Fabian Orland’s research while affiliated with RWTH Aachen University and other places

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Publications (4)


A Case Study on Coupling OpenFOAM with Different Machine Learning Frameworks
  • Conference Paper

November 2022

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64 Reads

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1 Citation

Fabian Orland

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Kim Sebastian Brose

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[...]

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Fig. 1. Trace visualization of our GMRES kernel program using 8 OpenMP threads. Different colors correspond to different operations performed by the GMRES solver (Color figure online).
Fig. 3. Trace comparison of our reference GMRES kernel (top) and the conditional nested parallel regions version (bottom) both using 8 OpenMP threads.
Load balance metrics obtained with our reference kernel using 8 to 48 OpenMP threads. We measured POP load balance efficiency, standard deviation, skew- ness and kurtosis based on runtime and GMRES iterations.
Performance results of our tasking approach with 8 OpenMP threads. Tasks per thread Load balance instructions time (accumulated)
Load balance metrics obtained with our kernel implementing nested tasks using 8 to 48 OpenMP threads. We measured POP load balance efficiency, standard deviation, skewness, kurtosis and the speedup compared to the reference kernel.
A Case Study on Addressing Complex Load Imbalance in OpenMP
  • Chapter
  • Full-text available

September 2020

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105 Reads

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4 Citations

Lecture Notes in Computer Science

Load balance is an important factor that fundamentally impacts the scalability of any parallel application. In this paper we present a case study to address a complex load imbalance related to the convergence behavior of the parallel SPMD implementation of a GMRES solver used in a real world application in the field of computational fluid dynamics. In order to tackle this load imbalance in OpenMP we illustrate different approaches involving the use of nested tasks as well as nested parallel regions. Furthermore, we evaluate these approaches on a small kernel program extracted from the original application code and show how the load balance is affected by each of these approaches.

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Citations (1)


... The critical insight to remember is that the performance of a specific workflow is substantially influenced not only by the efficiency of its elements but also by the way those elements are interconnected and integrated. Several studies have attempted to investigate how the coupling of workflow components affects overall performance [100,101,102,103,104,105,106]. Several studies have attempted to investigate how the coupling of workflow components affects overall performance [100,101,102,103,104,105,106]. ...

Reference:

AI-coupled HPC Workflow Applications, Middleware and Performance
A Case Study on Coupling OpenFOAM with Different Machine Learning Frameworks
  • Citing Conference Paper
  • November 2022