.gitignore

+.odp#
+*.odp#

LICENSE.md

+## Instructional Material
+
+This instructional material is
+made available under the [Creative Commons Attribution
+license][cc-by-human]. The following is a human-readable summary of
+(and not a substitute for) the [full legal text of the CC BY 4.0
+license][cc-by-legal].
+
+You are free:
+
+* to **Share**---copy and redistribute the material in any medium or format
+* to **Adapt**---remix, transform, and build upon the material
+
+for any purpose, even commercially.
+
+The licensor cannot revoke these freedoms as long as you follow the
+license terms.
+
+Under the following terms:
+
+* **Attribution**---You must give appropriate credit (mentioning that
+  your work is derived from work that is Copyright © European Molcular Biology Laboratory,
+  provide a [link to the
+  license][cc-by-human], and indicate if changes were made. You may do
+  so in any reasonable manner, but not in any way that suggests the
+  licensor endorses you or your use.
+
+**No additional restrictions**---You may not apply legal terms or
+technological measures that legally restrict others from doing
+anything the license permits.  With the understanding that:
+
+Notices:
+
+* You do not have to comply with the license for elements of the
+  material in the public domain or where your use is permitted by an
+  applicable exception or limitation.
+* No warranties are given. The license may not give you all of the
+  permissions necessary for your intended use. For example, other
+  rights such as publicity, privacy, or moral rights may limit how you
+  use the material.
+
+## Software
+
+Except where otherwise noted, the example programs
+provided by the European Molecular Biology Laboratory are made available under the
+[OSI][osi]-approved
+[MIT license][mit-license].
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+[cc-by-human]: https://creativecommons.org/licenses/by/4.0/
+[cc-by-legal]: https://creativecommons.org/licenses/by/4.0/legalcode
+[mit-license]: https://opensource.org/licenses/mit-license.html
+[ci]: http://communityin.org/
+[osi]: https://opensource.org

README.md

@@ -2,9 +2,16 @@
 
 # Intro
 
-Teaching material used during the High Performance Computing session of the EMBL Software Carpentry course.
+Teaching material for cluster computing course at EMBL.
 
-# Commands
+Directory structure:
+- `novice/`: material for the EMBL-internal Bio-IT course, _Introduction to Cluster Computing at EMBL_
+- `intermediate/`: material for the EMBL-internal Bio-IT course, _Intermediate Cluster Computing_
+- `swc/`: material for the HPC session of [Software Carpentry](https://software-carpentry.org/) workshops run at EMBL
+
+Each of the above directories has an `exercises/` subdirectory, containing files needed for exercises during the course.
+
+# Commands (for EMBL Cluster)
 
 Below are most of the commands used during the practical, so they can be copy/pasted, but I highly recommend typing along if you can.
 
@@ -16,7 +23,7 @@ ssh <username>@login.cluster.embl.de
 
 ## Clone this git repository
 ```
-git clone https://git.embl.de/msmith/embl_hpc.git
+git clone https://git.embl.de/grp-bio-it/embl_hpc.git
 ```
 
 ## Identifying our computer
@@ -46,13 +53,13 @@ srun ./hpc_example -t 10 -m 100
 
 ## Using our reserved training space
 ```
-srun --­­reservation=training ./hpc_example -­t 10 -­m 100
+srun --reservation=training ./hpc_example -t 10 -m 100
 ```
 
 ## Running in the background
 
 ```
-sbatch ­­--reservation=training ./batch_job.sh
+sbatch --reservation=training ./batch_job.sh
 ```
 
 ## Redirecting output
@@ -93,8 +100,8 @@ scancel -u <username>
 
 ## Defining time limits
 ```
-sbatch ­­--time=00­00:00:30  \
-    ­­--reservation=training \
+sbatch ­­--time=00 00:00:30  \
+      --reservation=training \
     batch_job.sh 60 500
 ```
 
@@ -105,7 +112,7 @@ seff <jobID>
 
 ## Emailing output
 ```
-sbatch ­­--mail­user=<first.last>@embl.de \
+sbatch ­­--mail-user=<first.last>@embl.de \
     ­­--reservation=training \
     ./batch_job.sh 20 500
 ```
@@ -120,7 +127,7 @@ module load BWA
 ## BWA example
 ```
 nano bwa/bwa_batch.sh
-sbatch ­­­­--reservation=training bwa/bwa_batch.sh
+sbatch --reservation=training bwa/bwa_batch.sh
 ```
 
 ## Running interactive jobs

intermediate/exercises/bwa/bwa_batch.sh

+#!/bin/bash
+#SBATCH -J bwa
+#SBATCH --time=00-00:06:00
+#SBATCH --mem=4000M
+#SBATCH --nodes=1
+#SBATCH --tmp=1G
+#SBATCH --gres=tmp:1G
+#SBATCH --output=bwa.out
+#SBATCH --open-mode=append
+
+## load required modules
+module load SAMtools BWA
+
+## copy data to /tmp and change directory to /tmp
+cp /g/its/home/pecar/benchmarks/msmith_bwa/Ecoli_genome.fa.gz $TMPDIR
+cp /g/its/home/pecar/benchmarks/msmith_bwa/reads_*.fq.gz $TMPDIR 
+cd $TMPDIR
+
+## create an index
+bwa index -p ecoli Ecoli_genome.fa.gz 
+
+## perform alignment
+bwa mem -t $SLURM_CPUS_PER_TASK ecoli reads_1.fq.gz reads_2.fq.gz > aligned.sam
+
+## create a compressed BAM file
+samtools view -b aligned.sam > aligned.bam
+
+## copy results back to where job was submitted from
+cp aligned.bam $SLURM_SUBMIT_DIR/

intermediate/exercises/gromacs/job.sh

+#!/bin/bash
+
+#SBATCH -J gromacs
+#SBATCH -N 1
+#SBATCH -c 8
+#SBATCH --hint=nomultithread
+#SBATCH -C avx
+#SBATCH -t 04:00
+
+module load GROMACS/2018.1-foss-2017b
+cp /g/its/home/pecar/benchmarks/pchen_gromacs/1OVA-AB.tpr $TMPDIR
+cd $TMPDIR
+
+gmx mdrun -s 1OVA-AB.tpr -nsteps 5000 -ntmpi 1
+tail -5 md.log
+

intermediate/exercises/gromacs/job_gpu.sh

+#!/bin/bash
+
+#SBATCH -J gromacs_gpu
+#SBATCH -N 1
+#SBATCH -c 4
+#SBATCH -p gpu
+#SBATCH --gres=gpu:1
+#SBATCH -C gpu=1080Ti
+#SBATCH -t 02:00
+
+module load GROMACS/2018.1-foss-2017b-CUDA-9.1.85
+cp /g/its/home/pecar/benchmarks/pchen_gromacs/1OVA-AB.tpr $TMPDIR
+cd $TMPDIR
+time gmx mdrun -s 1OVA-AB.tpr -nsteps 5000 -ntmpi 1 -nb gpu
+tail -5 md.log
+

intermediate/exercises/gromacs/job_mpi.sh

+#!/bin/bash
+#SBATCH -J gromacs_mpi
+#SBATCH -N 1
+#SBATCH -n 24
+#SBATCH -c 2
+#SBATCH -t 01:00
+
+module load GROMACS/2018.1-foss-2017b
+cp /g/its/home/pecar/benchmarks/pchen_gromacs/1OVA-AB.tpr $TMPDIR
+cd $TMPDIR
+
+#mpirun takes all info from slurm
+mpirun gmx_mpi mdrun -s 1OVA-AB.tpr -nsteps 5000
+
+tail -5 md.log
+

intermediate/exercises/imagej/array.job

+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -c 4
+#SBATCH --mem 4000
+#SBATCH -t 0-00:03:00
+#SBATCH --array=0-191:10%5
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+
+cd $TMPDIR
+START_TIME=$SECONDS
+
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,"$SLURM_ARRAY_TASK_ID","$((SLURM_ARRAY_TASK_ID+10))",0,0',numWorkers='8'" 
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Array $SLURM_ARRAY_TASK_ID took $ELAPSED_TIME seconds."

intermediate/exercises/imagej/manytasks.job

+#!/bin/bash
+#SBATCH -n 12
+#SBATCH -c 4
+#SBATCH --ntasks-per-node=1
+#SBATCH --mem 16000
+#SBATCH -t 0-00:10:00
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+srun -n 12 --ntasks-per-node=1 cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+srun -n 12 --ntasks-per-node=1 cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+cd $TMPDIR
+
+START_TIME=$SECONDS
+srun -n 1 /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,0,16,0,0',numWorkers='12'" &
+srun -n 1 /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,17,32,0,0',numWorkers='12'" &
+srun -n 1 /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,33,48,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,49,64,0,0',numWorkers='12'" &
+srun -n 1 /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,65,80,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,81,96,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,97,104,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,105,120,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,121,137,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,138,165,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,166,183,0,0',numWorkers='12'" &
+srun -n 1  /g/almf/software/Fiji.app/ImageJ-linux64 --mem=16000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,184,200,0,0',numWorkers='12'" &
+wait
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Elapsed time on `hostname` with $SLURM_CPUS_PER_TASK cores and 12x12: $ELAPSED_TIME"
+
+#excercise for the reader: copy results back to working dir

intermediate/exercises/imagej/parallel_12.job

+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -c 24
+#SBATCH --mem 180000
+#SBATCH -t 0-00:10:00
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+cd $TMPDIR
+
+START_TIME=$SECONDS
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,0,16,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,17,32,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,33,48,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,49,64,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,65,80,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,81,96,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,97,104,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,105,120,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,121,137,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,138,165,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,166,183,0,0',numWorkers='4'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,184,200,0,0',numWorkers='4'" &
+wait
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Elapsed time on `hostname` with $SLURM_CPUS_PER_TASK cores and 12x4: $ELAPSED_TIME"

intermediate/exercises/imagej/parallel_2.job

+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -c 24
+#SBATCH --mem 180000
+#SBATCH -t 0-00:10:00
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+
+cd $TMPDIR
+
+START_TIME=$SECONDS
+
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,0,100,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,101,200,0,0',numWorkers='24'" &
+wait
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Elapsed time with $SLURM_CPUS_PER_TASK cores and 2x24: $ELAPSED_TIME"

intermediate/exercises/imagej/parallel_4.job

+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -c 24
+#SBATCH --mem 180000
+#SBATCH -t 0-00:10:00
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+cd $TMPDIR
+
+START_TIME=$SECONDS
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,0,50,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,51,100,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,101,150,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,151,200,0,0',numWorkers='24'" &
+wait
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Elapsed time on `hostname` with $SLURM_CPUS_PER_TASK cores and 4x24: $ELAPSED_TIME"

intermediate/exercises/imagej/parallel_6.job

+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -c 24
+#SBATCH --mem 180000
+#SBATCH -t 0-00:10:00
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+cd $TMPDIR
+
+START_TIME=$SECONDS
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,0,33,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,34,66,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,67,99,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,100,133,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,134,166,0,0',numWorkers='24'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,167,200,0,0',numWorkers='24'" &
+wait
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Elapsed time on `hostname` with $SLURM_CPUS_PER_TASK cores and 6x24: $ELAPSED_TIME"

intermediate/exercises/imagej/parallel_8.job

+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -c 24
+#SBATCH --mem 180000
+#SBATCH -t 0-00:10:00
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+cd $TMPDIR
+
+START_TIME=$SECONDS
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,0,25,0,0',numWorkers='12'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,26,50,0,0',numWorkers='12'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,51,75,0,0',numWorkers='12'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,76,100,0,0',numWorkers='12'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,101,125,0,0',numWorkers='12'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,126,150,0,0',numWorkers='12'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,151,175,0,0',numWorkers='12'" &
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,176,200,0,0',numWorkers='12'" &
+wait
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Elapsed time on `hostname` with $SLURM_CPUS_PER_TASK cores and 8x12: $ELAPSED_TIME"

intermediate/exercises/imagej/serial.job

+#!/bin/bash
+#SBATCH -N 1
+#SBATCH -c 24
+#SBATCH --mem 180000
+#SBATCH -t 0-01:00:00
+
+module load Java
+module load X11
+
+mkdir -p ~/.imagej
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/IJ_Prefs.txt ~/.imagej/
+
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/fib-sem--cell--8x8x8nm.tif $TMPDIR/
+cp /g/its/home/pecar/benchmarks/tischer_fib-sem-cell-crop/bg-er.classifier $TMPDIR/
+
+cd $TMPDIR
+
+START_TIME=$SECONDS
+
+/g/almf/software/Fiji.app/ImageJ-linux64 --mem=32000M --ij2 --allow-multiple --headless --run  "Apply Classifier" "inputImageFile='fib-sem--cell--8x8x8nm.tif',memoryMB='32000',quitAfterRun='true',classifierFile='bg-er.classifier',dataSetID='fib-sem--cell--8x8x8nm',outputModality='Save class probabilities as Tiff slices',outputDirectory='.',inputModality='Open using ImageJ1 virtual',saveResultsTable='false',classificationIntervalXYZT='0,496,0,516,0,200,0,0',numWorkers='128'" 
+
+ELAPSED_TIME=$(($SECONDS - $START_TIME))
+
+echo "Elapsed time on `hostname` with $SLURM_CPUS_PER_TASK cores: $ELAPSED_TIME"

intermediate/exercises/tomsa/run.sh

+#!/bin/bash
+#SBATCH -J tomsa_mpi
+#also try -N with --ntasks-per-node
+#SBATCH -n 24
+#SBATCH -C "net10G|net25G"
+#SBATCH --switches=1
+#SBATCH --mem-per-cpu=20
+#SBATCH --time=00:05:00 
+
+module load foss/2017b
+
+cd $SCRATCHDIR
+tar zxf /g/its/home/pecar/benchmarks/turonova_tomsa/data.tgz
+
+START=$SECONDS
+mpirun tomsa -param params.txt -folder ./results
+echo took $((SECONDS-START)) seconds witn $SLURM_NTASKS tasks across $SLURM_NNODES nodes
+
+#cp results/* $SLURM_SUBMIT_DIR/