A mesh partitioning algorithm for preserving spatial locality in arbitrary geometries

Nivarti, Girish V.; Salehi, M T Zahraei; Bushe, W. Kendal

doi:10.1016/j.jcp.2014.10.022

Cited by 23 publications

(3 citation statements)

References 39 publications

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“…Analogously, the curve 𝐶 also has an order 𝑚 and its length equals to the total number of 𝑛2 𝑚 cells. Space-filling curves are used in task decomposition in parallel computing (Nivarti et al, 2015;Xia and Liang, 2016). The nD SFC key can be applied in combination with spherical coordinate reference systems, which is getting close to spatial representations based on discrete global grid structures (Sahr et al, 2003;Sirdeshmukh et al, 2019).…”

Section: Sfc Based Point Cloud Organizationmentioning

confidence: 99%

Organizing and visualizing point clouds with continuous levels of detail

Oosterom

Liu

et al. 2022

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Sfc Based Point Cloud Organizationmentioning

confidence: 99%

Organizing and visualizing point clouds with continuous levels of detail

Oosterom

Liu

et al. 2022

ISPRS Journal of Photogrammetry and Remote Sensing

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“…They exhibit good locality preservation properties that make them useful for partitioning or reordering data and computations [1,2]. Therefore, SFCs have been widely used in a number of applications, including parallel computing [3,4], file storage [5], database indexing [6][7][8], and image retrieval [9,10]. SFCs have been also proven a useful solution for massive points management [11].…”

Section: Introductionmentioning

confidence: 99%

A Parallel N-Dimensional Space-Filling Curve Library and Its Application in Massive Point Cloud Management

Guan

Oosterom

Cheng

2018

IJGI

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Because of their locality preservation properties, Space-Filling Curves (SFC) have been widely used in massive point dataset management. However, the completeness, universality, and scalability of current SFC implementations are still not well resolved. To address this problem, a generic n-dimensional (nD) SFC library is proposed and validated in massive multiscale nD points management. The library supports two well-known types of SFCs (Morton and Hilbert) with an object-oriented design, and provides common interfaces for encoding, decoding, and nD box query. Parallel implementation permits effective exploitation of underlying multicore resources. During massive point cloud management, all xyz points are attached an additional random level of detail (LOD) value l. A unique 4D SFC key is generated from each xyzl with this library, and then only the keys are stored as flat records in an Oracle Index Organized Table (IOT). The key-only schema benefits both data compression and multiscale clustering. Experiments show that the proposed nD SFC library provides complete functions and robust scalability for massive points management. When loading 23 billion Light Detection and Ranging (LiDAR) points into an Oracle database, the parallel mode takes about 10 h and the loading speed is estimated four times faster than sequential loading. Furthermore, 4D queries using the Hilbert keys take about 1~5 s and scale well with the dataset size.

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“…Trends in high performance computing have led to large increases in the degree of parallelism available to solve large problems more quickly. With these trends in hardware, researchers have been developing sophisticated implementations of a broad suite of existing algorithms (such as discontinuous Galerkin methods [1], mesh partitioning schemes [2], or other partial differential equation techniques [3]) for a wide variety of physics including computational fluid dynamics [4], elastodynamics and acoustics [3], plasma simulation [5], and astronomy [6], to make better use of the new hardware. Many Integrated Core (MIC) computing architectures are creating opportunities to achieve parallelism within a compute node by increasing both the on-chip and in-core level of parallelism, while creating lower cost computing options compared to traditional CPU cluster computing.…”

Section: Introductionmentioning

confidence: 99%

Optimization of elastodynamic finite integration technique on Intel Xeon Phi Knights Landing processors

Schneck

Gregory

Leckey

2018

Journal of Computational Physics

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This work describes the development and optimization of an implementation of an isotropic elastodynamic finite integration technique (EFIT) code for parallelized computation on Intel Knights Landing (KNL) hardware. EFIT is a numerical approach resulting in standard staggered-grid finite difference equations for the elastodynamic equations of motion to simulate bulk waves is solids. The computationally efficient simulation of elastodynamic wave propagation and interactions in aerospace materials is of high-interest in the fields of nondestructive evaluation (NDE) and structural health monitoring (SHM). Ultrasonic inspection uses an ultrasonic signal, generated at the surface of the material/structure via use of a piezoelectric transducer, to propagate sound waves into the material where it interacts with any existing defects, as well as with structural boundaries and any material inhomogeneity. Reflections from defects and boundaries are then measured by a transducer. Realistic ultrasound simulation tools can significantly aid the development and optimization of inspection techniques and can assist in the interpretation of experimental data.The optimization of an elastodynamics simulation code for the KNL Many Integrated Core processor was performed. The optimization focused on data locality and vectorization. Results show that tiling of the data to exploit the cache behavior and allow for significant utilization of the KNL hardware. The MPI implementation allows for a scalable implementation enabling large problems to be simulated. The model results were validated against theoretical dispersion curves to within 2% of the group velocity, and within 0.5% of the phase velocity of the A0 mode. Aggressive use of tiling, threading, and vectorization techniques allowed for dramatically improved time to solution.

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A mesh partitioning algorithm for preserving spatial locality in arbitrary geometries

Cited by 23 publications

References 39 publications

Organizing and visualizing point clouds with continuous levels of detail

Organizing and visualizing point clouds with continuous levels of detail

A Parallel N-Dimensional Space-Filling Curve Library and Its Application in Massive Point Cloud Management

Optimization of elastodynamic finite integration technique on Intel Xeon Phi Knights Landing processors

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