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AGPU-Accelerated Cartesian Grid Method for PDEs on Irregular Domain 认领 引用
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作者 Liwei Tan Minsheng Huang +2 位作者 Shuai Zhu Pan Wang Wenjun Ying 《Communications in Computational Physics》 SCIE CSCD 2025年第5期1250-1276,共27页
The kernel-free boundary integral(KFBI)method has successfully solved partial differential equations(PDEs)on irregular domains.Diverging from traditional boundary integralmethods,the computation of boundary integrals ... The kernel-free boundary integral(KFBI)method has successfully solved partial differential equations(PDEs)on irregular domains.Diverging from traditional boundary integralmethods,the computation of boundary integrals in KFBI is executed through the resolution of equivalent simple interface problems on Cartesian grids,utilizing fast algorithms.While existing implementations of KFBI methods predominantly utilize CPU platforms,GPU architecture’s superior computational capabilities and extensive memory bandwidth offer an efficient resolution to computational bottlenecks.This paper delineates the algorithms adapted for both single-GPU and multiple-GPU applications.On a single GPU,assigning individual threads can control correction,interpolation,and jump calculations.The algorithm is expanded to multiple GPUs to enhance the processing of larger-scale problems.The arrowhead decomposition method is employed in multiple-GPU settings,ensuring optimal computational efficiency and load balancing.Numerical examples show that the proposed algorithm is second-order accurate and efficient.Single-GPU solver runs 50-200 times faster than traditional CPU,and the parallel efficiency of a multiple-GPU distributed solver within the same NUMA node reaches up to 80%. 展开更多
关键词 GPU-accelerated kernel-free boundary integral method GPU parallel computing arrowhead decomposition method irregular domains
A GPU-Accelerated In-Memory Metadata Management Scheme forLarge-Scale Parallel File Systems 认领 引用 被引量：2
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作者 Zhi-Guang Chen Yu-Bo Liu +1 位作者 Yong-Feng Wang Yu-Tong Lu 《Journal of Computer Science & Technology》 SCIE EI CSCD 2021年第1期44-55,共12页
Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing nodes.Applications running on such a large-scale computing system are lik... Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing nodes.Applications running on such a large-scale computing system are likely to spawn millions of parallel processes,which usually generate a burst of I/O requests,introducing a great challenge into the metadata management of underlying parallel file systems.The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner,which will inevitably confront with serious network and consistence problems.This work instead pursues to enhance the metadata performance in the scale-up manner.Specifically,we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel.Our proposal designs a novel metadata server architecture,which employs CPU to interact with file system clients,while offloading the computing tasks about metadata into GPU.To take full advantages of the parallelism existing in GPU,we redesign the in-memory data structure for the name space of file systems.The new data structure can perfectly fit to the memory architecture of GPU,and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently.We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal,and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50%under typical metadata operations.The superiority is strengthened further on high concurrent scenarios,e.g.,the high-performance computing systems supporting millions of parallel threads. 展开更多
关键词 GPU-accelerated in-memory metadata management parallel file system
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Fast compression of computer-generated holographic images based on a GPU-accelerated skip-dimension vector quantization method 认领 引用 被引量：1
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作者 Y.K.Lam W.C.Situ P.W.M.Tsang 《Chinese Optics Letters》 SCIE EI CAS CSCD 2013年第5期28-32,共5页
A method for fast and low bit-rate compression of digital holograms based on a new vector quantization （VQ） method known as the skip-dimension VQ （SDVQ） is proposed. Briefly, a complex hologram is converted into a... A method for fast and low bit-rate compression of digital holograms based on a new vector quantization （VQ） method known as the skip-dimension VQ （SDVQ） is proposed. Briefly, a complex hologram is converted into a real off-axis hologram, and partitioned into a set of image vectors. The image vectors are passed into a graphic processing unit （GPU）, and compressed through SDVQ into a set of code indices considerably smaller in data size than the source hologram. Experimental evaluation reveals that our scheme is capable of compressing a digital hologram to a compression ratio of over 500 times, in approximately 20-22 ms. 展开更多
关键词 GPU Fast compression of computer-generated holographic images based on a GPU-accelerated skip-dimension vector quantization method VQ
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A GPU-Accelerated Cartesian Grid Method for the Heat,Wave and Schr¨odinger Equations on Irregular Domains 认领 引用
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作者 Liwei Tan Minsheng Huang Wenjun Ying 《Communications in Computational Physics》 SCIE 2024年第7期485-520,共36页
Based on Ying’s kernel-free boundary integral(KFBI)method[1],a secondorder method for general elliptic partial differential equations(PDEs),this paper develops a GPU-accelerated KFBI method for the heat,wave and Schr... Based on Ying’s kernel-free boundary integral(KFBI)method[1],a secondorder method for general elliptic partial differential equations(PDEs),this paper develops a GPU-accelerated KFBI method for the heat,wave and Schr¨odinger equations on the irregular domain.Since the limitation of time steps imposed by CFL conditions in the explicit scheme and the inadequate accuracy generated by the fully implicit scheme for the Laplacian operator,the paper selects a series of second-order time discrete schemes,and the Laplacian operator is split into explicit and implicit mixed ones.The Crank-Nicolson method is used to discretize the heat equation in temporal dimension while the implicitθ-scheme is for the wave equation.The Strang splitting method is applied to the Schr¨odinger equation.After discretizing the temporal dimension implicitly,the heat,wave and Schr¨odinger equations are transformed into a sequence of elliptic equations.The Laplacian operator on the right-hand side of the elliptic equation is obtained from the numerical scheme instead of being discretized and corrected by the five-point difference method.A Cartesian grid-based KFBI method is used to solve the resulting elliptic equations.The KFBI method is accelerated by the graphics processing unit(GPU)with a parallel Cartesian grid solver,achieving a high degree of parallelism.Numerical results show that the proposed method has a second-order accuracy for the heat,wave,and Schr¨odinger equations.Additionally,the GPU-accelerated solvers for the three types of time-dependent equations are 30 times faster than CPU-based solvers. 展开更多
关键词 GPU-accelerated kernel-free boundary integral method time discretization scheme irregular domains
A Multi-Scale Molecular Dynamics Scheme/Toolkit for Polymer Modeling 认领 引用
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作者 Miaolan Zhen Mingyang Li +4 位作者 Jialei Xu Rui Shi Hujun Qian Youliang Zhu Zhongyuan Lu 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第2期145-158,I0169,共14页
Multi-scale modeling of polymer systems remains a fundamental challenge in materials sci-ence due to the strongly coupled structures and dynamics across atomic,mesoscopic,and macroscopic scales.Here,we present PyGAMD-... Multi-scale modeling of polymer systems remains a fundamental challenge in materials sci-ence due to the strongly coupled structures and dynamics across atomic,mesoscopic,and macroscopic scales.Here,we present PyGAMD-ChemFAST(Python GPU-accelerated molecular dynamics-chemical-friendly automated simulation toolkit),an integrated modeling and simulation framework that combines a molecular simulation platform with an automated modeling and processing toolkit.The framework supports bidirectional cross-scale modeling,integrating the mapping from all-atom(AA)to coarse-grained(CG)models in coarse-grain-ing and from CG back to AA in fine-graining,while automating the entire workflow from model parameterization through dynamics simulation to property analysis.It accommodates complex polymer topologies and ensures cross-scale force field compatibility.By unifying modeling and simulation in a programmable environment,PyGAMD-ChemFAST enhances computational efficiency and ensures parameter consistency,especially in studying phase sep-aration,glass transition,crystallization,and mechanical behavior.This integrated framework provides an efficient and reliable platform for high-throughput,multi-scale polymer research. 展开更多
关键词 Polymer modeling Multi-scale simulation Python GPU-accelerated molecular dynamics(PyGAMD) Chemical-friendly automated simulation toolkit(ChemFAST)
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Advancement in CFD and Responsive AI to Examine Cardiovascular Pulsatile Flow in Arteries:A Review 认领 引用 被引量：1
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作者 Priyambada Praharaj Chandrakant R.Sonawane Arunkumar Bongale 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第12期2021-2064,共44页
This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics(CFD)in biomedical applications.Beyond its various engineering applications,CFD has started... This paper represents a detailed and systematic review of one of the most ongoing applications of computational fluid dynamics(CFD)in biomedical applications.Beyond its various engineering applications,CFD has started to establish a presence in the biomedical field.Cardiac abnormality,a familiar health issue,is an essential point of investigation by research analysts.Diagnostic modalities provide cardiovascular structural information but give insufficient information about the hemodynamics of blood.The study of hemodynamic parameters can be a potential measure for determining cardiovascular abnormalities.Numerous studies have explored the rheological behavior of blood experimentally and numerically.This paper provides insight into how researchers have incorporated the pulsatile nature of the blood experimentally,numerically,or through various simulations over the years.It focuses on how machine learning platforms derive outputs based on mass and momentum conservation to predict the velocity and pressure profile,analyzing various cardiac diseases for clinical applications.This will pave the way toward responsive AI in cardiac healthcare,improving productivity and quality in the healthcare industry.The paper shows how CFD is a vital tool for efficiently studying the flow in arteries.The review indicates this biomedical simulation and its applications in healthcare using machine learning and AI.Developing AI-based CFD models can impact society and foster the advancement towards responsive AI. 展开更多
关键词 Pulsatile flow cardiovascular CFD simulation patient-specific GPU-accelerated responsive AI
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PyGAMD:Python graphics processing unit-accelerated molecular dynamics software 认领 引用
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作者 Jialei Xu Shenghong Guo +4 位作者 Miaolan Zhen Zhuochen Yu Youliang Zhu Giuseppe Milano Zhongyuan Lu 《Materials Genome Engineering Advances》 CAS CSCD 2025年第2期74-85,共12页
PyGAMD(Python GPU-accelerated molecular dynamics software)is a molecular simulation platform developed from scratch.It is designed for soft matter,especially for polymer by integrating coarse-grained/multi-scale model... PyGAMD(Python GPU-accelerated molecular dynamics software)is a molecular simulation platform developed from scratch.It is designed for soft matter,especially for polymer by integrating coarse-grained/multi-scale models,methods,and force fields.It essentially includes an interpreter of molecular dynamics(MD)which supports secondary programming so that users can write their own functions by themselves,such as analytical potential forms for nonbonded,bond,angle,and dihedral interactions in an easy way,greatly extending the flexibility of MD simulations.The interpreter is written by pure Python language,making it easy to be modified and further developed.Some built-in libraries written by other languages that have been compiled for Python are added into PyGAMD to extend it's features,including configuration initialization,property analysis,etc.Machine learning force fields that are trained by DeePMD-kit are supported by PyGAMD for conveniently implementing multi-scale modeling and simulations.By designing an advanced framework of software,graphics processing unitacceleration achieved by the Numba library of Python and compute unified device architecture reaches a high computing efficiency. 展开更多
关键词 GPU-acceleration interpreter molecular dynamics multi-scale polymer soft matter
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