UCERF3-ETAS Measurements

Page is under active construction, some sections may be incomplete. Aug 13 2024 - bhatthal@usc.edu

This page summarizes the performance study of UCERF3-ETAS ran locally in Docker and on CARC Discovery, SDSC Expanse, and TACC Frontera. This study allows for evaluation of resource requirements in single-node and multiple-node simulations of the Ridgecrest M7.1 ETAS forecast (ci38457511).

Installation and Configuration

Running ETAS simulations on OpenSHA is simplified through a collection of launcher binaries and scripts called ucerf3-etas-launcher. The process of installation and configuration varies across systems, however the foundations remain the same. Running simulations always occurs in three phases.

1. Building configuration files for a specified event, where we configure MPI nodes and number of simulations
2. Launching the simulation with the configuration files
3. Consolidating and plotting simulations data

Docker

When running UCERF3-ETAS simulations locally, using Docker allows for a consistent environment without the need to manage dependencies and the ability to easily provision resources. Download the Docker image for the M7.1 Ridgecrest main shock by running docker pull sceccode/ucerf3_jup or searching for "ucerf3_jup" on Docker Desktop. I prefer to use Docker Desktop, but the command-line is sufficient.

Under Docker Desktop settings, I allocated 14 CPUs, 96GB of RAM, 1GB of Swap, and 64GB of disk storage to the Docker environment. Open a terminal on your system with the Docker CLT installed and run docker run -d -p 8888:8888 sceccode/ucerf3_jup --name ucerf3-etas. This allows you to run a container forwarding the port 8888 for the Jupyter Notebook server. From here, you can navigate to the Jupyter Notebook web application at http://localhost:8888 to access an interactive terminal for the container. Alternatively, you can run the container directly in Docker Desktop and navigate to the "Exec" tab to access the terminal without needing Jupyter Notebook or port-fortwarding.

Once inside your container, use the following workflow to run local simulations and plot data, where $NUM_SIM is the number of simulations desired.

u3etas_comcat_event_config_builder.sh --event-id ci38457511 --mag-complete 3.5 --radius 25 --num-simulations $NUM_SIM --days-before 7 --max-point-src-mag 6 --finite-surf-shakemap --finite-surf-shakemap-min-mag 4.5 --nodes 1 --hours 24 --output-dir target/docker-comcat-ridgecrest-m7.1-n1-s$NUM_SIM

u3etas_launcher.sh $HOME/target/docker-comcat-ridgecrest-m7.1-n${NUM_SIM}/config.json | tee target/docker-comcat-ridgecrest-m7.1-n1-s${NUM_SIM}/u3etas_launcher.log

u3etas_plot_generator.sh $HOME/target/docker-comcat-ridgecrest-m7.1-n1-s${NUM_SIM}/config.json

You'll notice that we didn't specify an hpc-site parameter. As we are running these simulations locally, and not on a High Performance Computing system, we don't need to define a site to generate a corresponding Slurm file. Instead of passing a slurm file to sbatch, we can execute the launcher directly with the generated config.json file. I also pipe into the tee command to capture output for logging purposes.

In Docker Desktop, you can navigate to the "Volumes" tab to find the stored data for the containers and download them onto your host system.

Discovery

Establish a Discovery SSH or CARC OnDemand connection and clone the ucerf3-etas-launcher GitHub repository at the path /project/scec_608/$USER/ucerf3/ucerf3-etas-launcher, where $USER is your username.

Edit the bashrc file at $HOME/.bashrc to update the PATH to include the downloaded ETAS scripts and load HPC modules necessary to run ETAS in a multiple-node environment.

# .bashrc

# Source global definitions
if [ -f /etc/bashrc ]; then
        . /etc/bashrc
fi

# Switch groups, but only if necessary
if [[ `id -gn` != "scec_608" && $- =~ i ]]
then
#    echo "switching group"
    newgrp scec_608
    exit
fi

PATH=$PATH:$HOME/.local/bin:$HOME/bin
export TERM=linux

## MODULES
module load usc # this is loaded by default on login nodes, but not on compute nodes, so we need to add 'usc' here so that the subsequent modules will work
module load gcc/11.3
module load openjdk
module load git
module load vim
# every once in a while CARC breaks java, and we need this to avoid unsatisfied link errors
# if you get them looking related to libawt_xawt.so: libXext.so.6 or similar, uncommend the following
# previously encountered and then went away, but came back after may 2024 maintenence window
module load libxtst # no clue why we suddently needed this to avoid a weird JVM unsatisfied link exception

# compute nodes don't have unzip...
which unzip > /dev/null 2> /dev/null
if [[ $? -ne 0 ]];then
        module load unzip
        module load bzip2
fi

## https://github.com/opensha/ucerf3-etas-launcher/tree/master/parallel/README.md
export PROJFS=/project/scec_608/$USER
export ETAS_LAUNCHER=$PROJFS/ucerf3/ucerf3-etas-launcher
export ETAS_SIM_DIR=$PROJFS/ucerf3/etas_sim
export ETAS_MEM_GB=5 # this will be overridden in batch scripts for parallel jobs, set low enough so that the regular U3ETAS scripts can run on the login node to configure jobs
export MPJ_HOME=/project/scec_608/kmilner/mpj/mpj-current
export PATH=$ETAS_LAUNCHER/parallel/slurm_sbin/:$ETAS_LAUNCHER/sbin/:$MPJ_HOME/bin:$PATH

if [[ `hostname` == e19-* ]];then
        # on a compute node in the SCEC queue
        export OPENSHA_MEM_GB=50
        export FST_HAZARD_SPACING=0.2
        export OPENSHA_JAR_DISABLE_UPDATE=1
elif [[ -n "$SLURM_JOB_ID" ]];then
        # on a compute node otherwise
        export OPENSHA_JAR_DISABLE_UPDATE=1
        unset OPENSHA_MEM_GB
else
        export OPENSHA_MEM_GB=10
fi
export OPENSHA_FST=/project/scec_608/kmilner/git/opensha-fault-sys-tools
export OPENSHA_FS_GIT_BRANCH=master
export PATH=$PATH:$OPENSHA_FST/sbin

You'll notice that in the bashrc there are references to user "kmilner", do not change these. The files here are readable by other users and are necessary for running ETAS. There are future plans to migrate much of this code outside of the user bash file and into an MPJ Express wrapper script, to improve portability and simplify the configuration process.

After editing the bashrc file, either login and logout or run source ~/.bashrc to load the new changes.

Utilizing launcher scripts, an interactive compute node can be accessed to build configuration files directly on Discovery, as opposed to building locally and transferring over SCP/SFTP. Non-trivial jobs cannot be executed on the head node, which is why configuration files are built in such a way. Do so now by running slurm_interactive.sh. After waiting for resource provisioning, build the configuration inside the interactive compute node with

cd $ETAS_SIM_DIR && u3etas_comcat_event_config_builder.sh --event-id ci38457511 --mag-complete 3.5 --radius 25 --num-simulations $NUM_SIM --days-before 7 --max-point-src-mag 6 --finite-surf-shakemap --finite-surf-shakemap-min-mag 4.5 --hpc-site USC_CARC --nodes $NUM_NODE --hours 24 --output-dir $ETAS_SIM_DIR/discovery-comcat-ridgecrest-m7.1-n${NUM_NODE}-s$NUM_SIM

where $NUM_SIM is the number of simulations to run and $NUM_NODE is the number of nodes to utilize.

The generated configuration does require a bit of manual tweaking prior to execution. Navigate to the generated simulation directory. You'll notice that unlike before with localized Docker runs, we have a a Slurm file.

Slurm files invoke the launcher script with the config JSON file through an MPJ wrapper. MPJ, or Message Passing in Java, is utilized to enable the parallel distribution of work across the HPC compute nodes. The Slurm file also specifies the number of nodes and other parameters relevant to work distribution. In your simulation folder you should see the files "etas_sim_mpj.slurm", "plot_results.slurm", and "opensha-all.jar". If the jar file failed to copy, copy it manually from ${ETAS_LAUNCHER}/opensha/opensha-all.jar Make the following changes to etas_sim_mpj.slurm:

• Rename partition from scec -> main: #SBATCH -p main
• Ensure the ETAS JSON config path is prefixed by simulation directory: ETAS_CONF_JSON="${ETAS_SIM_DIR}/...
• Update scratch directory from scratch2 -> scratch1: SCRATCH_OPTION="--scratch-dir /scratch1/$USER/etas_scratch"
• If this simulation is on a single-node, don't invoke the MPJ wrapper:

date
echo "RUNNING ETAS-LAUNCHER"
u3etas_launcher.sh --threads $THREADS $ETAS_CONF_JSON
ret=$?
date

Additionally, inside the config.json file, update the "outputDir" to be prefixed with "${ETAS_SIM_DIR}/" prior to the output name, to prevent the creation of a duplicate folder.

After making the necessary changes, place the ETAS simulation on the job queue by running slurm_submit.sh etas_sim_mpj.slurm. You can rename the slurm file prior to submission to set the job name to more easily manage jobs. Stdout and stderr is written files to {JOB}.o{ID} and {JOB}.e{ID} respectively. Runtime is derived from the timestamps in the output file. Results are written to either a results/ directory or the binary "results_complete.bin".

Generate plots with "plot_results.slurm". Similarly, you must also update the partition name from "scec" to "main" and submit the job with slurm_submit.sh. View final plots in the generated "index.html". If you do not have a graphical session, you may need to download the simulation folder to view plots locally.

Expanse

TODO(bhatthal)

Frontera

TODO(bhatthal)

Performance Results

In the tables below, service units was computed by dividing the runtime by 60 and multiplying by the number of nodes used.

TODO(bhatthal): Update Docker Core Hours and multiply SU by 14 for Docker. Determine cores for HPC between cpus-per-task, task-per-node, ntasks, and number of nodes

Docker Measurements

Number of Nodes	Available RAM (GB)	Number of Catalogs	Runtime (min)	Service Units (SUs) Used
1	75	10	1.3	2.2E-2
1	75	100	4.4	7.3E-2
1	75	1000	25.8	4.3E-1
1	75	10,000	286.3	4.8

Dockerized local runs with a resource allocation of 14 CPU (@ 1 thread / CPU), 96GB RAM, 1GB Swap, 64GB Disk. u3etas_launcher uses 80% of available RAM, ETAS_MEM_GB=75.

Single-node measurements below were collected using ETAS_MEM_GB=32. Multi-node measurements automatically override the default value.

Discovery Measurements

Number of Nodes	Available RAM (GB)	Number of Catalogs	Runtime (min)	Service Units (SUs) Used
1	32	10	1.7	2.8E-2
1	32	100	157.2	2.6
1	32	1000	201.4	3.4
1	32	10,000	424.8	7.1
14	50	10	2.9	6.8E-1
14	50	100	2.8	6.5E-1
14	50	1000	3.6	8.4E-1
14	50	10,000	17.2	4.0
14	50	100,000	228.1	53
32	50	10	0.78	0.42
32	50	100	1.2	0.64
32	50	1000	3.9	2.1
32	50	10,000	10.5	5.6
32	50	100,000	99.2	53

Expanse Measurements

Number of Nodes	Available RAM (GB)	Number of Catalogs	Runtime (min)	Service Units (SUs) Used
1	32	10	2.9	4.8E-2
1	32	100	10.4	1.7E-1
1	32	1000	22.6	3.8E-1
1	32	10,000	207.7	3.5
14	50	10	1.8	4.2E-1
14	50	100	2.1	4.9E-1
14	50	1000	5.4	1.3
14	50	10,000	18.9	4.4
14	50	100,000	162.4	38
32	50	10	2.1	1.1
32	50	100	2.3	1.2
32	50	1000	3.4	1.8
32	50	10,000	11.3	6.0
32	50	100,000	74.8	40

TODO(bhatthal): I haven't started Frontera runs yet, although confirmed I'm able to run a Slurm job for /bin/date

Conclusion

TODO(bhatthal)