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https://github.com/smyalygames/FiniteVolumeGPU.git
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Added arguments for job script
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@ -6,7 +6,7 @@
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#SBATCH --account=nn9882k
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#SBATCH --account=nn9882k
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#
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#
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# Wall clock limit:
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# Wall clock limit:
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#SBATCH --time=00:20:00
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#SBATCH --time=00:02:00
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#
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#
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# NOTE: See https://documentation.sigma2.no/jobs/projects_accounting.html when adjusting the values below
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# NOTE: See https://documentation.sigma2.no/jobs/projects_accounting.html when adjusting the values below
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#
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#
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@ -28,7 +28,7 @@
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# source /cluster/bin/jobsetup
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# source /cluster/bin/jobsetup
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module restore system # instead of 'module purge' rather set module environment to the system default
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module restore system # instead of 'module purge' rather set module environment to the system default
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module load CUDA/10.2.89
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module load CUDA/11.4.1
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# It is also recommended to to list loaded modules, for easier debugging:
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# It is also recommended to to list loaded modules, for easier debugging:
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module list
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module list
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@ -41,7 +41,7 @@ mkdir $SCRATCH/ShallowWaterGPU
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cp -r . $SCRATCH/ShallowWaterGPU
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cp -r . $SCRATCH/ShallowWaterGPU
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## Make sure the results are copied back to the submit directory (see Work Directory below):
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## Make sure the results are copied back to the submit directory (see Work Directory below):
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# chkfile MyResultFile
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# chkfile MyResultFileq
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# chkfile is replaced by 'savefile' on Saga
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# chkfile is replaced by 'savefile' on Saga
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savefile "$SCRATCH/ShallowWaterGPU/*.log"
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savefile "$SCRATCH/ShallowWaterGPU/*.log"
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savefile "$SCRATCH/ShallowWaterGPU/*.nc"
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savefile "$SCRATCH/ShallowWaterGPU/*.nc"
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@ -6,26 +6,26 @@
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#SBATCH --account=nn9882k
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#SBATCH --account=nn9882k
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#
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#
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# Wall clock limit:
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# Wall clock limit:
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#SBATCH --time=10:00:00
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#SBATCH --time=00:10:00
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#
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# NOTE: See https://documentation.sigma2.no/jobs/projects_accounting.html when adjusting the values below
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#
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#
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# Ask for 1 GPU (max is 2)
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# Note: The environment variable CUDA_VISIBLE_DEVICES will show which GPU
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# Note: The environment variable CUDA_VISIBLE_DEVICES will show which GPU
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# device(s) to use. It will have values '0', '1' or '0,1' corresponding to
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# device(s) to use. It will have values '0', '1' or '0,1' corresponding to
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# /dev/nvidia0, /dev/nvidia1 or both, respectively.
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# /dev/nvidia0, /dev/nvidia1 or both, respectively.
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#SBATCH --partition=accel --gres=gpu:1
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#SBATCH --partition=accel
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#
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#
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# Max memory usage per task (core) - increasing this will cost more core hours:
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# Max memory usage per task (core) - increasing this will cost more core hours:
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#SBATCH --mem-per-cpu=16G
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#SBATCH --mem-per-cpu=3800M
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#
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#
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# Number of tasks:
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#SBATCH --qos=devel
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#SBATCH --nodes=1 --ntasks-per-node=1
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## Set up job environment: (this is done automatically behind the scenes)
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## Set up job environment: (this is done automatically behind the scenes)
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## (make sure to comment '#' or remove the following line 'source ...')
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## (make sure to comment '#' or remove the following line 'source ...')
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# source /cluster/bin/jobsetup
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# source /cluster/bin/jobsetup
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module restore system # instead of 'module purge' rather set module environment to the system default
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module restore system # instead of 'module purge' rather set module environment to the system default
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module load CUDA/10.2.89
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module load CUDA/11.4.1
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# It is also recommended to to list loaded modules, for easier debugging:
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# It is also recommended to to list loaded modules, for easier debugging:
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module list
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module list
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@ -47,5 +47,5 @@ savefile "$SCRATCH/ShallowWaterGPU/*.json"
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## Do some work:
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## Do some work:
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cd $SCRATCH/ShallowWaterGPU
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cd $SCRATCH/ShallowWaterGPU
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srun $HOME/.conda/envs/ShallowWaterGPU_HPC/bin/python3 --version
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srun $HOME/.conda/envs/ShallowWaterGPU_HPC/bin/python3 --version
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srun $HOME/.conda/envs/ShallowWaterGPU_HPC/bin/python3 mpiTesting.py -nx 1024 -ny 1024 --profile
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srun $HOME/.conda/envs/ShallowWaterGPU_HPC/bin/python3 mpiTesting.py -nx $NX -ny $NY --profile
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@ -1,13 +1,13 @@
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#!/bin/bash
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#!/bin/bash
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# one node: 1-4 tasks/GPUs
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# one node: 1-4 GPUs
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sbatch --partition=accel --gres=gpu:1 --nodes=1 --ntasks-per-node=1 saga_strong_scaling_benchmark.job
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sbatch --nodes=1 --gpus-per-node=1 --ntasks-per-node=1 --export=ALL,NX=1024,NY=1024 saga_strong_scaling_benchmark.job
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sbatch --partition=accel --gres=gpu:2 --nodes=1 --ntasks-per-node=2 saga_strong_scaling_benchmark.job
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sbatch --nodes=1 --gpus-per-node=2 --ntasks-per-node=2 --export=ALL,NX=1024,NY=512 saga_strong_scaling_benchmark.job
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sbatch --partition=accel --gres=gpu:3 --nodes=1 --ntasks-per-node=3 saga_strong_scaling_benchmark.job
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sbatch --nodes=1 --gpus-per-node=3 --ntasks-per-node=3 --export=ALL,NX=1024,NY=341 saga_strong_scaling_benchmark.job
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sbatch --partition=accel --gres=gpu:4 --nodes=1 --ntasks-per-node=4 saga_strong_scaling_benchmark.job
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sbatch --nodes=1 --gpus-per-node=4 --ntasks-per-node=4 --export=ALL,NX=512,NY=512 saga_strong_scaling_benchmark.job
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# 2-4 nodes: 4 tasks/GPUs per node
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# 2-4 nodes: 1 GPUs per node
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sbatch --partition=accel --gres=gpu:4 --nodes=2 --ntasks-per-node=4 saga_strong_scaling_benchmark.job
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sbatch --nodes=2 --gpus-per-node=1 --ntasks-per-node=1 --export=ALL,NX=1024,NY=512 saga_strong_scaling_benchmark.job
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sbatch --partition=accel --gres=gpu:4 --nodes=3 --ntasks-per-node=4 saga_strong_scaling_benchmark.job
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sbatch --nodes=3 --gpus-per-node=1 --ntasks-per-node=1 --export=ALL,NX=1024,NY=341 saga_strong_scaling_benchmark.job
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sbatch --partition=accel --gres=gpu:4 --nodes=4 --ntasks-per-node=4 saga_strong_scaling_benchmark.job
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sbatch --nodes=4 --gpus-per-node=1 --ntasks-per-node=1 --export=ALL,NX=512,NY=512 saga_strong_scaling_benchmark.job
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