merge with JZ

benchmarks/pmwd_pm.slurm

@@ -0,0 +1,147 @@
+#!/bin/bash
+##############################################################################################################################
+# USAGE:sbatch --account=tkc@a100 --nodes=1 --gres=gpu:1 --tasks-per-node=1 -C a100  benchmarks/particle_mesh_a100.slurm 
+##############################################################################################################################
+#SBATCH --job-name=Particle-Mesh   # nom du job
+#SBATCH --cpus-per-task=8           # nombre de CPU par tache pour gpu_p5 (1/8 du noeud 8-GPU)
+#SBATCH --hint=nomultithread         # hyperthreading desactive
+#SBATCH --time=04:00:00              # temps d'execution maximum demande (HH:MM:SS)
+#SBATCH --output=%x_%N_a100.out # nom du fichier de sortie
+#SBATCH --error=%x_%N_a100.out  # nom du fichier d'erreur (ici commun avec la sortie)
+#SBATCH --exclusive                  # ressources dediees
+##SBATCH --qos=qos_gpu-dev
+# Nettoyage des modules charges en interactif et herites par defaut
+num_nodes=$SLURM_JOB_NUM_NODES
+num_gpu_per_node=$SLURM_NTASKS_PER_NODE
+OUTPUT_FOLDER_ARGS=1
+# Calculate the number of GPUs
+nb_gpus=$(( num_nodes * num_gpu_per_node))
+
+module purge
+
+echo "Job partition: $SLURM_JOB_PARTITION"
+# Decommenter la commande module suivante si vous utilisez la partition "gpu_p5"
+# pour avoir acces aux modules compatibles avec cette partition
+
+if [[ "$SLURM_JOB_PARTITION" == "gpu_p5" ]]; then
+    module load cpuarch/amd
+    source /gpfsdswork/projects/rech/tkc/commun/venv/a100/bin/activate
+    gpu_name=a100
+else
+    source /gpfsdswork/projects/rech/tkc/commun/venv/v100/bin/activate
+    gpu_name=v100
+fi
+
+# Chargement des modules
+module load nvidia-compilers/23.9 cuda/12.2.0 cudnn/8.9.7.29-cuda  openmpi/4.1.5-cuda nccl/2.18.5-1-cuda cmake
+module load nvidia-nsight-systems/2024.1.1.59
+
+
+echo "The number of nodes allocated for this job is: $num_nodes"
+echo "The number of GPUs allocated for this job is: $nb_gpus"
+
+export EQX_ON_ERROR=nan
+export ENABLE_PERFO_STEP=NVTX
+export MPI4JAX_USE_CUDA_MPI=1
+
+function profile_python() {
+    if [ $# -lt 1 ]; then
+        echo "Usage: profile_python <python_script> [arguments for the script]"
+        return 1
+    fi
+
+    local script_name=$(basename "$1" .py)
+    local output_dir="prof_traces/$gpu_name/$nb_gpus/$script_name"
+    local report_dir="out_prof/$gpu_name/$nb_gpus/$script_name"
+
+    if [ $OUTPUT_FOLDER_ARGS -eq 1 ]; then
+        local args=$(echo "${@:2}" | tr ' ' '_')
+        # Remove characters '/' and '-' from folder name
+        args=$(echo "$args" | tr -d '/-')
+        output_dir="prof_traces/$gpu_name/$nb_gpus/$script_name/$args"
+        report_dir="out_prof/$gpu_name/$nb_gpus/$script_name/$args"
+    fi
+
+    mkdir -p "$output_dir"
+    mkdir -p "$report_dir"
+
+    srun timeout 10m nsys profile -t cuda,nvtx,osrt,mpi -o "$report_dir/report_rank%q{SLURM_PROCID}" python "$@" > "$output_dir/$script_name.out" 2> "$output_dir/$script_name.err" || true
+}
+
+function run_python() {
+    if [ $# -lt 1 ]; then
+        echo "Usage: run_python <python_script> [arguments for the script]"
+        return 1
+    fi
+
+    local script_name=$(basename "$1" .py)
+    local output_dir="traces/$gpu_name/$nb_gpus/$script_name"
+
+    if [ $OUTPUT_FOLDER_ARGS -eq 1 ]; then
+        local args=$(echo "${@:2}" | tr ' ' '_')
+        # Remove characters '/' and '-' from folder name
+        args=$(echo "$args" | tr -d '/-')
+        output_dir="traces/$gpu_name/$nb_gpus/$script_name/$args"
+    fi
+
+    mkdir -p "$output_dir"
+
+    srun timeout 10m python "$@" > "$output_dir/$script_name.out" 2> "$output_dir/$script_name.err" || true
+}
+
+
+
+# run or profile
+
+function slaunch() {
+    run_python "$@"
+}
+
+function plaunch() {
+    profile_python "$@"
+}
+
+# Echo des commandes lancees
+set -x
+
+# Pour ne pas utiliser le /tmp
+export TMPDIR=$JOBSCRATCH
+# Pour contourner un bogue dans les versions actuelles de Nsight Systems
+# il est également nécessaire de créer un lien symbolique permettant de
+# faire pointer le répertoire /tmp/nvidia vers TMPDIR
+ln -s $JOBSCRATCH /tmp/nvidia
+
+# mpch=(128 256 512 1024 2048 4096)
+grid=(256 512 1024 2048 4096 8192)
+precisions=(float32 float64)
+solvers=(lpt lfm)
+
+# GPU name is a100 if num_gpu_per_node is 8, otherwise it is v100
+
+if [ $num_gpu_per_node -eq 8 ]; then
+    gpu_name="a100"
+else
+    gpu_name="v100"
+fi
+
+out_dir="pm_prof/$gpu_name/$nb_gpus"
+trace_dir="traces/$gpu_name/$nb_gpus/bench_pmwd"
+echo "Output dir is  : $out_dir" 
+echo "Trace dir is  : $trace_dir"
+
+for pr in "${precisions[@]}"; do
+    for g in "${grid[@]}"; do
+        for solver in "${solvers[@]}"; do
+           slaunch bench_pmwd.py -m $g -b $g -pr $pr -s $solver -i 4 -o $out_dir -f
+        done
+        # delete crash core dump files
+        rm -f core.python.*
+    done
+done
+
+# # zip the output files and traces
+# tar -czvf $out_dir.tar.gz $out_dir
+# tar -czvf $trace_dir.tar.gz $trace_dir
+# # remove the output files and traces
+# rm -rf $out_dir $trace_dir
+#