Software Cost Analysis of GPU-Accelerated Aeroacoustics Simulations in C++ with OpenACC

Nicolini, M.; Miller, Julian; Wienke, Sandra; Schlottke-Lakemper, Michael; Meinke, Matthias; Müller, Matthias S.

doi:10.1007/978-3-319-46079-6_36

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The collected productivity data of the students opens up wide areas of research into HPC programming productivity such as the estimation of software costs of HPC projects. While most of this research is ongoing and will require more data, some early results can be found in [2,3,6]. Figure 2 provides an example for the analyses carried out on the productivity data collected during the OpenMP lab in summer 2019.…”

Section: Evaluation Of the Labsmentioning

confidence: 99%

Self-paced Learning in HPC Lab Courses

Terboven¹,

Miller²,

Wienke³

et al. 2020

JOCSE

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In a software lab, groups of students develop parallel code using modern tools, document the results and present their solutions. The learning objectives include the foundations of High-Performance Computing (HPC), such as the understanding of modern architectures, the development of parallel programming skills, and coursespecic topics, like accelerator programming or cluster setup. In order to execute the labs successfully with limited personnel resources and still provide students with access to world-class HPC architectures, we developed a set of concepts to motivate students and to track their progress. This includes the learning status survey and the developer diary, which are presented in this work. We also report on our experiences with using innovative teaching concepts to incentivize students to optimize their codes, such as using competition among the groups. Our concepts enable us to track the eectiveness of our labs and to steer them for increasing sizes of diverse students. We conclude that software labs are eective in adding practical experiences to HPC education. Our approach to hand out open tasks and to leave creative freedom in implementing the solutions enables the students to self-pace their learning process and to vary their investment of eort during the semester. Our eort and progress tracking ensures the achieving of the extensive learning objectives and enables our research on HPC programming productivity.

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Section: Evaluation Of the Labsmentioning

confidence: 99%

Self-paced Learning in HPC Lab Courses

Terboven¹,

Miller²,

Wienke³

et al. 2020

JOCSE

Self Cite

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“…CFD solver for the compressible Navier-Stokes equations based on a third-order reconstructed discontinuous Galerkin method, which was parallelized on a single GPU using OpenACC directives was presented in [9]. OpenACC parallelization techniques, applied to an aeroacoustics simulation code, are addressed in [10] for C++ codes. [11] accelerated a 2D solver for the Euler equations based on the MUSCL and NND algorithms on GPUs.…”

Section: Introductionmentioning

confidence: 99%

GPU-acceleration of A High Order Finite Difference Code Using Curvilinear Coordinates

Kupiainen

Gong

Axner

et al. 2020

Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things

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GPU-accelerated computing is becoming a popular technology due to the emergence of techniques such as OpenACC, which makes it easy to port codes in their original form to GPU systems using compiler directives, and thereby speeding up computation times relatively simply. In this study we have developed an OpenACC implementation of the high order finite difference CFD solver ESSENSE for simulating compressible flows. The solver is based on summation-by-part form difference operators, and the boundary and interface conditions are weakly implemented using simultaneous approximation terms. This case study focuses on porting code to GPUs for the most time-consuming parts namely sparse matrix vector multiplications and the evaluations of fluxes. The resulting OpenACC implementation is used to simulate the Taylor-Green vortex which produces a maximum speed-up of 61.3 on a single V100 GPU by compared to serial CPU version.

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