Implementation of Cubic Spline Interpolation on Parallel Skeleton Using Pipeline Model on CPU-GPU Cluster

Mohanty, Prasant; Reza, Motahar; Kumar, Piyush; Kumar, Praveen

doi:10.1109/iacc.2016.143

Cited by 12 publications

(7 citation statements)

References 7 publications

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“…Although the GPU accelerated Kriging interpolation algorithm [27], cubic spline interpolation [28] and bilinear interpolation [29] have been studied in the literature, the best linear unbiased estimation capabilities provided by Kriging interpolation brings extra computation. Since the interpolation calculation result of the aerodynamic coefficient only affects the guidance and control system in the current step, the guidance system tracks the standard trajectory during each guidance period, so no cumulative error occurs.…”

Section: Parallel Algorithm Design Based On Cuda a Parallel Intmentioning

confidence: 99%

Real-Time Simulation and Optimization of Elastic Aircraft Vehicle Based on Multi-GPU Workstation

2019

IEEE Access

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Modern aircraft such as missile and rocket, due to the large slenderness ratio of slender body vehicles, the influence of elastic deformation and vibration on navigation, guidance, and engine modules in simulation can not be ignored. For the problems of slow calculation speed and incapability of real-time simulation for time-domain simulation, by analyzing the time proportion of each calculation step under different computing scale, the dynamic parallel construction of octree is used to represent the aerodynamic parameter table under the environment of single and multi GPU. Meanwhile, an innovative parallel algorithm of element stiffness matrix based on finite element model is designed in GPU architecture. Accordingly, the optimized performance is enhanced through the adaptive hardware resources and rational use of shared memory. Furthermore, A multi-threaded asynchronous framework based on task queue and thread pool is proposed to realize the parallel task calculation with different granularities. The numerical result shows that the acceleration ratio of about 20 times in the single GPU condition can be obtained, and the acceleration ratio of at least 30 times can be obtained by the parallel computing of dual GPUs, enabling the real-time simulation of the flexible aircraft with 1200 elements within 20ms. INDEX TERMS Flexible aircraft, parallel computing, parallel algorithms, heterogeneous computing, Octree.

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Section: Parallel Algorithm Design Based On Cuda a Parallel Intmentioning

confidence: 99%

Real-Time Simulation and Optimization of Elastic Aircraft Vehicle Based on Multi-GPU Workstation

2019

IEEE Access

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“…Mei et al [10] proposed an improved GPU-based adaptive inverse distance weighting interpolation algorithm for k-nearest neighbors search, with a speedup of 1000+. Mohanty et al [11] proposed a parallel interpolation component known as skeleton, which can be implemented in several parallel paradigms, such as OpenMP, MPI, and CUDA.…”

Section: Introductionmentioning

confidence: 99%

Parallel Computing Method of Commonly Used Interpolation Algorithms for Remote Sensing Images

Fan,

Zuo,

Zhou

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Parallel computing is a common method to accelerate remote sensing image processing. The paper briefly describes six commonly used interpolation functions and studies three commonly used parallel computing methods of the corresponding nine interpolation algorithms in remote sensing image processing. Firstly, two kinds of general parallel interpolation algorithms (for CPU and GPU respectively) were designed. Then, in two typical application scenarios (data-intensive and computingintensive), four computing methods (one serial method and three parallel methods) of these interpolation algorithms were tested. Finally, the acceleration effects of all parallel algorithms were compared and analyzed. On the whole, the acceleration effect of parallel interpolation algorithm is better in computer-intensive scenario. In CPU-oriented methods, the speedup of all parallel interpolation algorithms mainly depends on the number of physical cores of CPU, while in GPU-oriented methods, a speedup is greatly affected by the computation complexity of an algorithm and the application scenario. GPU has a better acceleration effect on the interpolation algorithms with bigger computation complexity, and has more advantages in the computing-intensive scenarios. In most cases, GPU-based interpolation is ideal for efficient interpolation.

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“…Also, in the field of gravitational wave data analysis, much work is done simplifying complex waveforms and analyses to achieve results in a physically reasonable and desirable amount of time. A large portion of this work involves using cubic splines for interpolation [13], a highly parallelizable process that has shown promising results using GPUs. Yet further examples of areas using GPUs include image in-painting [14], simulations of the human brain [15,16], and animation [17].…”

Section: Introductionmentioning

confidence: 99%

A Batched GPU Methodology for Numerical Solutions of Partial Differential Equations

Carroll¹,

Gloster²,

Bustamante³

et al. 2021

Preprint

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In this paper we present a methodology for data accesses when solving batches of Tridiagonal and Pentadiagonal matrices that all share the same left-hand-side (LHS) matrix. The intended application is to the numerical solution of Partial Differential Equations via the finite-difference method, although the methodology is applicable more broadly. By only storing one copy of this matrix, a significant reduction in storage overheads is obtained, together with a corresponding decrease in compute time. Taken together, these two performance enhancements lead to an overall more efficient implementation over the current state of the art algorithms cuThomasBatch and cuPentBatch, allowing for a greater number of systems to be solved on a single GPU. We demonstrate the methodology in the case of the Diffusion Equation, Hyperdiffusion Equation, and the Cahn-Hilliard Equation, all in one spatial dimension. In this last example, we demonstrate how the method can be used to perform 2 20 independent simulations of phase separation in one dimension. In this way, we build up a robust statistical description of the coarsening phenomenon which is the defining behavior of phase separation. We anticipate that the method will be of further use in other similar contexts requiring statistical simulation of physical systems.

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Implementation of Cubic Spline Interpolation on Parallel Skeleton Using Pipeline Model on CPU-GPU Cluster

Cited by 12 publications

References 7 publications

Real-Time Simulation and Optimization of Elastic Aircraft Vehicle Based on Multi-GPU Workstation

Real-Time Simulation and Optimization of Elastic Aircraft Vehicle Based on Multi-GPU Workstation

Parallel Computing Method of Commonly Used Interpolation Algorithms for Remote Sensing Images

A Batched GPU Methodology for Numerical Solutions of Partial Differential Equations

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