Abstract
The development of parallel solutions over contemporary heterogeneous supercomputers is complex and challenging, especially for coding, performance analysis, and behavioral characterization. The task-based programming model is a possible alternative to adequately reduce the burden on the programmer. Such model consists of dividing the application into tasks with dependencies through a directed acyclic graph (DAG), and subject the DAG to a runtime scheduler that will map tasks to resources. In this paper, we present the design, development, and performance analysis of a task-based heterogeneous (CPU and GPU) application of a Computational Fluid Dynamics (CFD) problem that simulates the flow of an incompressible Newtonian fluid with constant viscosity. We implement our solution based on the StarPU runtime and use the StarVZ toolkit to conduct a comprehensive performance analysis. Results indicate that our solution provides a 6.5\(\times \) speedup compared to the serial version on the target machine using 7 CPU workers and a 60\(\times \) speedup using 5 CPU and 2 GPU workers.
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Acknowledgements
This study was financed by the National Council for Scientific and Technological Development (CNPq). We thank these projects for supporting this investigation: FAPERGS GreenCloud (16/488-9), the FAPERGS MultiGPU (16/354-8), the CNPq 447311/2014-0, the CAPES/Brafitec EcoSud 182/15, and the CAPES/Cofecub 899/18. The companion material is hosted by CERN’s Zenodo for which we are also grateful.
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Nesi, L.L., Schnorr, L.M., Navaux, P.O.A. (2019). Design, Implementation and Performance Analysis of a CFD Task-Based Application for Heterogeneous CPU/GPU Resources. In: Senger, H., et al. High Performance Computing for Computational Science – VECPAR 2018. VECPAR 2018. Lecture Notes in Computer Science(), vol 11333. Springer, Cham. https://doi.org/10.1007/978-3-030-15996-2_3
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