1. Workshop: Using the VIC3 Cluster for Statistical Analyses Support perspective G.J. Bex

2. Overview Cluster VIC3: hardware & software Statistics research scenario Worker framework MapReduce with Worker Q&A

3. Birds eye view of VIC3 login1 login2 svcs1 svcs2 r1i0n0 r1i0n1 r1i3n15 r2i0n0 r2i0n1 netapp ~vsc30034 /bin r2i3n15

4. VIC3 nodes Compute nodes – 112 nodes with 2 quad core 'harpertown', 8GB RAM – 80 nodes with 2 quad core 'nehalem', 24GB RAM – 6 nodes with 2 quad core 'nehalem', 72 GB RAM and local hard disk Storage – 20 TB disk space shared between home directories and scratch space, access via NFS – 4 nodes with disks for a parallel file system (needed for MPI I/O jobs) Service nodes include 2 login nodes 1584 cores, for  16.6 TFlop (theoretical peak)

5. What can you run? All open source linux software All linux software the K.U.Leuven has a license for that covers the cluster, and you are a K.U.Leuven staff member All linux software you have a license for that covers the cluster No Windows software R, SAS, MATLAB are ok for K.U.Leuven & UHasselt users

6. Overview Cluster VIC3: hardware & software Statistics research scenario Worker framework MapReduce with Worker Q&A

7. Running example: SAS code Your SAS program, e.g., ' clmk.sas ' – is usually interactive – depends on parameters, e.g., type of distribution alpha, beta – has to be run for several types and values of alpha and beta

8. Running example: batch mode 1 st step: convert it for batch mode – capture command line variables: – run it from the command line: … %LET type = "%scan(&sysparm, 1, %str(:))"; %LET alpha = %scan(&sysparm, 2, %str(:)); %LET beta = %scan(&sysparm, 3, %str(:)); … $ sas –batch –noterminal –sysparm discr:1.3:15.0 clmk.sas

9. login I've got a job to do: PBS files compute nodes queue system/scheduler: Torque/Moab #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm discr:1.3:15.0 clmk.sas #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm discr:1.3:15.0 clmk.sas clmk.pbs $ msub clmk.pbs

10. No more modifying! #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm discr:1.3:15.0 clmk.sas #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm discr:1.3:15.0 clmk.sas $ msub clmk.pbs #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm $type:$alpha:$beta clmk.sas #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm $type:$alpha:$beta clmk.sas $ msub clmk.pbs –v type=discr,alpha=1.3,beta=15.0

11. Going parallel… or nuts? Parameter sets… – are independent, so computations can be done in parallel! – but all combination of type, alpha, beta: large number of jobs Worker framework

12. Overview Cluster VIC3: hardware & software Statistics research scenario Worker framework MapReduce with Worker Q&A

13. Conceptually typealphabeta discr1.315.0 discr1.330.0 discr1.815.0 discr1.830.0 ……… cont1.315.0 ……… #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm $type:$alpha:$beta clmk.sas #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm $type:$alpha:$beta clmk.sas

14. Concrete typealphabeta discr1.315.0 discr1.330.0 discr1.815.0 discr1.830.0 ……… cont1.315.0 ……… #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm $type:$alpha:$beta clmk.sas #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR sas -batch –noterminal \ -sysparm $type:$alpha:$beta clmk.sas clmk.pbs clmk.csv $ module load worker/1.0 $ wsub –data clmk.csv –batch clmk.pbs -l nodes=2:ppn=8 N N rows will be computed in parallel by 2 × 8 – 1 = 15 cores

15. Caveat 1: time is of the essence… How long does your job need? (= walltime) – time to compute N rows/requested cores walltime limitations – more than 5 minutes – less than 2 days hence, if walltime exceeds 2 days, split data and submit multiple jobs explicitly request sufficient walltime: No hard limits, but guidelines to reduce queue time $ wsub –data clmk.csv –batch clmk.pbs \ -l nodes=2:ppn=8,walltime=36:00:00

16. Caveat 2: slave labour P cores, how to choose P? – functions 1 master P – 1 slaves – each compute node has 8 cores, so P mod 8 = 0 – N  >> P: better load balancing, efficiency – larger P shorter walltime (potentially) longer time in queue shortest turn-around: hard to predict turn-around = queue time + walltime

17. Caveat 3: independence #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR log_name="clmk-$type-$alpha-$beta.log" print_name="clmk-$type-$alpha-$beta.lst" sas -batch –noterminal \ -log $log_name \ -print $print_name \ -sysparm $type:$alpha:$beta clmk.sas #!/bin/bash –l module load SAS/9.2 cd $PBS_O_WORKDIR log_name="clmk-$type-$alpha-$beta.log" print_name="clmk-$type-$alpha-$beta.lst" sas -batch –noterminal \ -log $log_name \ -print $print_name \ -sysparm $type:$alpha:$beta clmk.sas SAS locks log and output files! Make sure each computation writes to its own files!

18. Overview Cluster VIC3: hardware & software Statistics research scenario Worker framework MapReduce with Worker Q&A

19. Conceptually: MapReduce data.txt data.txt.1 data.txt.2 data.txt.7 … result.txt result.txt.1 result.txt.2 result.txt.7 … map reduce

20. Concrete: -prolog & -epilog data.txt data.txt.1 data.txt.2 data.txt.7 … result.txt result.txt.1 result.txt.2 result.txt.7 … prolog.shepilog.shprolog.sh batch.sh $ wsub –prolog prolog.sh –batch batch.sh \ –epilog epilog.sh –l nodes=3:ppn=8

21. Overview Cluster VIC3: hardware & software Statistics research scenario Worker framework MapReduce with Worker Q&A

22. Where to find help? http://www.vscentrum.be/vsc-help-center hpcinfo@icts.kuleuven.be http://status.kuleuven.be/hpc UHasselt staff: geertjan.bex@uhasselt.be