[2] On the robustness and performance of entropy stable discontinuous collocation methods for the compressible Navier-Stokes equations, ROjas .
et.al.
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