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https://github.com/ceed/laghos

High-order Lagrangian Hydrodynamics Miniapp
https://github.com/ceed/laghos

ceed finite-elements high-order hpc hydrodynamics lagrangian miniapp proxy-application

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High-order Lagrangian Hydrodynamics Miniapp

Host: GitHub
URL: https://github.com/ceed/laghos
Owner: CEED
License: bsd-2-clause
Created: 2017-06-27T18:24:26.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2024-10-29T19:03:41.000Z (3 months ago)
Last Synced: 2024-10-29T20:21:26.665Z (3 months ago)
Topics: ceed, finite-elements, high-order, hpc, hydrodynamics, lagrangian, miniapp, proxy-application
Language: C++
Homepage: http://ceed.exascaleproject.org/miniapps
Size: 15.1 MB
Stars: 185
Watchers: 36
Forks: 60
Open Issues: 8
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG
- License: LICENSE

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README

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High-order Lagrangian Hydrodynamics Miniapp

[![Build Status](https://travis-ci.org/CEED/Laghos.svg?branch=master)](https://travis-ci.org/CEED/Laghos)
[![Build and Test (GH Actions)](https://github.com/CEED/Laghos/workflows/build-and-test-laghos/badge.svg?branch=master)](https://github.com/CEED/Laghos/actions?query=workflow%3Abuild-and-test-laghos)

## Purpose

**Laghos** (LAGrangian High-Order Solver) is a miniapp that solves the
time-dependent Euler equations of compressible gas dynamics in a moving
Lagrangian frame using unstructured high-order finite element spatial
discretization and explicit high-order time-stepping.

Laghos is based on the discretization method described in the following article:

> V. Dobrev, Tz. Kolev and R. Rieben

> [High-order curvilinear finite element methods for Lagrangian hydrodynamics](https://doi.org/10.1137/120864672)

> *SIAM Journal on Scientific Computing*, (34) 2012, pp. B606–B641.

Laghos captures the basic structure of many compressible shock hydrocodes,
including the [BLAST code](http://llnl.gov/casc/blast) at [Lawrence Livermore
National Laboratory](http://llnl.gov). The miniapp is built on top of a general
discretization library, [MFEM](http://mfem.org), thus separating the pointwise
physics from finite element and meshing concerns.

The Laghos miniapp is part of the [CEED software suite](http://ceed.exascaleproject.org/software),
a collection of software benchmarks, miniapps, libraries and APIs for
efficient exascale discretizations based on high-order finite element
and spectral element methods. See http://github.com/ceed for more
information and source code availability.

The CEED research is supported by the [Exascale Computing Project](https://exascaleproject.org/exascale-computing-project)
(17-SC-20-SC), a collaborative effort of two U.S. Department of Energy
organizations (Office of Science and the National Nuclear Security
Administration) responsible for the planning and preparation of a
[capable exascale ecosystem](https://exascaleproject.org/what-is-exascale),
including software, applications, hardware, advanced system engineering and early
testbed platforms, in support of the nation’s exascale computing imperative.

## Characteristics

The problem that Laghos is solving is formulated as a big (block) system of
ordinary differential equations (ODEs) for the unknown (high-order) velocity,
internal energy and mesh nodes (position). The left-hand side of this system of
ODEs is controlled by *mass matrices* (one for velocity and one for energy),
while the right-hand side is constructed from a *force matrix*.

Laghos supports two options for deriving and solving the ODE system, namely the
*full assembly* and the *partial assembly* methods. Partial assembly is the main
algorithm of interest for high orders. For low orders (e.g. 2nd order in 3D),
both algorithms are of interest.

The full assembly option relies on constructing and utilizing global mass and
force matrices stored in compressed sparse row (CSR) format. In contrast, the
[partial assembly](http://ceed.exascaleproject.org/ceed-code) option defines
only the local action of those matrices, which is then used to perform all
necessary operations. As the local action is defined by utilizing the tensor
structure of the finite element spaces, the amount of data storage, memory
transfers, and FLOPs are lower (especially for higher orders).

The Laghos implementation includes support for hardware devices, such
as GPUs, and programming models, such as CUDA, OCCA, RAJA and OpenMP,
based on [MFEM](http://mfem.org), version 4.1 or later. These device
backends are selectable at runtime, see the `-d/--device` command-line
option.

Other computational motives in Laghos include the following:

- Support for unstructured meshes, in 2D and 3D, with quadrilateral and
hexahedral elements (triangular and tetrahedral elements can also be used, but
with the less efficient full assembly option). Serial and parallel mesh
refinement options can be set via a command-line flag.
- Explicit time-stepping loop with a variety of time integrator options. Laghos
supports Runge-Kutta ODE solvers of orders 1, 2, 3, 4 and 6, as well as a
specialized Runge-Kutta method of order 2 that ensures exact energy
conservation on fully discrete level (RK2Avg).
- Continuous and discontinuous high-order finite element discretization spaces
of runtime-specified order.
- Moving (high-order) meshes.
- Separation between the assembly and the quadrature point-based computations.
- Point-wise definition of mesh size, time-step estimate and artificial
viscosity coefficient.
- Constant-in-time velocity mass operator that is inverted iteratively on
each time step. This is an example of an operator that is prepared once (fully
or partially assembled), but is applied many times. The application cost is
dominant for this operator.
- Time-dependent force matrix that is prepared every time step (fully or
partially assembled) and is applied just twice per "assembly". Both the
preparation and the application costs are important for this operator.
- Domain-decomposed MPI parallelism.
- Optional in-situ visualization with [GLVis](http:/glvis.org) and data output
for visualization and data analysis with [VisIt](http://visit.llnl.gov).

## Code Structure

- The file `laghos.cpp` contains the main driver with the time integration loop
starting around line 609.
- In each time step, the ODE system of interest is constructed and solved by
the class `LagrangianHydroOperator`, defined around line 544 of `laghos.cpp`
and implemented in files `laghos_solver.hpp` and `laghos_solver.cpp`.
- All quadrature-based computations are performed in the function
`LagrangianHydroOperator::UpdateQuadratureData` in `laghos_solver.cpp`.
- Depending on the chosen option (`-pa` for partial assembly or `-fa` for full
assembly), the function `LagrangianHydroOperator::Mult` uses the corresponding
method to construct and solve the final ODE system.
- The full assembly computations for all mass matrices are performed by the MFEM
library, e.g., classes `MassIntegrator` and `VectorMassIntegrator`. Full
assembly of the ODE's right hand side is performed by utilizing the class
`ForceIntegrator` defined in `laghos_assembly.hpp`.
- The partial assembly computations are performed by the classes
`ForcePAOperator` and `MassPAOperator` defined in `laghos_assembly.hpp`.
- When partial assembly is used, the main computational kernels are the
`Mult*` functions of the classes `MassPAOperator` and `ForcePAOperator`
implemented in file `laghos_assembly.cpp`. These functions have specific
versions for quadrilateral and hexahedral elements.
- The orders of the velocity and position (continuous kinematic space)
and the internal energy (discontinuous thermodynamic space) are given
by the `-ok` and `-ot` input parameters, respectively.

## Building

Laghos has the following external dependencies:

- *hypre*, used for parallel linear algebra, we recommend version 2.11.2

https://github.com/hypre-space/hypre/releases/tag/v2.11.2

- METIS, used for parallel domain decomposition (optional), we recommend [version 4.0.3](https://github.com/mfem/tpls/blob/gh-pages/metis-4.0.3.tar.gz)

https://github.com/mfem/tpls

- MFEM, used for (high-order) finite element discretization, its GitHub master branch

https://github.com/mfem/mfem

To build the miniapp, first download *hypre* and METIS from the links above
and put everything on the same level as the `Laghos` directory:
```sh
~> ls
Laghos/ v2.11.2.tar.gz metis-4.0.3.tar.gz
```

Build *hypre*:
```sh
~> tar -zxvf v2.11.2.tar.gz
~> cd hypre-2.11.2/src/
~/hypre-2.11.2/src> ./configure --disable-fortran
~/hypre-2.11.2/src> make -j
~/hypre-2.11.2/src> cd ../..
~> ln -s hypre-2.11.2 hypre
```
For large runs (problem size above 2 billion unknowns), add the
`--enable-bigint` option to the above `configure` line.

Build METIS:
```sh
~> tar -zxvf metis-4.0.3.tar.gz
~> cd metis-4.0.3
~/metis-4.0.3> make
~/metis-4.0.3> cd ..
~> ln -s metis-4.0.3 metis-4.0
```
This build is optional, as MFEM can be build without METIS by specifying
`MFEM_USE_METIS = NO` below.

Clone and build the parallel version of MFEM:
```sh
~> git clone https://github.com/mfem/mfem.git ./mfem
~> cd mfem/
~/mfem> git checkout master
~/mfem> make parallel -j
~/mfem> cd ..
```
The above uses the `master` branch of MFEM.
See the [MFEM building page](http://mfem.org/building/) for additional details.

(Optional) Clone and build GLVis:
```sh
~> git clone https://github.com/GLVis/glvis.git ./glvis
~> cd glvis/
~/glvis> make
~/glvis> cd ..
```
The easiest way to visualize Laghos results is to have GLVis running in a
separate terminal. Then the `-vis` option in Laghos will stream results directly
to the GLVis socket.

Build Laghos
```sh
~> cd Laghos/
~/Laghos> make -j
```
This can be followed by `make test` and `make install` to check and install the
build respectively. See `make help` for additional options.

See also the `make setup` target that can be used to automated the
download and building of hypre, METIS and MFEM.

## Running

#### Sedov blast

The main problem of interest for Laghos is the Sedov blast wave (`-p 1`) with
partial assembly option (`-pa`).

Some sample runs in 2D and 3D respectively are:
```sh
mpirun -np 8 ./laghos -p 1 -dim 2 -rs 3 -tf 0.8 -pa
mpirun -np 8 ./laghos -p 1 -dim 3 -rs 2 -tf 0.6 -pa -vis
```

The latter produces the following density plot (notice the `-vis` option)

[![Sedov blast image](data/sedov.png)](https://glvis.org/live/?stream=../data/laghos.saved)

#### Taylor-Green and Gresho vortices

Laghos includes also smooth test problems that expose all the principal
computational kernels of the problem except for the artificial viscosity
evaluation. (Viscosity can still be activated for these problems with the
`--impose-viscosity` option.)

Some sample runs in 2D and 3D respectively are:
```sh
mpirun -np 8 ./laghos -p 0 -dim 2 -rs 3 -tf 0.5 -pa
mpirun -np 8 ./laghos -p 0 -dim 3 -rs 1 -tf 0.25 -pa
mpirun -np 8 ./laghos -p 4 -m data/square_gresho.mesh -rs 3 -ok 3 -ot 2 -tf 0.62 -s 7 -vis -pa
```

The latter produce the following velocity magnitude plots (notice the `-vis` option)

#### Triple-point problem

This is a well known three-material problem that combines shock waves and
vorticity, thus examining the complex computational abilities of Laghos.

Some sample runs in 2D and 3D respectively are:
```sh
mpirun -np 8 ./laghos -p 3 -m data/rectangle01_quad.mesh -rs 2 -tf 5.0 -pa
mpirun -np 8 ./laghos -p 3 -m data/box01_hex.mesh -rs 2 -tf 5.0 -vis -pa
```

The latter produces the following specific internal energy plot (notice the `-vis` option)

## Verification of Results

To make sure the results are correct, we tabulate reference final iterations
(`step`), time steps (`dt`) and energies (`|e|`) for the runs listed below:

1. `mpirun -np 8 ./laghos -p 0 -dim 2 -rs 3 -tf 0.75 -pa`
2. `mpirun -np 8 ./laghos -p 0 -dim 3 -rs 1 -tf 0.75 -pa`
3. `mpirun -np 8 ./laghos -p 1 -dim 2 -rs 3 -tf 0.8 -pa`
4. `mpirun -np 8 ./laghos -p 1 -dim 3 -rs 2 -tf 0.6 -pa`
5. `mpirun -np 8 ./laghos -p 2 -dim 1 -rs 5 -tf 0.2 -fa`
6. `mpirun -np 8 ./laghos -p 3 -m data/rectangle01_quad.mesh -rs 2 -tf 3.0 -pa`
7. `mpirun -np 8 ./laghos -p 3 -m data/box01_hex.mesh -rs 1 -tf 5.0 -pa`
8. `mpirun -np 8 ./laghos -p 4 -m data/square_gresho.mesh -rs 3 -ok 3 -ot 2 -tf 0.62831853 -s 7 -pa`
9. `mpirun -np 8 ./laghos -p 7 -m data/rt2D.mesh -tf 4 -rs 1 -ok 4 -ot 3 -pa`

| `run` | `step` | `dt` | `e` |
| ----- | ------ | ---- | --- |
| 1. | 339 | 0.000702 | 4.9695537349e+01 |
| 2. | 1041 | 0.000121 | 3.3909635545e+03 |
| 3. | 1154 | 0.001655 | 4.6303396053e+01 |
| 4. | 560 | 0.002449 | 1.3408616722e+02 |
| 5. | 413 | 0.000470 | 3.2012077410e+01 |
| 6. | 2872 | 0.000064 | 5.6547039096e+01 |
| 7. | 858 | 0.000474 | 5.6691500623e+01 |
| 8. | 776 | 0.000045 | 4.0982431726e+02 |
| 9. | 2462 | 0.000050 | 1.1792848680e+02 |

Similar GPU runs using the MFEM CUDA *device* can be run as follows:

1. `./laghos -p 0 -dim 2 -rs 3 -tf 0.75 -pa -d cuda`
2. `./laghos -p 0 -dim 3 -rs 1 -tf 0.75 -pa -d cuda`
3. `./laghos -p 1 -dim 2 -rs 3 -tf 0.80 -pa -d cuda`
4. `./laghos -p 1 -dim 3 -rs 2 -tf 0.60 -pa -d cuda`
5. -- this is a 1D test that is not supported on the device --
6. `./laghos -p 3 -m data/rectangle01_quad.mesh -rs 2 -tf 3.0 -pa -d cuda`
7. `./laghos -p 3 -m data/box01_hex.mesh -rs 1 -tf 5.0 -pa -cgt 1e-12 -d cuda`
8. `./laghos -p 4 -m data/square_gresho.mesh -rs 3 -ok 3 -ot 2 -tf 0.62831853 -s 7 -pa -d cuda`
9. `./laghos -p 7 -m data/rt2D.mesh -tf 4 -rs 1 -ok 4 -ot 3 -pa -d cuda`

An implementation is considered valid if the final energy values are all within
round-off distance from the above reference values.

## Performance Timing and FOM

Each time step in Laghos contains 3 major distinct computations:

1. The inversion of the global kinematic mass matrix (CG H1).
2. The force operator evaluation from degrees of freedom to quadrature points (Forces).
3. The physics kernel in quadrature points (UpdateQuadData).

By default Laghos is instrumented to report the total execution times and rates,
in terms of millions of degrees of freedom per second (megadofs), for each of
these computational phases. (The time for inversion of the local thermodynamic
mass matrices (CG L2) is also reported, but that takes a small part of the
overall computation.)

Laghos also reports the total rate for these major kernels, which is a proposed
**Figure of Merit (FOM)** for benchmarking purposes. Given a computational
allocation, the FOM should be reported for different problem sizes and finite
element orders.

A sample run on the [Vulcan](https://computation.llnl.gov/computers/vulcan) BG/Q
machine at LLNL is:

```
srun -n 294912 laghos -pa -p 1 -tf 0.6 -pt 911 -m data/cube_922_hex.mesh \
--ode-solver 7 --max-steps 4
--cg-tol 0 --cg-max-iter 50 -ok 3 -ot 2 -rs 5 -rp 2
```
This is Q3-Q2 3D computation on 294,912 MPI ranks (18,432 nodes) that produces
rates of approximately 125419, 55588, and 12674 megadofs, and a total FOM of
about 2064 megadofs.

To make the above run 8 times bigger, one can either weak scale by using 8 times
as many MPI tasks and increasing the number of serial refinements: `srun -n
2359296 ... -rs 6 -rp 2`, or use the same number of MPI tasks but increase the
local problem on each of them by doing more parallel refinements: `srun -n
294912 ... -rs 5 -rp 3`.

## Versions

In addition to the main MPI-based CPU implementation in https://github.com/CEED/Laghos,
the following versions of Laghos have been developed

- **SERIAL** version in the [serial/](./serial/README.md) directory.
- **AMR** version in the [amr/](./amr/README.md) directory.
This version supports dynamic adaptive mesh refinement.

## Contact

You can reach the Laghos team by emailing [email protected] or by leaving a
comment in the [issue tracker](https://github.com/CEED/Laghos/issues).

## Copyright

The following copyright applies to each file in the CEED software suite,
unless otherwise stated in the file:

See files LICENSE and NOTICE for details.