Is phase 1 the old method and 2 the new? Is this 128^3 mesh per process? On Sun, Mar 7, 2021 at 7:27 AM Stefano Zampini <[email protected]> wrote:
> > > [2] On the robustness and performance of entropy stable discontinuous >> collocation methods for the compressible Navier-Stokes equations, ROjas . >> et.al. >> https://arxiv.org/abs/1911.10966 >> > > This is not the proper reference, here is the correct one > https://www.sciencedirect.com/science/article/pii/S0021999120306185?dgcid=rss_sd_all > However, there the algorithm is only outlined, and performances related to > the mesh distribution are not really reported. > We observed a large gain for large core counts and one to all > distributions (from minutes to seconds) by splitting the several > communication rounds needed by DMPlex into stages: from rank 0 to 1 rank > per node, and then decomposing independently within the node. > Attached the total time for one-to-all DMPlexDistrbute for a 128^3 mesh > > >> >> >>> ? >>> >>> The attached plots suggest (A), (B), and (C) is happening for >>> Cahn-Hilliard problem (from firedrake-bench repo) on a 2D 8Kx8K >>> unit-square mesh. The implementation is here [1]. Versions are >>> Firedrake, PyOp2: 20200204.0; PETSc 3.13.1; ParMETIS 4.0.3. >>> >>> Two questions, one on (A) and the other on (B)+(C): >>> >>> 1. Is (A) result expected? Given (A), any effort to improve the quality >>> of the compiled assembly kernels (or anything else other than mesh >>> distribution) appears futile since it takes 1% of end-to-end execution >>> time, or am I missing something? >>> >>> 1a. Is mesh distribution fundamentally necessary for any FEM framework, >>> or is it only needed by Firedrake? If latter, then how do other >>> frameworks partition the mesh and execute in parallel with MPI but avoid >>> the non-scalable mesh destribution step? >>> >>> 2. Results (B) and (C) suggest that the mesh distribution step does >>> not scale. Is it a fundamental property of the mesh distribution problem >>> that it has a central bottleneck in the master process, or is it >>> a limitation of the current implementation in PETSc-DMPlex? >>> >>> 2a. Our (B) result seems to agree with Figure 4(left) of [2]. Fig 6 of >>> [2] >>> suggests a way to reduce the time spent on sequential bottleneck by >>> "parallel mesh refinment" that creates high-resolution meshes from an >>> initial coarse mesh. Is this approach implemented in DMPLex? If so, any >>> pointers on how to try it out with Firedrake? If not, any other >>> directions for reducing this bottleneck? >>> >>> 2b. Fig 6 in [3] shows plots for Assembly and Solve steps that scale >>> well up >>> to 96 cores -- is mesh distribution included in those times? Is anyone >>> reading this aware of any other publications with evaluations of >>> Firedrake that measure mesh distribution (or explain how to avoid or >>> exclude it)? >>> >>> Thank you for your time and any info or tips. >>> >>> >>> [1] >>> https://github.com/ISI-apex/firedrake-bench/blob/master/cahn_hilliard/firedrake_cahn_hilliard_problem.py >>> >>> [2] Unstructured Overlapping Mesh Distribution in Parallel, Matthew G. >>> Knepley, Michael Lange, Gerard J. Gorman, 2015. >>> https://arxiv.org/pdf/1506.06194.pdf >>> >>> [3] Efficient mesh management in Firedrake using PETSc-DMPlex, Michael >>> Lange, Lawrence Mitchell, Matthew G. Knepley and Gerard J. Gorman, SISC, >>> 38(5), S143-S155, 2016. http://arxiv.org/abs/1506.07749 >>> >> > > -- > Stefano >
