SeisSol with GPUs

General

The current GPU version of SeisSol targets the latest NVidia Graphics cards. Therefore, you need to have at least CUDA 10 installed in your environment. Moreover, make sure that your installed MPI implementation is CUDA-Aware. Here is a list of known CUDA-Aware MPI vendors:

  • OpenMPI

  • MVAPICH2

  • CRAY MPI

  • IBM Platform MPI

  • SGI MPI

Please, referer to the corresponding documentation if you need to install CUDA-Aware MPI manually. We also encourage you to bind your MPI with UCX communication layer if you need to manually configure CUDA-Aware MPI for a cluster or your local server with an open-source MPI implementation e.g., OpenMPI.

GPU version of SeisSol follows single rank/single GPU strategy. Therefore, if you want to run SeisSol on M nodes where each node is equipped with N GPUs then make sure that you launch SeisSol with M x N MPI processes.

To achieve the most efficient CPU-to-GPU communication and vice versa you have to pin your MPI processes to CPU cores which are the closest to the target GPUs. This problem is also known as GPU affinity. Latest versions of workload managers (e.g., SLURM) are aware of this problem and try to provide an automatic, GPU-aware process pinning. Consider the following SLURM options:

  • –ntasks-per-gpu

  • –gpu-bind

You can also enforce good GPU affinity with rankfiles if your GPU cluster or local server does not use a workload manager but is equipped with multiple GPUs per node.

Process Pinning

Correct process pinning of 4 MPI processes where each process controls 3 OpenMP threads and one communication thread.

Some systems have complex numbering of processing units and/or NUMA domains. Sometime it is very difficult to achieve desirable pinning of the communication and/or output-writing threads with HPC resource managers like SLURM. Therefore, SeisSol provides SEISSOL_FREE_CPUS_MASK environment variable which helps to describe locations of the auxiliary threads per local MPI process. The variable accepts a comma separated list of elements where an element can be either 1) an integer, or 2) a range of integers defined as [start, end] or 3) a comma separated list of integers surrounded by the curly brackets. The i-th list element describes the free cpus locations for the i-th local MPI process.

# SEISSOL_FREE_CPUS_MASK="(int | range: <int-int> | list: {int,+})+"
# Examples,
export SEISSOL_FREE_CPUS_MASK="24,28,32,36"
export SEISSOL_FREE_CPUS_MASK="24-27,28-31,32-35,36-39"
export SEISSOL_FREE_CPUS_MASK="{24,25},{28,29},{32,33},{36,37}"

# Note, it is allowed to mix the formats of list elements. For example,
export SEISSOL_FREE_CPUS_MASK="24,28-31,{28,29},36"

Supported SeisSol Features

  • elastic wave propagation model

  • kinematic point sources

  • dynamic rupture: linear slip weakening, slow and fast velocity weakening friction laws

  • off-fault plasticity model

Compilation

To start off, make sure that you already have GemmForge installed on your system. If you don’t have then follow this link.

After that, get the latest version of SeisSol

git clone --recurse-submodules https://github.com/SeisSol/SeisSol.git seissol-gpu

Compile SeisSol with (e.g.)

mkdir -p seissol-gpu/build && cd seissol-gpu/build
cmake -DDEVICE_BACKEND=cuda -DDEVICE_ARCH=sm_70 -DHOST_ARCH=skx \
-DCMAKE_BUILD_TYPE=Release -DPRECISION=double ..
make -j

The following two CMake options can be useful to improve performance:

  • USE_GRAPH_CAPTURING: enables CUDA/HIP graphs. These are used to speed up the kernel execution for elastic or anisotropic equations.

  • PREMULTIPLY_FLUX: enables the pre-multiplying of flux matrices (it was disabled for CPUs to free up cache space). This usually results in a speedup for AMD and Nvidia GPUs. By default, it is switched on when compiling for an AMD or Nvidia GPU and switched off in all other cases.

Execution

The launching process of the GPU version of SeisSol is similar as the one of the CPU version.

mpirun -n <M x N> ./SeisSol_dsm70_cuda_* ./parameters.par

It is important to know that the GPU version of SeisSol by default allocates 1GB of GPU memory at the beginning of SeisSol execution. It is necessary for fast allocation/deallocation of GPU memory needed for holding temporary data. The default value can be changed by setting a necessary one to DEVICE_STACK_MEM_SIZE environment variable. For example, the following will force SeisSol to allocate 1.5GB of stack GPU memory for temporary data:

export DEVICE_STACK_MEM_SIZE=1.5
mpirun -n <M x N> ./SeisSol_dsm70_cuda_* ./parameters.par

The following device-specific environment variables are supported right now:

  • SEISSOL_PREFERRED_MPI_DATA_TRANSFER_MODE

  • SEISSOL_SERIAL_NODE_DEVICE_INIT

Currently, SeisSol allocates MPI user-buffers using the unified/managed memory type. Some MPI implementations perform poorly during non-blocking point-to-point communication. SeisSol provides the SEISSOL_PREFERRED_MPI_DATA_TRANSFER_MODE environment variable which can be used to select the memory type for the user-buffers. The host value means that the data will be copied to/from the host memory before/after each MPI_Isend / MPI_Irecv. Setting the variable to device will result in utilizing the regular device memory for non-blocking communication. The default value is direct which means that the communication goes over the unified/managed memory and thus does not involve explicit data copies.

Data Flow Diagram

The variable “SEISSOL_SERIAL_NODE_DEVICE_INIT” exists to mitigate some possible execution bugs with regard to AMD GPU drivers. It is disabled by default and scheduled for removal long-term. To enable it, set “SEISSOL_SERIAL_NODE_DEVICE_INIT=1”. To explicitly disable it, write “SEISSOL_SERIAL_NODE_DEVICE_INIT=0”.