remove mention of P100

Author: CallumWalley

docs/Software/Available_Applications/AlphaFold.md

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

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

@@ -41,10 +41,10 @@ https://community.nanoporetech.com/
 ### Example Slurm script
 
 - Following Slurm script is a template to run Basecalling on NVIDIA
-    P100 GPUs.( We do not recommend running Guppy jobs on CPUs )
+    A100 GPUs.( We do not recommend running Guppy jobs on CPUs )
 - `--device auto` will automatically pick up the GPU over CPU
 - Also,  NeSI Mahuika cluster can provide A100 GPUs  which can be 5-6
-    times faster than P100 GPUs for Guppy Basecalling with  version. 5
+    times faster than A100 GPUs for Guppy Basecalling with  version. 5
     and above. This can be requested with
     `#SBATCH --gpus-per-node A100:1` variable
 - Config files are stored in

docs/Software/Containers/NVIDIA_GPU_Containers.md

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

docs/Software/Parallel_Computing/Parallel_Computing.md

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

docs/Tutorials/Introduction_To_HPC/Parallel.md

@@ -78,7 +78,7 @@ Create a new script called `gpu-job.sl`
 #SBATCH --account         {{config.extra.project_code}} 
 #SBATCH --output          %x.out
 #SBATCH --mem-per-cpu     2G
-#SBATCH --gpu-per-node    P100:1
+#SBATCH --gpu-per-node    A100:1
 
 module load CUDA
 nvidia-smi