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‎docs/Software/Available_Applications/AlphaFold.md‎

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@@ -112,7 +112,7 @@ Input *fasta* used in following example  is 3RGK
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#SBATCH --job-name af-2.3.2-monomer
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#SBATCH --mem 24G
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#SBATCH --cpus-per-task 8
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#SBATCH --gpus-per-node 1
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#SBATCH --gpus-per-node A100:1
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#SBATCH --time 02:00:00
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#SBATCH --output %j.out
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@@ -157,7 +157,7 @@ MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAPKPH
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#SBATCH --job-name af-2.3.2-multimer
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#SBATCH --mem 30G
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#SBATCH --cpus-per-task 4
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#SBATCH --gpus-per-node 1
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#SBATCH --gpus-per-node A100:1
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#SBATCH --time 01:45:00
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#SBATCH --output slurmout.%j.out
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@@ -207,7 +207,7 @@ modifications. Image (.*simg*) and the corresponding definition file
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#SBATCH --job-name alphafold2_monomer_example
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#SBATCH --mem 30G
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#SBATCH --cpus-per-task 6
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#SBATCH --gpus-per-node 1
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#SBATCH --gpus-per-node A100:1
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#SBATCH --time 02:00:00
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#SBATCH --output slurmout.%j.out
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@@ -246,7 +246,7 @@ singularity exec --nv /opt/nesi/containers/AlphaFold/alphafold_2.2.0.simg python
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#SBATCH --job-name alphafold2_monomer_example
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#SBATCH --mem 30G
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#SBATCH --cpus-per-task 6
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#SBATCH --gpus-per-node 1
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#SBATCH --gpus-per-node A100:1
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#SBATCH --time 02:00:00
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#SBATCH --output slurmout.%j.out
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@@ -282,11 +282,8 @@ singularity exec --nv /opt/nesi/containers/AlphaFold/alphafold_2.2.0.simg python
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1. Values for `--mem` , `--cpus-per-task` and `--time` Slurm variables
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are for *3RGK.fasta*. Adjust them accordingly
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2. We have tested this on both P100 and A100 GPUs where the runtimes
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were identical. Therefore, the above example was set to former
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via `1`
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3. The `--nv` flag enables GPU support.
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4. `--pwd /app/alphafold` is to workaround this [existing
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2. The `--nv` flag enables GPU support.
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3. `--pwd /app/alphafold` is to workaround this [existing
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issue](https://github.com/deepmind/alphafold/issues/32)
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### AlphaFold2 : Initial Release ( this version does not support `multimer`)

‎docs/Software/Available_Applications/ont-guppy-gpu.md‎

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### Example Slurm script
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- Following Slurm script is a template to run Basecalling on NVIDIA
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P100 GPUs.( We do not recommend running Guppy jobs on CPUs )
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A100 GPUs. (We do not recommend running Guppy jobs on CPUs.)
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- `--device auto` will automatically pick up the GPU over CPU
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- Also,  NeSI Mahuika cluster can provide A100 GPUs  which can be 5-6
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times faster than P100 GPUs for Guppy Basecalling with  version. 5
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and above. This can be requested with
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`#SBATCH --gpus-per-node A100:1` variable
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- Config files are stored in
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***/opt/nesi/CS400\_centos7\_bdw/ont-guppy-gpu/(version)/data/ ***
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with read permissions to all researchers (replace ***(version)***

‎docs/Software/Containers/NVIDIA_GPU_Containers.md‎

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@@ -61,7 +61,7 @@ running the NAMD image on NeSI, based on the NVIDIA instructions
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#SBATCH --time=00:10:00
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#SBATCH --ntasks=1
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#SBATCH --cpus-per-task=8
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#SBATCH --gpus-per-node P100:1
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#SBATCH --gpus-per-node A100:1
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#SBATCH --mem=1G
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module purge

‎docs/Software/Parallel_Computing/Parallel_Computing.md‎

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@@ -176,7 +176,7 @@ GPUs excel at large-scale parallel operations on matrices, making them ideal for
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#SBATCH --account nesi99991
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#SBATCH --output %x.out
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#SBATCH --mem-per-cpu 2G
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#SBATCH --gpus-per-node P100:1
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#SBATCH --gpus-per-node A100:1
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module load CUDA
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nvidia-smi

‎docs/Tutorials/Introduction_To_HPC/Parallel.md‎

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@@ -78,7 +78,7 @@ Create a new script called `gpu-job.sl`
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#SBATCH --account {{config.extra.project_code}}
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#SBATCH --output %x.out
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#SBATCH --mem-per-cpu 2G
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#SBATCH --gpu-per-node P100:1
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#SBATCH --gpu-per-node A100:1
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module load CUDA
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nvidia-smi
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```
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```out
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|===============================+======================+======================|
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| 0 Tesla P100-PCIE... On | 00000000:05:00.0 Off | 0 |
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| N/A 28C P0 24W / 250W | 0MiB / 12288MiB | 0% Default |
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| | | N/A |
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+-------------------------------+----------------------+----------------------+
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+-----------------------------------------------------------------------------+
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| Processes: |
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| GPU GI CI PID Type Process name GPU Memory |
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| ID ID Usage |
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|=============================================================================|
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| No running processes found |
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+-----------------------------------------------------------------------------+
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hu Mar 26 12:47:29 2026
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+-----------------------------------------------------------------------------------------+
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| NVIDIA-SMI 580.105.08 Driver Version: 580.105.08 CUDA Version: 13.0 |
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+-----------------------------------------+------------------------+----------------------+
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| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
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| | | MIG M. |
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|=========================================+========================+======================|
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| 0 NVIDIA A100-SXM4-80GB Off | 00000000:C7:00.0 Off | 0 |
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| N/A 31C P0 61W / 400W | 0MiB / 81920MiB | 0% Default |
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| | | Disabled |
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+-----------------------------------------+------------------------+----------------------+
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+-----------------------------------------------------------------------------------------+
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| Processes: |
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| GPU GI CI PID Type Process name GPU Memory |
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| ID ID Usage |
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|=========================================================================================|
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| No running processes found |
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+-----------------------------------------------------------------------------------------+
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```
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### Job arrays

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