The MiniWeather Mini App

A mini app simulating weather-like flows for training in parallelizing accelerated HPC architectures. Currently includes:

MPI (C, Fortran, and C++)
OpenACC Offload (C and Fortran)
OpenMP Threading (C and Fortran)
OpenMP Offload (C and Fortran)
C++ Portability
- CUDA-like approach
- https://github.com/mrnorman/YAKL/wiki/CPlusPlus-Performance-Portability-For-OpenACC-and-OpenMP-Folks
- C++ code works on CPU, Nvidia GPUs (CUDA), and AMD GPUs (HIP)

Author: Matt Norman, Oak Ridge National Laboratory, https://mrnorman.github.io

Contributors:

Matt Norman (ORNL)
Jeff Larkin (Nvidia)
Isaac Lyngaas (ORNL)

Introduction
- Brief Description of the Code
Compiling and Running the Code
Parallelization
- Indexing
- MPI Domain Decomposition
- OpenMP CPU Threading
- OpenACC Accelerator Threading
- OpenMP Offload Accelerator Threading
- C++ Performance Portability
Numerical Experiments
Physics, PDEs, and Numerical Approximations
MiniWeather Model Scaling Details
Checking for Correctness
Further Resources
Common Problems

Introduction

The miniWeather code mimics the basic dynamics seen in atmospheric weather and climate. The dynamics themselves are dry compressible, stratified, non-hydrostatic flows dominated by buoyant forces that are relatively small perturbations on a hydrostatic background state. The equations in this code themselves form the backbone of pretty much all fluid dynamics codes, and this particular flavor forms the base of all weather and climate modeling.

With about 500 total lines of code (and only about 200 lines that you care about), it serves as an approachable place to learn parallelization and porting using MPI + X, where X is OpenMP, OpenACC, CUDA, or potentially other approaches to CPU and accelerated parallelization. The code uses periodic boundary conditions in the x-direction and solid wall boundary conditions in the z-direction.

Brief Description of the Code

Domain Parameters

A fuller description of the science, math, and dynamics are play are given later, but this section is reserved to describing some of the main variables and flow in the code. The code is decomposed in two spatial dimensions, x and z, with nx_glob and nz_glob cells in the global domain and nx and nz cells in the local domain, using straightforward domain decomposition for MPI-level parallelization. The global domain is of size xlen and zlen meters, and hs “halo” cells are appended to both sides of each dimension for convenience in forming stencils of cells for reconstruction.

Fluid State Variables

There are four main arrays used in this code: state, state_tmp, flux, and tend, and the dimensions for each are given in the code upon declaration in the comments. Each of these arrays is described briefly below:

state: This is the fluid state at the current time step, and it is the only array that persists from one time step to the next. The other four are only used within the calculations to advance the model to the next time step. The fluid state describes the average state over each cell area in the spatial domain. This variable contains four fluid states, which are the traditional mass, momenta, and thermodynamic quantities of most fluid models:
1. Density (ID_DENS): The 2-D density of the fluid, $\large \rho$ , in $\large \text{kg}\ \text{m}^{-2}$ (note this is normally $\large \text{kg}\ \text{m}^{-3}$ , but this is a 2-D model, not 3-D)
2. U-momentum (ID_UMOM): The momentum per unit area of the fluid in the x-direction calculated as $\large \rho u$ , where u is the x-direction wind velocity. The units are $\large \text{kg}\ \text{m}^{-1}\ \text{s}^{-1}$ . Note that to get true momentum, you must integrate over the cell.
3. W-momentum (ID_WMOM): The momentum per unit area of the fluid in the z-direction calculated as $\large \rho w$ , where w is the z-direction wind velocity. The units are $\large \text{kg}\ \text{m}^{-1}\ \text{s}^{-1}$ . Note that to get true momentum, you must integrate over the cell.
4. Potential Temperature (ID_RHOT): The product of density and potential temperature, $\large \rho \theta$ , where , $\large P_{0}=10^{5}\,\text{Pa}$ , T is the true temperature, and $\large R_d$ and $\large c_p$ are the dry air constant and specific heat at constant pressure for dry air, respectively. The units of this quantity are $\large \text{K}\,\text{kg}\,\text{m}^{-2}$ .
state_tmp: This is a temporary copy of the fluid state used in the Runge-Kutta integration to keep from overwriting the state at the beginning of the time step, and it has the same units and meaning.
flux: This is fluid state at cell boundaries in the x- and z-directions, and the units and meanings are the same as for state and state_tmp. In the x-direction update, the values of flux at indices i and i+1 represents the fluid state at the left- and right-hand boundaries of cell i. The indexing is analogous in the z-direction. The fluxes are used to exchange fluid properties with neighboring cells.
tend: This is the time tendency of the fluid state $\large \partial\mathbf{q}/\partial t$ , where $\large \mathbf{q}$ is the the state vector, and as the name suggests, it has the same meaning and units as state, except per unit time (appending $\large \text{s}^{-1}$ to the units). In the Finite-Volume method, the time tendency of a cell is equivalent to the divergence of the flux across a cell.

Overall Model Flow

This code follows a traditional Finite-Volume control flow.

To compute the time tendency, given an initial state at the beginning of a time step that contains cell-averages of the fluid state, the value of the state at cell boundaries is reconstructed using a stencil. Then, a viscous term is added to the fluid state at the cell boundaries to improve stability. Next, the tendencies are computed as the divergence of the flux across the cell. Finally, the tendencies are applied to the fluid state.

Once the time tendency is computed, the fluid PDEs are essentially now cast as a set of ODEs varying only in time, and this is called the “semi-discretized” form of the equations. To solve the equations in time, an ODE solver is used, in this case, a Runge-Kutta method. Finally, at the highest level, the equations are split into separate solves in each dimension, x and z.

Compiling and Running the Code

Software Dependencies

Parallel-netcdf: https://trac.mcs.anl.gov/projects/parallel-netcdf
- This is a dependency for two reasons: (1) NetCDF files are easy to visualize and convenient to work with; (2) The users of this code shouldn't have to write their own parallel I/O.
Ncview: http://meteora.ucsd.edu/~pierce/ncview_home_page.html
- This is the easiest way to visualize NetCDF files.
MPI
For OpenACC: An OpenACC-capable compiler (PGI / Nvidia, Cray, GNU)
- A free version of the PGI / Nvidia compiler can be obtained by googling for the "Community Edition"
For OpenMP: An OpenMP offload capable compiler (Cray, XL, GNU)
For C++ portability, Nvidia's CUB and AMD's hipCUB and rocPRIM are already included as submodules
CMake: https://cmake.org

On Ubuntu, the pnetcdf, ncview, mpi, and cmake dependencies can be installed with:

sudo apt-get install cmake libopenmpi-dev libpnetcdf-dev ncview

Basic Setup

git clone [email protected]:mrnorman/miniWeather.git
cd miniWeather
git submodule update --init --recursive

Directories and Compiling

There are four main directories in the mini app: (1) a Fortran source directory; (2) a C source directory; (3) a C++ source directory; and (4) a documentation directory. The C code is technically C++ code but only because I wanted to use the ampersand pass by reference notation rather than the hideous C asterisks.

miniWeather uses the CMake build system, so you'll need to have cmake installed. Look at the fortran/build/cmake_summit_[compiler].sh, c/build/cmake_summit_[compiler].sh, and cpp/build/cmake_summit_[compiler].sh scripts for examples of how to run the cmake configuration in Fortran, C, and C++, respectively.

Building and Testing Workflow

Note that you must source the cmake scripts in the build/ directores because they do module loading and set a TEST_MPI_COMMAND environment variable because it will differ from machine to machine.

cd miniWeather/[language]/build
source cmake_[machine]_[compiler].sh
make
make test

To run the code after confirming the tests pass, you can launch the executable of your choice with mpirun -n [# tasks] ./executable_name on most machines.

On Summit, it gets more complicated, unfortunately. You need to deal with so-called ``resource sets''. The following are single-node options:

MPI-only: jsrun -n 6 -a 7 -c 7 ./executable_name
MPI + GPU: jsrun -n 6 -a 1 -c 1 -g 1 ./executable_name

For more nodes on Summit, just multiply the -n parameter by the number of nodes.

Summit: On OLCF's Summit computer (and several other computers), you need to have an allocation loaded in order to run the code. You cannot run the code on the login nodes because the code is compiled for spectrum-mpi, which will give a segmentation fault unless you run the executable with jsrun. It's easiest to grab an interactive allocation on one node for two hours. Each Summit node has six GPUs and 42 CPU cores.

Also, on Summit, it's best to clone the entire repo in /gpfs space because the unit tests require writing files, which can only be done in GPFS space.

Compiler Considerations

To use OpenACC and OpenMP offloading, look at the miniWeather/[c | fortran]/cmake_summit_gnu.sh files for guidance in terms of compiler options. Also, you need to use GNU version 8.1 or higher for OpenACC and OpenMP offloading to work. PGI should compile OpenACC fine with any reasonably modern version, and the same is true for IBM XL with OpenMP offload.

C and Fortran

To compile the code, first edit the Makefile and change the flags to point to your parallel-netcdf installation as well as change the flags based on which compiler you are using. There are five versions of the code in C and Fortran: serial, mpi, mpi+openmp, and mpi+openacc, mpi+openmp4.5. The filenames make it clear which file is associated with which programming paradigm.

It's best to run the cmake configure from the build directories. For PGI, to enable OpenACC, and OpenMP, you'll need to specify:

cmake ... -DOPENACC_FLAGS="-ta=nvidia,cc??" # cc?? is, e.g. cc35, cc50, cc70, etc.
                                            # It depends on what GPU you're using
          -DOPENMP_FLAGS="-mp"

For GNU, to enable OpenACC, OpenMP, and OpenMP offload, you'll need to specify:

cmake ... -DOPENACC_FLAGS="-fopenacc"
          -DOPENMP45_FLAGS="-fopenmp"
          -DOPENMP_FLAGS="-fopenmp"

For IBM XL, to enable OpenMP and OpenMP offload, you'll need to specify:

cmake ... -DOPENMP45_FLAGS="-qsmp=omp -qoffload"
          -DOPENMP_FLAGS="-qsmp=omp"

After the cmake configure, type make -j to build the code, and type make test to run the tests. The executables are named serial, mpi, openmp, openmp45, and openacc.

C++

For the C++ code, there are three configurations: serial, mpi, and mpi+parallel_for. The latter uses a C++ kernel-launching approach, which is essentially CUDA with greater portability for multiple backends. This code also uses cmake, and you can use the summit scripts as examples.

Altering the Code's Configurations

To alter the configuration of the code, you can control the number of cells in the x- and z-directions, the length of simulation time, the output frequency, and the initial data to use by passing the following variables to the CMake configuration:

-DNX=400: Uses 400 cells in the x-direction
-DNZ=200: Uses 200 cells in the z-direction
-DSIM_TIME=1000: Simulates for 1,000 seconds model time
-DOUT_FREQ=10: Outputs every 10 seconds model time
-DDATA_SPEC=DATA_SPEC_THERMAL: Initializes a rising thermal

It's best if you keep NX exactly twice the value of NZ since the domain is 20km x 10km.

The data specifications are DATA_SPEC_COLLISION, DATA_SPEC_THERMAL, DATA_SPEC_MOUNTAIN, DATA_SPEC_DENSITY_CURRENT, and DATA_SPEC_INJECTION, and each are described later on.

Running the Code

To run the code, simply call:

mpirun -n [# ranks] ./[parallel_id]

where [parallel_id] is serial, mpi, openmp, openacc, openmp45, or parallelfor. You'll notice some [parallel_id]_test executables as well. These use fixed values for nx, nz, sim_time, out_freq, and data_spec for unit testing whereas the [parallel_id] executables use the values you specified to CMake through the -D definitions.

Since parameters are set in the code itself, you don't need to pass any parameters. Some machines use different tools instead of mpirun (e.g., OLCF's Summit uses jsrun).

Viewing the Output

The file I/O is done in the netCDF format: (https://www.unidata.ucar.edu/software/netcdf). To me, the easiest way to view the data is to use a tool called “ncview” (http://meteora.ucsd.edu/~pierce/ncview_home_page.html). To use it, you can simply type ncview output.nc, making sure you have X-forwarding enabled in your ssh session. Further, you can call ncview -frames output.nc, and it will dump out all of your frames in the native resolution you're viewing the data in, and you you can render a movie with tools like ffmpeg.

Parallelization

This code was designed to parallelize with MPI first and then OpenMP, OpenACC, OpenMP offload, or parallel_for next, but you can always parallelize with OpenMP or OpenACC without MPI if you want. But it is rewarding to be able to run it on multiple nodes at higher resolution for more and sharper eddies in the dynamics.

As you port the code, you'll want to change relatively little code at a time, re-compile, re-run, and look at the output to see that you're still getting the right answer. There are advantages to using a visual tool to check the answer (e.g., ncview), as it can sometimes give you clues as to why you're not getting the right answer.

Note that you only need to make changes code within the first 450 source lines for C and Fortran, and each loop that needs threading is decorated with a // THREAD ME comment. Everything below that is initialization and I/O code that doesn't need to be parallelized (unless you want to) for C and Fortran directives-based approaches.

For the C++ code, you will need to work with the initialization and File I/O code, but everything you need to do is explicitly guided via comments in the code.

Indexing

The code makes room for so-called “halo” cells in the fluid state. This is a common practice in any algorithm that uses stencil-based reconstruction to estimate variation within a domain. In this code, there are hs halo cells on either side of each spatial dimension, and I pretty much hard-code hs=2.

Fortran

In the Fortran code's fluid state (state), the x- and z-dimensions are dimensioned as multi-dimensional arrays that range from 1-hs:nx+hs. In the x-direction, 1-hs:0 belong to the MPI task to the left, cells 1:nx belong to the current MPI task, and nx+1:nx+hs belong to the MPI task to the right. In the z-dimension, 1-hs:0 are artificially set to mimic a solid wall boundary condition at the bottom, and nz+1:nz+hs are the same for the top boundary. The cell-interface fluxes (flux) are dimensioned as 1:nx+1 and 1:nz+1 in the x- and z-directions, and the cell average tendencies (tend) are dimensioned 1:nx and 1:nz in the x- and z-directions. The cell of index i will have left- and right-hand interface fluxes of index i and i+1, respectively, and it will be evolved by the tendency at index i. The analog of this is also true in the z-direction.

C

In the C code, the fluid state array is dimensioned to size nz+2*hs and nx+2*hs in the x- and z-directions. In the x-direction, cells 0 to hs-1 belong to the left MPI task, cells hs to nx+hs-1 belong to the current MPI tasks, and cells nx+hs to nx+2*hs-1 belong to the right MPI task. The z-direction's halo cells are used to mimic solid wall boundaries. The cell-interface fluxes (flux) are dimensioned as nx+1 and nz+1 in the x- and z-directions, and the cell average tendencies (tend) are dimensioned nx and nz in the x- and z-directions. The cell of index i+hs will have left- and right-hand interface fluxes of index i and i+1, respectively, and it will be evolved by the tendency at index i. The analog of this is also true in the z-direction.

C++

The C++ indexing is the same as the C indexing, but instead of having to flatten array indices into a single dimension like the C code, multi-dimensional arrays are used with () indexing syntax and the right-most index varying the fastest.

MPI Domain Decomposition

This code was designed to use domain decomposition, where each MPI rank “owns” a certain set of cells in the x-direction and contains two “halo” cells from the left- and right-hand MPI tasks in the x-direction as well. The domain is only decomposed in the x-direction and not the z-direction for simplicity.

IMPORTANT: Please be sure to set nranks, myrank, nx, i_beg, left_rank, and right_rank. These are clearly marked in the serial source code. You can set more variables, but these are used elsewhere in the code (particularly in the parallel file I/O), so they must be set.

To parallelize with MPI, there are only two places in the code that need to be altered. The first is the initialization, a subroutine / function named init, where you must determine the number of ranks, you process's rank, the beginning index of your rank's first cell in the x-direction, the number of x-direction cells your rank will own, and the MPI rank IDs that are to your left and your right. Because the code is periodic in the x-direction, your left and right neighboring ranks will wrap around. For instance, if your are rank 0, your left-most rank will be nranks-1.

The second place is in the routine that sets the halo values in the x-direction. In this routine, you need to:

Create MPI data buffers (at the same place the other arrays are declared) to hold the data that needs to be sent and received, allocate them in the init() routine, and deallocate them in the finalize() routine.
Pack the data you need to send to your left and right MPI neighbors
Send the data to your left and right MPI neighbors
Receive the data from your left and right MPI neighbors
Unpack the data from your left and right neighbors and place the data into your MPI rank's halo cells.

Once you complete this, the code will be fully parallelized in MPI. Both of the places you need to add code for MPI are marked in the serial code, and there are some extra hints in the set_halo_values_x() routine as well.

OpenMP CPU Threading

For the OpenMP code, you basically need to decorate the loops with omp parallel do in Fortran or omp parallel for in C, and pay attention to any variables you need to make private() so that each thread has its own copy. Keep in mind that OpenMP works best on “outer” loops rather than “inner” loops. Also, for sake of performance, there are a couple of instances where it is wise to use the “collapse” clause because the outermost loop is not large enough to support the number of threads most CPUs have.

In Fortran, you can parallelize three loops with the following directive:

!$omp parallel do collapse(3)
do ll = 1 , NUM_VARS
  do k = 1 , nz
    do i = 1 , nx
      state_out(i,k,ll) = state_init(i,k,ll) + dt * tend(i,k,ll)
    enddo
  enddo
enddo

This will collapse the three loops together (combining their parallelism) and then launch that parallelism among a number of CPU threads. In C / C++, it will be:

#pragma omp parallel for collapse(3)
for (ll=0; ll<NUM_VARS; ll++) {
  for (k=0; k<nz; k++) {
    for (i=0; i<nx; i++) {
      inds = ll*(nz+2*hs)*(nx+2*hs) + (k+hs)*(nx+2*hs) + i+hs;
      indt = ll*nz*nx + k*nx + i;
      state_out[inds] = state_init[inds] + dt * tend[indt];
    }
  }
}

OpenACC Accelerator Threading

To thread the same loops among the threads on a GPU, you will use the following in Fortran:

!$acc parallel loop collapse(3)
do ll = 1 , NUM_VARS
  do k = 1 , nz
    do i = 1 , nx
      state_out(i,k,ll) = state_init(i,k,ll) + dt * tend(i,k,ll)
    enddo
  enddo
enddo

In C / C++, it will be:

#pragma acc parallel loop collapse(3)
for (ll=0; ll<NUM_VARS; ll++) {
  for (k=0; k<nz; k++) {
    for (i=0; i<nx; i++) {
      inds = ll*(nz+2*hs)*(nx+2*hs) + (k+hs)*(nx+2*hs) + i+hs;
      indt = ll*nz*nx + k*nx + i;
      state_out[inds] = state_init[inds] + dt * tend[indt];
    }
  }
}

The OpenACC approach will differ depending on whether you're in Fortran or C. Just a forewarning, OpenACC is much more convenient in Fortran when it comes to data movement because in Fortran, the compiler knows how big your arrays are, and therefore the compiler can (and does) create all of the data movement for you (NOTE: This is true for PGI and Cray but not for GNU at the moment). All you have to do is optimize the data movement after the fact. for more information about the OpenACC copy directives, see:

https://github.com/mrnorman/miniWeather/wiki/A-Practical-Introduction-to-GPU-Refactoring-in-Fortran-with-Directives-for-Climate#optimizing--managing-data-movement

Fortran Code

The OpenACC parallelization is a bit more involved when it comes to performance. But, it's a good idea to just start with the kernels themselves, since the compiler will generate all of your data statements for you on a per-kernel basis. You need to pay attention to private variables here as well. Only arrays need to be privatized. Scalars are automatically privatized for you.

Once you're getting the right answer with the kernels on the GPU, you can look at optimizing data movement by putting in data statements. I recommend putting data statements for the state, tend, flux, hy_*, and the MPI buffers (sendbuf_l, sendbuf_r, recvbuf_l, and recvbuf_r) around the main time stepping loop. Then, you need to move the data to the host before sending MPI data, back to the device once you receive MPI data, and to the host before file I/O.

C Code

In the C code, you'll need to put in manual copy(), copyin(), and copyout() statements on a per-kernel basis, and you'll need to explicitly declare the size of each array as well.

IMPORTANT: The syntax for data movement in C will seem odd to you. The syntax is:

#pragma acc data copy( varname[ starting_index : size_of_transfer ] )

So, for instance, if you send a variable, var, of size n to the GPU, you will say, #pragma acc data copyin(var[0:n]). Many would expect it to look like an array slice (e.g., (0:n-1)), but it is not.

Other than this, the approach is the same as with the Fortran case.

OpenMP Offload Accelerator Threading

To launch the same loops in OpenMP offload on a GPU's threads, you will use:

!$omp target teams distribute parallel do simd collapse(3)
do ll = 1 , NUM_VARS
  do k = 1 , nz
    do i = 1 , nx
      state_out(i,k,ll) = state_init(i,k,ll) + dt * tend(i,k,ll)
    enddo
  enddo
enddo

Note that some compilers do different things for simd and parallel for, and therefore, there is no portable use of OpenMP offload at this point. In C / C++, this will be:

#pragma omp target teams distribute parallel for simd collapse(3)
for (ll=0; ll<NUM_VARS; ll++) {
  for (k=0; k<nz; k++) {
    for (i=0; i<nx; i++) {
      inds = ll*(nz+2*hs)*(nx+2*hs) + (k+hs)*(nx+2*hs) + i+hs;
      indt = ll*nz*nx + k*nx + i;
      state_out[inds] = state_init[inds] + dt * tend[indt];
    }
  }
}

The OpenMP 4.5+ approach is very similar to OpenACC for this code, except that the XL and GNU compilers do not generate data statements for you in Fortran. For OpenMP offload, you'll change your data statements from OpenACC as follows:

copyin (var) --> map(to:     var)
copyout(var) --> map(from:   var)
copy   (var) --> map(tofrom: var)
create (var) --> map(alloc:  var)
delete (var) --> map(delete: var)

C++ Performance Portability

The C++ code is in the cpp directory, and it uses a custom multi-dimensional Array class from Array.h for large global variables and Static Array (SArray) class in SArray.h for small local arrays placed on the stack. For adding MPI to the serial code, please follow the instructions in the above MPI section. The primary purpose of the C++ code is to get used to what performance portability looks like in C++, and this is moving from the miniWeather_mpi.cpp code to the miniWeather_mpi_parallelfor.cpp code, where you change all of the loops into parallel_for kernel launches, similar to the Kokkos syntax. As an example of transforming a set of loops into parallel_for, consider the following code:

inline void applyTendencies(realArr &state2, real const c0, realArr const &state0,
                                             real const c1, realArr const &state1,
                                             real const ct, realArr const &tend,
                                             Domain const &dom) {
  for (int l=0; l<numState; l++) {
    for (int k=0; k<dom.nz; k++) {
      for (int j=0; j<dom.ny; j++) {
        for (int i=0; i<dom.nx; i++) {
          state2(l,hs+k,hs+j,hs+i) = c0 * state0(l,hs+k,hs+j,hs+i) +
                                     c1 * state1(l,hs+k,hs+j,hs+i) +
                                     ct * dom.dt * tend(l,k,j,i);
        }
      }
    }
  }
}

will become:

inline void applyTendencies(realArr &state2, real const c0, realArr const &state0,
                                             real const c1, realArr const &state1,
                                             real const ct, realArr const &tend,
                                             Domain const &dom) {
  // for (int l=0; l<numState; l++) {
  //   for (int k=0; k<dom.nz; k++) {
  //     for (int j=0; j<dom.ny; j++) {
  //       for (int i=0; i<dom.nx; i++) {
  yakl::parallel_for( Bounds<4>(numState,dom.nz,dom.ny,dom.nx) , YAKL_LAMBDA (int l, int k, int j, int i) {
    state2(l,hs+k,hs+j,hs+i) = c0 * state0(l,hs+k,hs+j,hs+i) +
                               c1 * state1(l,hs+k,hs+j,hs+i) +
                               ct * dom.dt * tend(l,k,j,i);
  }); 
}

For a fuller description of how to move loops to parallel_for, please see the following webpage:

https://github.com/mrnorman/YAKL/wiki/CPlusPlus-Performance-Portability-For-OpenACC-and-OpenMP-Folks

https://github.com/mrnorman/YAKL

I strongly recommend moving to parallel_for while compiling for the CPU so you don't have to worry about separate memory address spaces at the same time. Be sure to use array bounds checking during this process to ensure you don't mess up the indexing in the parallel_for launch. You can do this by adding -DARRAY_DEBUG to the CXX_FLAGS in your Makefile. After you've transformed all of the for loops to parallel_for, you can deal with the complications of separate memory spaces.

GPU Modifications

First, you'll have to pay attention to asynchronicity. parallel_for is asynchronous, and therefore, you'll need to add yakl::fence() in two places: (1) MPI ; and (2) File I/O.

Next, if a parallel_for's kernel uses variables with global scope, which it will in this code, you will get a runtime error when running on the GPU. C++ Lambdas do not capture variables with global scope, and therefore, you'll be using CPU copies of that data, which isn't accessible from the GPU. The most convenient way to handle this is to create local references as follows:

auto &varName = ::varName;

The ::varName syntax is telling the compiler to look in the global context for varName rather than the local context.

This process will be tedious, but it is something you nearly always have to do in C++ performance portability approaches. So it's good to get used to doing it. You will run into similar issues if you attempt to use data from your own class because the this pointer is typically into CPU memory because class objects are often allocated on the CPU. This can also be circumvented via local references in the same manner as above.

You have to put YAKL_INLINE in front of the following functions because they are called from kernels: injection, density_current, turbulence, mountain_waves, thermal, collision, hydro_const_bvfreq, hydro_const_theta, and sample_ellipse_cosine.

Next, you'll need to create new send and recv MPI buffers that are created in CPU memory to easily interoperate with the MPI library. To do this, you'll use the realArrHost typedef in const.h.

realArrHost sendbuf_l_cpu;
realArrHost sendbuf_r_cpu;
realArrHost recvbuf_l_cpu;
realArrHost recvbuf_r_cpu;

You'll also need to replace the buffers in MPI_Isend() and MPI_Irecv() with the CPU versions.

Next, you need to allocate these in init() in a similar manner as the existing MPI buffers, but replacing realArr with realArrHost.

Finally, you'll need to manage data movement to and from the CPU in the File I/O and in the MPI message exchanges.

For the File I/O, you can use Array::createHostCopy() in the ncmpi_put_* routines, and you can use it in-place before the .data() function calls, e.g.,

arrName.createHostCopy().data()

For the MPI buffers, you'll need to use the Array::deep_copy_to(Array &target) member function. e.g.,

sendbuf_l.deep_copy_to(sendbuf_l_cpu);

A deep copy from a device Array to a host Array will invoke cudaMemcopy(...,cudaMemcpyDeviceToHost), and a deep copy from a host Array to a device Array will invoke cudaMemcpy(...,cudaMemcpyHostToDevice) under the hood. You will need to copy the send buffers from device to host just before calling MPI_Isend(), and you will need to copy the recv buffers from host to device just after MPI_WaitAll() on the receive requests, req_r.

Why Doesn't MiniWeather Use CUDA?

Because if you've refactored your code to use kernel launching (i.e., CUDA), you should really be using a C++ portability framework. The code is basically identical, but it can run on many different backends from a single source.

Why Doesn't MiniWeather Use Kokkos or RAJA?

I chose not to use the mainline C++ portability frameworks for two main reasons.

It's easier to compile and managed things with a C++ performance portability layer that's < 3K lines of code long, hence: YAKL (Yet Another Kernel Launcher).
Kokkos in particular would not play nicely with the rest of the code in the CMake project. Likely if a Kokkos version is added, it will need to be a completely separate project and directory.
With YAKL.h and Array.h, you can see for yourself what's going on when we launch kernels using parallel_for on different hardware backends.

Numerical Experiments

A number of numerical experiments are in the code for you to play around with. You can set these by changing the data_spec_int variable.

Rising Thermal

data_spec_int = DATA_SPEC_THERMAL
sim_time = 1000

This simulates a rising thermal in a neutral atmosphere, which will look something like a “mushroom” cloud (without all of the violence).

Potential Temperature after 500 seconds:

Potential Temperature after 1,000 seconds:

Colliding Thermals

data_spec_int = DATA_SPEC_COLLISION
sim_time = 700

This is similar to the rising thermal test case except with a cold bubble at the model top colliding with a warm bubble at the model bottom to produce some cool looking eddies.

Potential Temperature after 200 seconds:

Potential Temperature after 400 seconds:

Potential Temperature after 700 seconds:

Mountain Gravity Waves

data_spec_int = DATA_SPEC_MOUNTAIN
sim_time = 1500

This test cases passes a horizontal wind over a faked mountain at the model bottom in a stable atmosphere to generate a train of stationary gravity waves across the model domain.

Potential Temperature after 400 seconds:

Potential Temperature after 1,300 seconds:

Density Current

data_spec_int = DATA_SPEC_DENSITY_CURRENT
sim_time = 600

This test case creates a neutrally stratified atmosphere with a strong cold bubble in the middle of the domain that crashes into the ground to give the feel of a weather front (more of a downburst, I suppose).

Potential Temperature after 200 seconds:

Potential Temperature after 600 seconds:

Injection

data_spec_int = DATA_SPEC_INJECTION
sim_time = 1200

A narrow jet of fast and slightly cold wind is injected into a balanced, neutral atmosphere at rest from the left domain near the model top. This has nothing to do with atmospheric flows. It's just here for looks.

Potential Temperature after 300 seconds:

Potential Temperature after 1,000 seconds:

Physics, PDEs, and Numerical Approximations

While the numerical approximations in this code are certainly cheap and dirty, they are a fast and easy way to get the job done in a relatively small amount of code. For instance, on 16 K20x GPUs, you can perform a "colliding thermals” simulation with 5 million grid cells (3200 x 1600) in just a minute or two.

The 2-D Euler Equations

This app simulates the 2-D inviscid Euler equations for stratified fluid dynamics, which are defined as follows:

$\large \frac{\partial}{\partial t}\left[\begin{array}{c} \rho\\ \rho u\\ \rho w\\ \rho\theta \end{array}\right]+\frac{\partial}{\partial x}\left[\begin{array}{c} \rho u\\ \rho u^{2}+p\\ \rho uw\\ \rho u\theta \end{array}\right]+\frac{\partial}{\partial z}\left[\begin{array}{c} \rho w\\ \rho wu\\ \rho w^{2}+p\\ \rho w\theta \end{array}\right]=\left[\begin{array}{c} 0\\ 0\\ -\rho g\\ 0 \end{array}\right]$

$\large \rho_{H}=-\frac{1}{g}\frac{\partial p}{\partial z}$

where $\large \rho$ is density, u, and w are winds in the x-, and z-directions, respectively, $\large \theta$ is potential temperature related to temperature, T, by $\large \theta=T\left(P_{0}/P\right)^{R_{d}/c_{p}}$ , $\large P_{0}=10^{5}\,\text{Pa}$ is the surface pressure, g=9.8 $\large \text{\,\ m}\,\mbox{s}^{-2}$ is acceleration due to gravity, $\large p=C_{0}\left(\rho\theta\right)^{\gamma}$ is the pressure as determined by an alternative form of the ideal gas equation of state, $\large C_{0}=R_{d}^{\gamma}p_{0}^{-R_{d}/c_{v}}$ , $\large R_{d}=287\,\mbox{J}\,\mbox{kg}^{-1}\,\mbox{K}^{-1}$ is the dry gas constant, $\large \gamma=c_{p}/c_{v}$ , $\large c_{p}=1004\,\mbox{J}\,\mbox{kg}^{-1}\,\mbox{K}^{-1}$ is specific heat at constant pressure, and $\large c_{v}=717\,\mbox{J}\,\mbox{kg}^{-1}\,\mbox{K}^{-1}$ is specific heat at constant volume. This can be cast in a more convenient form as:

$\large \frac{\partial\mathbf{q}}{\partial t}+\frac{\partial\mathbf{f}}{\partial x}+\frac{\partial\mathbf{h}}{\partial z}=\mathbf{s}$

where a bold font represents a vector quantity.

Maintaining Hydrostatic Balance

The flows this code simulates are relatively small perturbations off of a “hydrostatic” balance, which balances gravity with a difference in pressure:

$\large \frac{dp}{dz}=-\rho g$

Because small violations of this balance lead to significant noise in the vertical momentum, it's best not to try to directly reconstruct this balance but rather to only reconstruct the perturbations. Therefore, hydrostasis is subtracted from the equations to give:

$\large \frac{\partial}{\partial t}\left[\begin{array}{c} \rho^{\prime}\\ \rho u\\ \rho w\\ \left(\rho\theta\right)^{\prime} \end{array}\right]+\frac{\partial}{\partial x}\left[\begin{array}{c} \rho u\\ \rho u^{2}+p\\ \rho uw\\ \rho u\theta \end{array}\right]+\frac{\partial}{\partial z}\left[\begin{array}{c} \rho w\\ \rho wu\\ \rho w^{2}+p^{\prime}\\ \rho w\theta \end{array}\right]=\left[\begin{array}{c} 0\\ 0\\ -\rho^{\prime}g\\ 0 \end{array}\right]$

where a “prime” quantity represents that variable with the hydrostatic background state subtracted off (not a spatial derivative).

Dimensional Splitting

This equation is solved using dimensional splitting for simplicity and speed. The equations are split into x- and z-direction solves that are, respectively:

$\large x:\,\,\,\,\,\,\,\,\,\,\frac{\partial\mathbf{q}}{\partial t}+\frac{\partial\mathbf{f}}{\partial x}=\mathbf{0}$

$\large z:\,\,\,\,\,\,\,\,\,\,\frac{\partial\mathbf{q}}{\partial t}+\frac{\partial\mathbf{h}}{\partial x}=\mathbf{s}$

Each time step, the order in which the dimensions are solved is reversed, giving second-order accuracy overall.

Finite-Volume Spatial Discretization

A Finite-Volume discretization is used in which the PDE in a given dimension is integrated over a cell domain, $\large \Omega_{i}\in\left[x_{i-1/2},x_{i+1/2}\right]$ , where $\large x_{i\pm1/2}=x_{i}\pm\Delta x$ , $\large x_{i}$ is the cell center, and $\large \Delta x$ is the width of the cell. The integration is the same in the z-direction. Using the Gauss divergence theorem, this turns the equation into (using the z-direction as an example):

$\large \frac{\partial\overline{\mathbf{q}}_{i,k}}{\partial t}=-\frac{\mathbf{h}_{i,k+1/2}-\mathbf{h}_{i,k-1/2}}{\Delta z}+\overline{\mathbf{s}}_{i,k}$

where $\large \overline{\mathbf{q}}_{i,k}$ and $\large \overline{\mathbf{s}}_{i,k}$ are the cell-average of the fluid state and source term over the cell of index i,k.

To compute the update one needs the flux vector at the cell interfaces and the cell-averaged source term. To compute the flux vector at interfaces, fourth-order-accurate polynomial interpolation is used using the four cell averages surrounding the cell interface in question.

Runge-Kutta Time Integration

So far the PDEs have been discretized in space but are still continuous in time. To integrate in time, we use a simple three-stage, linearly third-order-accurate Runge-Kutta integrator. It is solved as follows:

$\large \mathbf{q}^{\star}=\mathbf{q}^{n}+\frac{\Delta t}{3}RHS\left(\mathbf{q}^{n}\right)$

$\large \mathbf{q}^{\star\star}=\mathbf{q}^{n}+\frac{\Delta t}{2}RHS\left(\mathbf{q}^{\star}\right)$

$\large \mathbf{q}^{n+1}=\mathbf{q}^{n}+\Delta tRHS\left(\mathbf{q}^{\star\star}\right)$

When it comes to time step stability, I simply assume the maximum speed of propagation is 450,\text{m},\text{s}^{-1}, which basically means that the maximum wind speed is assumed to be 100,\text{m},\text{s}^{-1}, which is a safe assumption. I set the CFL value to 1.5 for this code.

Hyper-viscosity

The centered fourth-order discretization is unstable for non-linear equations and requires extra dissipation to damp out small-wavelength energy that would otherwise blow up the simulation. This damping is accomplished with a scale-selective fourth-order so-called “hyper”-viscosity that is defined as:

$\large \frac{\partial\mathbf{q}}{\partial t}+\frac{\partial}{\partial x}\left(-\kappa\frac{\partial^{3}\mathbf{q}}{\partial x^{3}}\right)=\mathbf{0}$

and this is also solved with the Finite-Volume method just like above. The hyperviscosity constant is defined as:

$\large \kappa=-\beta\left(\Delta x\right)^{4}2^{-4}\left(\Delta t\right)^{-1}$

where $\large \beta\in\left[0,1\right]$ is a user-defined parameter to control the strength of the diffusion, where a higher value gives more diffusion. The parameter $\large \beta$ is not sensitive to the grid spacing, and it seems that $\large \beta=0.25$ is generally enough to get rid of $\large 2\Delta x$ noise contamination.

MiniWeather Model Scaling Details

If you want to do scaling studies with miniWeather, this section will be important to make sure you're doing an apples-to-apples comparison.

sim_time: The sim_time parameter does not mean the wall time it takes to simulate but rather refers amount of model time simulated. As you increase sim_time, you should expect the walltime to increase linearly.
nx_glob, nz_glob: As a rule, it's easiest if you always keep nx_glob = nz_glob * 2 since the domain is always 20km x 10km in the x- and z-directions. As you increase nx_glob (and proportionally nz_glob) by some factor f, the time step automatically reduced by that same factor, f. Therefore, increasing nx_glob by 2x leads to 8x more work that needs to be done. Thus, with the same amount of parallelism, you should expect a 2x increase in nx_glob and nz_glob to increase the walltime by 8x (neglecting parallel overhead concerns).
- More precisely, the time step is directly proportional to the minimum grid spacing. The x- and y-direction grid spacings are: dx=20km/nx_glob and dz=10km/nz_glob. So as you decrease the minimum grid spacing (by increasing nx_glob and/or nz_glob), you proportionally decrease the size of the time step and therefore proportionally increase the number of time steps you need to complete the simulation (thus proportionally increasing the expected walltime).
The larger the problem size, nx_glob and nz_glob, the lower the relative parallel overheads will be. You can get to a point where there isn't enough work on the accelerator to keep it busy and / or enough local work to amortize parallel overheads. At this point, you'll need to increase the problem size to see better scaling. This is a typical Amdahl's Law situation.

Remember that you can control each of these parameters through the CMake configure.

Checking for Correctness

Domain-Integrated Mass and Total Energy

There are two main ways to check for correctness. The easiest is to look at the domain-integrated mass and total energy printed out at the end of the simulation.

Mass Change

In all cases for Fortran and C, the relative mass change printed out should be at machine precision (magnitude 1.e-13 or lower just to be flexible with reduced precision optimizations). If the mass changes more than this, you've introduced a bug.

For the C++ code, which uses single precision, the relative mass displayed at the end of the run should be of magnitude 1.e-9 or lower.

Total Energy Change

In order to use total energy to check the answer, you need to set the following parameters:

nx_glob: >= 100
nz_glob: >= 50 and exactly half of nx_glob
sim_time = 400
data_spec_int = DATA_SPEC_THERMAL

Also, it is assumed you have not changed any other default parameters such as xlen and zlen

From there, you can scale up to any problem size or node count you wish. The relative change in total energy should always be negative, and the magnitude should always be less than 4.5e-5. If the magnitude is larger than this, or if the value is positive, then you have introduced a bug. As you increase the problem size, the energy is always better conserved. These total energy change values are valid for single precision in C++ as well.

You can run make test from the build directory of each language folder to run all of the available tests for a given compiler and compiler flags from the CMake configure, and it will automatically check mass and total energy change for you. The tests also automatically set the number of cells, the data specification, and the simulation time for you.

Notes on Summit

On Summit, you need to have the miniWeather repo cloned in /gpfs space, and you need to have an active interactive job for make test to work.

NetCDF Files

Your other option is to create two baseline NetCDF files whose answers you trust: (1) with -O0 optimizations; and (2) with -O3 optimizations. Then, you can use the following python script to do a 3-way diff between the two baselines and the refactored code. The refactored diff should be of the same order of magnitude as the baseline compiler optimization diffs. Note that if you run for too long, non-linear chaotic amplification of the initially small differences will eventually be come too large to make for a useful comparison, so try to limit the simulation time to, say, 400 seconds or less.

The reason you have to go to all of this trouble is because of chaotic amplification of initially small differences (the same reason you can't predict weather reliably past a few days). Therefore, you can't compare snapshots to machine precision tolerance.

Click here to expand python script

import netCDF4
import sys
import numpy as np

#######################################################################################
#######################################################################################
##
## nccmp3.py: A simple python-based NetCDF 3-way comparison tool. The purpose of this
## is to show whether files 2 and 3 have more of a difference than files 1 and 2. The
## specific purpose is to compare refactored differences against presumed bit-level
## differences (like -O0 and -O3 compiler flags). This prints the relative 2-norm of
## the absolute differences between files 1 & 2 and files 2 & 3, as well as the ratio
## of the relative 2-norms between the 2-3 comparison and the 1-2 comparison.
##
## python nccmp.py file1.nc file2.nc file3.nc
##
#######################################################################################
#######################################################################################

#Complain if there aren't two arguments
if (len(sys.argv) < 4) :
  print("Usage: python nccmp.py file1.nc file2.nc")
  sys.exit(1)

#Open the files
nc1 = netCDF4.Dataset(sys.argv[1])
nc2 = netCDF4.Dataset(sys.argv[2])
nc3 = netCDF4.Dataset(sys.argv[3])

#Print column headers
print("Var Name".ljust(20)+":  "+"|1-2|".ljust(20)+"  ,  "+"|2-3|".ljust(20)+"  ,  "+"|2-3|/|1-2|")

#Loop through all variables
for v in nc1.variables.keys() :
  #Only compare floats
  if (nc2.variables[v].dtype == np.float64 or nc2.variables[v].dtype == np.float32) :
    #Grab the variables
    a1 = nc1.variables[v][:]
    a2 = nc2.variables[v][:]
    a3 = nc3.variables[v][:]
    #Compute the absolute difference vectors
    adiff12 = abs(a2-a1)
    adiff23 = abs(a3-a2)

    #Compute relative 2-norm between files 1 & 2 and files 2 & 3
    norm12 = np.sum( adiff12**2 )
    norm23 = np.sum( adiff23**2 )
    #Assume file 1 is "truth" for the normalization
    norm_denom = np.sum( a1**2 )
    #Only normalize if this denominator is != 0
    if (norm_denom != 0) :
      norm12 = norm12 / norm_denom
      norm23 = norm23 / norm_denom

    #Compute the ratio between the 2-3 norm and the 1-2 norm
    normRatio = norm23
    #If the denom is != 0, then go ahead and compute the ratio
    if (norm12 != 0) :
      normRatio = norm23 / norm12
    else :
      #If they're both zero, then just give a ratio of "1", showing they are the same
      if (norm23 == 0) :
        normRatio = 1
      #If the 1-2 norm is zero but the 2-3 norm is not, give a very large number so the user is informed
      else :
        normRatio = 1e50

    #Only print ratios that are > 2, meaning 2-3 diff is >2x more than the 1-2 diff.
    #In the future, this should be added as a command line parameter for the user to choose.
    if (normRatio > 2) :
      print(v.ljust(20)+":  %20.10e  ,  %20.10e  ,  %20.10e"%(norm12,norm23,norm23/norm12))

Further Resources

Common Problems

You cannot use -DARRAY_DEBUG on the GPU. If you do, it may segfault or give wrong answers
It appears if you build for the wrong GPU, the code often will still run but may give wrong answers.

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c		c
cpp		cpp
documentation		documentation
fortran		fortran
julia		julia
.gitmodules		.gitmodules
LICENSE		LICENSE
README.md		README.md
petascale_institute.md		petascale_institute.md

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