A Python Framework for Fast Modelling and Simulation of Cellular Nonlinear Networks and other Finite-difference Time-domain Systems

Dogaru, Radu; Dogaru, Ioana

doi:10.1109/cscs52396.2021.00043

Cited by 4 publications

(2 citation statements)

References 15 publications

(24 reference statements)

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“…Numba enables compiling Python to machine code and achieves speeds typically only attainable by lower-level compiled languages, such as C, C++ or FORTRAN. Numba has been used across many scientific domains as both a tool to accelerate codebases and as a platform for computational research, as puth forth by authors such as [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74]. Numba is essentially a front end to LLVM, which was originally proposed as a high-performance compiler infrastructure by [75].…”

Section: Fair Cpu and Gpu Comparisonsmentioning

confidence: 99%

A GPU-Accelerated Particle Advection Methodology for 3D Lagrangian Coherent Structures in High-Speed Turbulent Boundary Layers

Lagares

Araya

2023

Energies

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In this work, we introduce a scalable and efficient GPU-accelerated methodology for volumetric particle advection and finite-time Lyapunov exponent (FTLE) calculation, focusing on the analysis of Lagrangian coherent structures (LCS) in large-scale direct numerical simulation (DNS) datasets across incompressible, supersonic, and hypersonic flow regimes. LCS play a significant role in turbulent boundary layer analysis, and our proposed methodology offers valuable insights into their behavior in various flow conditions. Our novel owning-cell locator method enables efficient constant-time cell search, and the algorithm draws inspiration from classical search algorithms and modern multi-level approaches in numerical linear algebra. The proposed method is implemented for both multi-core CPUs and Nvidia GPUs, demonstrating strong scaling up to 32,768 CPU cores and up to 62 Nvidia V100 GPUs. By decoupling particle advection from other problems, we achieve modularity and extensibility, resulting in consistent parallel efficiency across different architectures. Our methodology was applied to calculate and visualize the FTLE on four turbulent boundary layers at different Reynolds and Mach numbers, revealing that coherent structures grow more isotropic proportional to the Mach number, and their inclination angle varies along the streamwise direction. We also observed increased anisotropy and FTLE organization at lower Reynolds numbers, with structures retaining coherency along both spanwise and streamwise directions. Additionally, we demonstrated the impact of lower temporal frequency sampling by upscaling with an efficient linear upsampler, preserving general trends with only 10% of the required storage. In summary, we present a particle search scheme for particle advection workloads in the context of visualizing LCS via FTLE that exhibits strong scaling performance and efficiency at scale. Our proposed algorithm is applicable across various domains, requiring efficient search algorithms in large, structured domains. While this article focuses on the methodology and its application to LCS, an in-depth study of the physics and compressibility effects in LCS candidates will be explored in a future publication.

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Section: Fair Cpu and Gpu Comparisonsmentioning

confidence: 99%

A GPU-Accelerated Particle Advection Methodology for 3D Lagrangian Coherent Structures in High-Speed Turbulent Boundary Layers

Lagares

Araya

2023

Energies

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“…Dogaru R. and Dogaru I. [85] evaluated Numba in reaction-diffusion cellular non-linear networks. Azizi [86] utilized Numba and CuPy, among other Python-based platforms, to optimize expectation-maximization algorithms, yielding promising results.…”

Section: Introductionmentioning

confidence: 99%

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

Askar,

Yergaliyev,

Shukirgaliyev

et al. 2024

Computation

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This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport in plane-parallel geometry on three GPU cards: NVIDIA Tesla A100, Tesla V100, and GeForce RTX3080. We compared Numba and CuPy to each other and our CUDA C implementation. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks.

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Evaluation of Alternatives to Accelerate Scientific Numerical Calculations on Graphics Processing Units Using Python

Villalobos,

Meneses

2024

Communications in Computer and Information Science

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A Python Framework for Fast Modelling and Simulation of Cellular Nonlinear Networks and other Finite-difference Time-domain Systems

Cited by 4 publications

References 15 publications

A GPU-Accelerated Particle Advection Methodology for 3D Lagrangian Coherent Structures in High-Speed Turbulent Boundary Layers

A GPU-Accelerated Particle Advection Methodology for 3D Lagrangian Coherent Structures in High-Speed Turbulent Boundary Layers

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

Evaluation of Alternatives to Accelerate Scientific Numerical Calculations on Graphics Processing Units Using Python

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