__                __
          / /   ____  ____  / /_  ____  _____
         / /   / __ `/ __ `/ __ \/ __ \/ ___/
        / /___/ /_/ / /_/ / / / / /_/ (__  )
       /_____/\__,_/\__, /_/ /_/\____/____/
                   /____/

    High-order Lagrangian Hydrodynamics Miniapp

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

Laghos captures the basic structure of many compressible shock hydrocodes, including the BLAST code at Lawrence Livermore National Laboratory. The miniapp is built on top of a general discretization library, MFEM, thus separating the pointwise physics from finite element and meshing concerns.

The Laghos miniapp is part of the CEED software suite, 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 (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, 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 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).

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.
• 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 and data output for visualization and data analysis with VisIt.

Code Structure

• The file laghos.cpp contains the main driver with the time integration loop starting around line 431.
• In each time step, the ODE system of interest is constructed and solved by the class LagrangianHydroOperator, defined around line 375 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 on CPU

Laghos has the following external dependencies:

• hypre, used for parallel linear algebra, we recommend version 2.10.0b
  https://computation.llnl.gov/casc/hypre/software.html

• METIS, used for parallel domain decomposition (optional), we recommend version 4.0.3
  http://glaros.dtc.umn.edu/gkhome/metis/metis/download

• 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:

~> ls
Laghos/  hypre-2.10.0b.tar.gz  metis-4.0.tar.gz

Build hypre:

~> tar -zxvf hypre-2.10.0b.tar.gz
~> cd hypre-2.10.0b/src/
~/hypre-2.10.0b/src> ./configure --disable-fortran
~/hypre-2.10.0b/src> make -j
~/hypre-2.10.0b/src> cd ../..

For large runs (problem size above 2 billion unknowns), add the --enable-bigint option to the above configure line.

Build METIS:

~> 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:

~> git clone https://github.com/mfem/mfem.git ./mfem
~> cd mfem/
~/mfem> git checkout laghos-v1.0
~/mfem> make parallel -j
~/mfem> cd ..

The above uses the laghos-v1.0 tag of MFEM, which is guaranteed to work with Laghos v1.0. Alternatively, one can use the latest versions of the MFEM and Laghos master branches (provided there are no conflicts. See the MFEM building page for additional details.

(Optional) Clone and build GLVis:

~> 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

~> cd Laghos/
~/Laghos> make

This can be followed by make test and make install to check and install the build respectively. See make help for additional options.

Building on GPU with cuda, or RAJA

Environment setup

export MPI_PATH=~/usr/local/openmpi/3.0.0

Hypre

• https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods/download/hypre-2.11.2.tar.gz
• tar xzvf hypre-2.11.2.tar.gz
• cd hypre-2.11.2/src
• ./configure --disable-fortran --with-MPI --with-MPI-include=$MPI_PATH/include --with-MPI-lib-dirs=$MPI_PATH/lib
• make -j
• cd ../..

Metis

• http://glaros.dtc.umn.edu/gkhome/fetch/sw/metis/metis-5.1.0.tar.gz
• tar xzvf metis-5.1.0.tar.gz
• cd metis-5.1.0
• make config shared=1 prefix=`pwd`
• make && make install
• cd ..

MFEM

• git clone git@github.com:mfem/mfem.git
• cd mfem
• make config MFEM_USE_MPI=YES HYPRE_DIR=`pwd`/../hypre-2.11.2/src/hypre MFEM_USE_METIS_5=YES METIS_DIR=`pwd`/../metis-5.1.0
• make status to verify that all the include paths are correct
• make -j
• cd ..

Laghos

• git clone git@github.com:CEED/Laghos.git
• cd Laghos
• git checkout raja-dev
• edit the makefile, set NV_ARCH to the desired architecture and the absolute paths to CUDA_DIR, MFEM_DIR, MPI_HOME
• make to build for the CPU version
• ./laghos -cfl 0.1 should give step 78, t = 0.5000, dt = 0.001835, |e| = 7.0537801760
• cp ./laghos ./laghos.cpu
• make clean && make cuda
• ./laghos -cfl 0.1 should give you again again step 78, t = 0.5000, dt = 0.001835, |e| = 7.0537801760
• cp ./laghos ./laghos.gpu
• if you set up the RAJA_DIR path in the makefile, you can make clean && make raja, cp ./laghos ./laghos.raja

Options

• -m : Mesh file to use
• -ok : Order (degree) of the kinematic finite element space
• -rs : Number of times to refine the mesh uniformly in serial
• -p : Problem setup to use, Sedov problem is '1'
• -cfl : CFL-condition number
• -ms : Maximum number of steps (negative means no restriction)
• -mult: Enable or disable MULT test kernels
• -cuda: Enable or disable CUDA kernels if you are using RAJA
• -uvm: Enable or disable Unified Memory
• -aware: Enable or disable MPI CUDA Aware
• -hcpo: Enable or disable Host Conforming Prolongation Operations, which transfers ALL the data to the host before communications

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:

mpirun -np 8 laghos -p 1 -m data/square01_quad.mesh -rs 3 -tf 0.8 -no-vis -pa
mpirun -np 8 laghos -p 1 -m data/cube01_hex.mesh -rs 2 -tf 0.6 -no-vis -pa

The latter produces the following density plot (when run with the -vis instead of the -no-vis option)

Taylor-Green vortex

Laghos includes also a smooth test problem, that exposes all the principal computational kernels of the problem except for the artificial viscosity evaluation.

Some sample runs in 2D and 3D respectively are:

mpirun -np 8 laghos -p 0 -m data/square01_quad.mesh -rs 3 -tf 0.5 -no-vis -pa
mpirun -np 8 laghos -p 0 -m data/cube01_hex.mesh -rs 1 -cfl 0.1 -tf 0.25 -no-vis -pa

The latter produces the following velocity magnitude plot (when run with the -vis instead of the -no-vis option)

Triple-point problem

Well known three-material problem combines shock waves and vorticity, thus examining the complex computational abilities of Laghos.

Some sample runs in 2D and 3D respectively are:

mpirun -np 8 laghos -p 3 -m data/rectangle01_quad.mesh -rs 2 -tf 2.5 -cfl 0.025 -no-vis -pa
mpirun -np 8 laghos -p 3 -m data/box01_hex.mesh -rs 1 -tf 2.5 -cfl 0.05 -no-vis -pa

The latter produces the following specific internal energy plot (when run with the -vis instead of the -no-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 nine runs listed above:

1. mpirun -np 8 laghos -p 0 -m data/square01_quad.mesh -rs 3 -tf 0.75 -no-vis -pa
2. mpirun -np 8 laghos -p 0 -m data/cube01_hex.mesh -rs 1 -tf 0.75 -no-vis -pa
3. mpirun -np 8 laghos -p 1 -m data/square01_quad.mesh -rs 3 -tf 0.8 -no-vis -pa
4. mpirun -np 8 laghos -p 1 -m data/cube01_hex.mesh -rs 2 -tf 0.6 -no-vis -pa
5. mpirun -np 8 laghos -p 2 -m data/segment01.mesh -rs 5 -tf 0.2 -no-vis -fa
6. mpirun -np 8 laghos -p 3 -m data/rectangle01_quad.mesh -rs 2 -tf 3.0 -no-vis -pa
7. mpirun -np 8 laghos -p 3 -m data/box01_hex.mesh -rs 1 -tf 3.0 -no-vis -pa

run	step	dt	e
1.	339	0.000702	49.6955373491
2.	1041	0.000121	3390.9635545458
3.	1150	0.002271	46.3055694501
4.	561	0.000360	134.0937837919
5.	414	0.000339	32.0120759615
6.	5310	0.000264	141.8348694390
7.	937	0.002285	144.0012514765

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, as illustrated in the sample scripts in the timing directory.

A sample run on the Vulcan BG/Q machine at LLNL is:

srun -n 393216 laghos -pa -p 1 -tf 0.6 -no-vis
                      -pt 322 -m data/cube_12_hex.mesh
                      --cg-tol 0 --cg-max-iter 50 --max-steps 2
                      -ok 3 -ot 2 -rs 5 -rp 3

This is Q3-Q2 3D computation on 393,216 MPI ranks (24,576 nodes) that produces rates of approximately 168497, 74221, and 16696 megadofs, and a total FOM of about 2073 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 3145728 ... -rs 6 -rp 3, or use the same number of MPI tasks but increase the local problem on each of them by doing more parallel refinements: srun -n 393216 ... -rs 5 -rp 4.

Versions

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

• A serial version in the serial directory.
• GPU version based on OCCA.
• A RAJA-based version in the raja-dev branch.

Contact

You can reach the Laghos team by emailing laghos@llnl.gov or by leaving a comment in the issue tracker.

Copyright

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

Copyright (c) 2017, Lawrence Livermore National Security, LLC. Produced at the Lawrence Livermore National Laboratory. LLNL-CODE-734707. All Rights reserved.

See files LICENSE and NOTICE for details.