Design, Implementation and Performance Analysis of a CFD Task-Based Application for Heterogeneous CPU/GPU Resources

Nesi, Lucas Leandro; Schnorr, Lucas Mello; Navaux, Philippe O. A.

doi:10.1007/978-3-030-15996-2_3

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…This article is an extension of our previous work published in the VECPAR 2018 conference. The changes include the implementation of the task‐based arrow version, a complete reexecution of the experiments to evaluate previously observed performance against the new arrow version, and a discussion on the challenge to find the right granularity for CFD problems in task‐based runtimes.…”

Section: Introductionmentioning

confidence: 78%

Task‐based parallel strategies for computational fluid dynamic application in heterogeneous CPU/GPU resources

Nesi

Serpa

Schnorr

et al. 2020

Concurrency and Computation

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Summary Parallel applications executing in contemporary heterogeneous clusters are complex to code and optimize. The task‐based programming model is an alternative to handle the coding complexity. This model consists of splitting the problem domain into tasks with dependencies through a directed acyclic graph, and submit the set of tasks to a runtime scheduler that maps each task dynamically to resources. We consider that computational fluid dynamics applications are typical in scientific computing but not enough exploited by designs that employ the task‐based programming model. This article presents task‐based parallel strategies for a simple CFD application that targets heterogeneous multi‐CPU/multi‐GPU computing resources. We design, develop, evaluate, and compare the performance of three parallel strategies (naive, ghost‐cells, and arrow) of a task‐based heterogeneous (CPU and GPU) application that simulates the flow of an incompressible Newtonian fluid with constant viscosity. All implementations rely on the StarPU runtime, and we use the StarVZ toolkit to conduct comprehensive performance analysis. Results indicate that the ghost cell strategy provides the best speedup (77×) considering the simulation time when the GPU resources still have available memory. However, the arrow strategy achieves better results when the simulation data increases.

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Section: Introductionmentioning

confidence: 78%

Task‐based parallel strategies for computational fluid dynamic application in heterogeneous CPU/GPU resources

Nesi

Serpa

Schnorr

et al. 2020

Concurrency and Computation

Self Cite

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“…This paradigm represents an appealing approach for HPC-CFD problems [13,14]. The model requires annotating the code with task constructs enclosing pieces of code that will be asynchronously executed in parallel; we specify tasks data-dependencies to ensure correct execution order.…”

Section: Task Modelmentioning

confidence: 99%

Automated Generation of High-Performance Computational Fluid Dynamics Codes

Macià,

Martıínez-Ferrer,

Ayguadé

et al. 2022

Preprint

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Domain-Specific Languages (DSLs) improve programmers productivity by decoupling problem descriptions from algorithmic implementations. However, DSLs for High-Performance Computing (HPC) have two additional critical requirements: performance and scalability. This paper presents the automated process of generating, from abstract mathematical specifications of Computational Fluid Dynamics (CFD) problems, optimised parallel codes that perform and scale as manually optimised ones. We consciously combine within Saiph, a DSL for solving CFD problems, low-level optimisations and parallelisation strategies, enabling high-performance single-core executions which effectively scale to multi-core and distributed environments. Our results demonstrate how high-level DSLs can offer competitive performance by transparently leveraging state-of-the-art HPC techniques.

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CFD Code Parallelization on GPU and the Code Portability

Tomanović,

Belošević,

Milićević

et al. 2024

Advcd Theory and Sims

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Goal of this paper is to develop a fully functional parallel Computational Fluid Dynamics (CFD) code that is optimized to run on a single Graphics Processing Unit (GPU). This is achieved by writing the code in FORTRAN and OpenACC (Open Accelerators), providing them with an easily portable, platform independent code. Existing CFD code is significantly modified to allow for parallel asynchronous execution. Also, due to strong recursive dependencies in Tridiagonal Matrix Algorithm (TDMA) solver, it is replaced with Jacobi, which provides fast execution in environments with large number of parallel cores. In this research a computer code for simulation of 2D flow of water through the axisymmetric channel is used as a base for development. The parallel code is executed on GPU, single, and multicore Central Processing Unit (CPU), and the execution times are compared between platforms. Even though that Jacobi solver performs worse on single core computers, compared to its Gauss‐seidel counterpart, it is used to provide a baseline for comparison. In this work, it is shown that computation on finer grids takes less time on GPU than on CPU. The computation time increase with the number of cells in grid on GPU should follow the observed linear trend until the GPUs physical limitations are reached depending on memory size and core count.

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Design, Implementation and Performance Analysis of a CFD Task-Based Application for Heterogeneous CPU/GPU Resources

Cited by 3 publications

References 17 publications

Task‐based parallel strategies for computational fluid dynamic application in heterogeneous CPU/GPU resources

Task‐based parallel strategies for computational fluid dynamic application in heterogeneous CPU/GPU resources

Automated Generation of High-Performance Computational Fluid Dynamics Codes

CFD Code Parallelization on GPU and the Code Portability

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