NAS Parallel Benchmark Kernels with Python: A performance and programming effort analysis focusing on GPUs

Domenico, Daniel Di; Cavalheiro, Gerson Geraldo H.; Lima, João Vicente Ferreira

doi:10.1109/pdp55904.2022.00013

Cited by 7 publications

(2 citation statements)

References 21 publications

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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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“…A recent review and comparison of previous work on NPB parallelizations is given by Löff et al [27]. There are also research efforts porting NPB to higher-level programming languages such as Python, providing parallel implementations for GPUs by using Numba (an LLVM-based Python JIT compiler) [18].…”

Section: Related Workmentioning

confidence: 99%

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

Ernstsson

Griebler

Keßler

2022

Int J Parallel Prog

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We analyze the performance portability of the skeleton-based, single-source multi-backend high-level programming framework SkePU across multiple different CPU–GPU heterogeneous systems. Thereby, we provide a systematic application efficiency characterization of SkePU-generated code in comparison to equivalent hand-written code in more low-level parallel programming models such as OpenMP and CUDA. For this purpose, we contribute ports of the STREAM benchmark suite and of a part of the NAS Parallel Benchmark suite to SkePU. We show that for STREAM and the EP benchmark, SkePU regularly scores efficiency values above 80% and in particular for CPU systems, SkePU can outperform hand-written code.

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NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs

2022

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NAS Parallel Benchmark Kernels with Python: A performance and programming effort analysis focusing on GPUs

Cited by 7 publications

References 21 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

Assessing Application Efficiency and Performance Portability in Single-Source Programming for Heterogeneous Parallel Systems

NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs

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