/OpenCL and Open Accelerator(OpenACC).Herein,we present an accelerated PRM scalar advection scheme with Message Passing Interface(MPI) and OpenACC to fully exploit GPUs’ power over a cluster with multiple Central Processing Units(CPUs) and GPUs,together with optimization of various parameters such as minimizing data transfer,memory coalescing,exposing more parallelism,and overlapping computation with data transfers.Results show that about 3.5 times speedup is obtained for the entire model running at medium resolution with double precision when comparing the scheme’s elapsed time on a node with two GPUs(NVIDIA P100) and two 16-core CPUs(Intel Gold 6142).Further,results obtained from experiments of a higher resolution model with multiple GPUs show excellent scalability.
基金supported by the National Natural Science Foundation of China with Grant No. 10772040, 50921001 and 50909016The financial support from the Important National Science & Technology Specific Projects of China with Grant No. 2008ZX05026-02 is also appreciated
文摘The Moving Particle Semi-implicit (MPS) method performs well in simulating violent free surface flow and hence becomes popular in the area of fluid flow simulation. However, the implementations of searching neighbouring particles and solving the large sparse matrix equations (Poisson-type equation) are very time-consuming. In order to utilize the tremendous power of parallel computation of Graphics Processing Units (GPU), this study has developed a GPU-based MPS model employing the Compute Unified Device Architecture (CUDA) on NVIDIA GTX 280. The efficient neighbourhood particle searching is done through an indirect method and the Poisson-type pressure equation is solved by the Bi-Conjugate Gradient (BiCG) method. Four different optimization levels for the present general parallel GPU-based MPS model are demonstrated. In addition, the elaborate optimization of GPU code is also discussed. A benchmark problem of dam-breaking flow is simulated using both codes of the present GPU-based MPS and the original CPU-based MPS. The comparisons between them show that the GPU-based MPS model outperforms 26 times the traditional CPU model.
