Introduction to UBELIX
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1 Science IT Support (ScITS) Michael Rolli, Nico Färber Informatikdienste Universität Bern , Introduction to UBELIX
2 Agenda > Introduction to UBELIX (Overview only) Other topics spread in > Introducing Slurm (with Exercises) > Bad Real-Life Examples > Disk Space Management / Local Scratch 2
3 Who We Are > Dr. med. Michael Rolli Medical degree at UniBE : Institute of Medical Education (IML) Software Developer (E-Learning stuff for curriculum) & Linux Sysadmin since fulltime Sysadmin UBELIX > Nico Färber M.Sc. in Computer Science at UniBE 2016 Fulltime Sysadmin UBELIX 3
4 Before we dive in > anybody not yet subscribed to our mailing list should do so: > We send information regarding UBELIX using this communication channel (only 2-10 mail per month!) 4
5 About UBELIX System Overview 5
6 And this is how it looks like 6
7 Keep in mind throughout the course! The most important thing about cluster usage is. 7
8 Keep in mind throughout the course! KNOW YOUR SOFTWARE! Know how it works. Know what it s doing. Know what it needs and its characteristics. Know how it uses resources. Know its dependencies. 8
9 Introducing SLURM 9
10 Important Commands SLURM Command sbatch squeue scontrol sacct scancel salloc srun Description Allocate resources, submit a batch job for later execution List active jobs Show detailed information about active jobs Query information about past jobs Delete active jobs Allocate resources, start interactive session Run parallel tasks within allocation > Do not use sacct/squeue/scontrol too frequently. Those commands are expensive in terms of RPC calls! > watch n 120 squeue --user=$user --state=r # every 2 minutes is enough 10
11 Cluster Partitions > 4 important cluster partitions > Each partition with different specifications/restrictions Partition Default Runtime Max Runtime Default Memory per CPU Max Memory on Node Max CPUs per Node all* 1h 96h 2GB 252GB 16 empi 1h 24h 2GB 125GB 20 phi 1h 24h 2GB 108GB 256 long 1h 360h 2GB 94GB 24 > Access to the long partition must be requested on a per-user basis! à Mail to grid-support@id.unibe.ch > Default partition: all 11
12 sbatch Job Submission > Submit a batch job for later execution. > Request resources from Slurm, e.g: --time --cpu-per-tasks --nodes --ntasks-per-node --mem-per-cpu > Other options, e.g: --mail-user --mail-type --output/--error > Upon job start, Slurm sets environment variables that are available in your job script, e.g: SLURM_CPUS_PER_TASK SLURM_MEM_PER_CPU > There are many more options and environment variables: man sbatch 12
13 squeue/scontrol Show Job Information > Use the squeue command to answer questions like: What is the state of a job? What are the IDs of my jobs? When will a pending job start? (info not avail for all jobs) Where is a job running? How long is a job already running? Why is my job still pending? > Customizable output format: squeue --user=$user --state=r --format= jobid,jobname,partition,ncpus,nodes,state > Use the scontrol command to answer questions like: Where are the output/error files of a job? To which processors is my job restricted?... > More information available here: man squeue and man scontrol 13
14 sacct Show Past Jobs > By default only jobs from the current day are displayed. To display previous jobs use date options: sacct --starttime= sacct --starttime= endtime= > Display information of a specific job: sacct --job=<job ID> > Display all jobs in a specific state (must use date options!) sacct --starttime= state=f (show all jobs since 1 January 2017 with state FAILED) > Customize output format with the --format option > Use man sacct for further information 14
15 scancel - Delete Active Jobs > You can only delete your own jobs! > Examples: scancel scancel scancel --state=pd (this will delete all pending jobs!) > Specify the job ID of an array job with an array tasks ID to delete individual array tasks: scancel _4 scancel (this will delete all array tasks!) > More information: man scancel 15
16 EX1: Login and submit your first job > Login to the cluster: ssh > Make a workshop directory and copy the job-template therein mkdir p workshop/ex1_simple_job; cd workshop/ex1_simple_job/ cp -r /gpfs/software/workshop/job_script_template.sh simple_job.sh > Add your address and adapt mail-type accordingly: #SBATCH --mail-user=<your address> #SBATCH --mail-type=end,fail > Add code to print all environment variables: echo Environment Variables: env && sleep 60 > Submit the job to the cluster: JOBID=$(sbatch --parsable job_script_template.sh) > Check the current status of your job: squeue --job=$jobid > When the job has finished, examine all environment variables from the job output 16
17 EX1: sent by Slurm 17
18 Array Jobs > Submit collections of similar jobs as one array job Easier to manage large number of jobs Positive effect on scheduler performance! > Suitable for jobs with identical resource requirements, that are controllable by parameters assigned, i.e. one pipeline (same procedure) for many datasets. > Use the --array option to submit an array job: --array=1-20%4 (20 array tasks, run max 4 tasks simultaneously) --array=0-16:4 (5 array tasks with ID: 0,4,8,12,16) > You can use various environment variables to customize the array tasks: SLURM_ARRAY_TASK_ID SLURM_ARRAY_TASK_MAX > More on array jobs: man sbatch 18
19 Environment Modules (1/2) > UBELIX provides centrally managed software and often different versions of the same software > UBELIX provides software for Bioinformatics built by the Vital-IT project. > Several packages are available in different versions Intel Compiler, PGI Compiler, Matlab, Gaussian,... > Use environment modules to load a specific version => modules prepare your shell environment ($PATH, $MANPATH, ) > Do not forget to add the module load command to your job script too! 19
20 Environment Modules (2/2) > module list list currently loaded modules > module avail list all available modules > module load <name/version> load a specific module > module unload <name/version> unload a loaded module > Examples: module load intel[/2016] (if ver omitted à latest gets loaded) module load vital-it && module load R/
21 EX2: Submit an array job > Copy all job examples to your home directory and change to ex2 job folder: cp r /gpfs/software/workshop/* $HOME/workshop/ cd $HOME/workshop/ex2_array_job > In job.slurm adapt mail-user and mail-type options > Run job with 10 array tasks, run max. 5 array tasks concurrently: #SBATCH --array=10%5 > In R file use environment variable to refer to input data set and output file: SLURM_ARRAY_TASK_ID > Submit the job, and check the state of the different array tasks: JOBID=$(sbatch --parsable job.slurm) watch squeue --job=$jobid > The example makes use of the R package matrixstats from CRAN, which does not compile with GCC (standard C compiler) anymore! 21
22 Local Parallel Computation (SMP) > Possible benefit from parallelization and optimal degree of parallelization is problemdependent > No generic way for converting a sequential job into a parallel job! > Slurm does not prevent your software from running more processes/threads than CPUs are allocated to your job. BUT: Slurm will restrict all processes/threads to the allocated CPUs à a lot of context switching à inefficient > Use --cpus-per-task to request CPUs for shared memory jobs. Slurm will allocate the CPUs on the same node! > Use environment variables to refer to allocated resources: $SLURM_CPUS_PER_TASK, $SLURM_MEM_PER_CPU,... > Requesting resources in Slurm (e.g. multiple CPUs) does not make your job parallel. Each software has its own mechanisms to make use of parallelism! 22
23 A side note on Context Switching (1/2) > In computing, a context switch is the process of storing and restoring the state (more specifically, the execution context) of a process or thread so that execution can be resumed from the same point at a later time. This enables multiple processes to share a single CPU and is an essential feature of a multitasking operating system. > Context-switch time is overhead; the system does no useful work while switching! 23
24 A side note on Context Switching (1/2) See 24
25 EX3: Parallel MATLAB > Change directory to MATLAB folder and adapt mail options as before > The Parallel Computing Toolbox provides parallel workers to execute code in parallel, locally on a multi-core machine > Request certain number of CPUs from Slurm: #SBATCH --cpus-per-task=8 > Among others, Slurm will set the environment variable SLURM_CPUS_PER_TASK to the number of CPUs allocated to the job > Use this variable to create the pool of parallel workers: parpool('local',str2num(getenv('slurm_cpus_per_task'))); > Release the workers when the parallel computation is done: delete(gcp); 25
26 EX4: Gaussian > Change directory to Gaussian folder and adapt mail options as before > Load Gaussian09: module load gaussian/g09 > Always use the same number of CPUs/amount of memory as allocated by Slurm: %nprocshared=12 %mem=24576mb (...) > Or script it using Slurm environment variables: GAUSSIAN_CPU=${SLURM_CPUS_PER_TASK} GAUSSIAN_MEM=$((SLURM_CPUS_PER_TASK * SLURM_MEM_PER_CPU)) echo -e "%nprocshared=${gaussian_cpu}\n%mem= \ ${GAUSSIAN_MEM}MB\n$(cat gtemplate.gjf)" > ginput.gjf > With short input files you can also use Heredoc syntax, ask Google for details. 26
27 Debugging Jobs > Different reasons why a job can fail: Job exceeds allocated resources (time, memory) Bug in your code Node failure > Exit code i.e. X:Y X = exit code of the job script ; Y = if > 0 à signal that lead to job abortion (i.e. SIGKILL à 9) > Check the Slurm error/output files of the job for any hint > Check any program-specific output file, e.g: R writes output by default to <R script>out, e.g: fit.rout > If you ask for help: Provide the job id of the problematic job Provide the path to the job-script, logfiles and/or program output Describe the problem sufficiently (What do you wanted to do? What happened? What did you expect to happen?) 27
28 EX5: Parallel R (1/2) > Change directory to R folder and adapt mail options as before > R provides different packages for parallelization. Here we use the doparallel and foreach packages > Install the required packages: > Load the packages: library(foreach) library(doparallel) > Request a certain number of CPUs from Slurm: #SBATCH --cpus-per-task=8 > Register the parallel backend. Again, use the corrsponding environment variable: registerdoparallel(cores=sys.getenv( SLURM_CPUS_PER_TASK ) > Close the implicitly created cluster: stopimplicitcluster() 28
29 EX5: Parallel R (2/2) > Submit the job. Why did the job fail? > Fix the problem and submit the job again > Important remark: If cores is not specified with registerdoparallel(), cores is set to one-half the number of cores detected by the parallel package: e.g. on a hnode: > detectcores() [1] 16 > getdoparworkers() [1] 8 > Problem: Without specifying a number of cores, R may uses more parallel workers than cores were allocated to the job -> Context switching -> Performance degradation! > With R it is also possible to run code in parallel on multiple nodes (not discussed here): hint: makecluster() 29
30 Command-Line vs. Embedded Options > You can specify options either on the command line or as embedded flags in the job script. Options specified on the command line take precedence > Command-line options: sbatch --mem-per-cpu=2g (...) <job script> > Embedded options: #!/bin/bash #SBATCH --mem-per-cpu=2g (...) > We recommend using embedded options for default values and command-line options to overwrite certain default values, e.g. for comparing the performance of a job for different numbers of CPUs 30
31 EX6: Parallel R revised > Submit the same R job (exercise 5 remove the line with sleep before submitting!) multiple times, each time with a different degree of parallelization: for i in {1,2,4,8}; do sbatch --cpus-per-task=$i <job script> done > Check the output files and compare the performance of each computation. > This is simple benchmarking and shows to what extend solving the problem scales by using more and more resources. 31
32 Distributed Parallel Computation (MPI) > Distribute parallel computation over multiple nodes. Communication between nodes using message passing interface (MPI) > Job not limited by number of CPUs and memory available of a single node > Communication overhead compared to shared memory à Use all CPUs on each node (if memory is not the limiting factor) > Use constraint=<feature> to request a homogeneous set of nodes (i.e. same CPU technology): > --constraint=sandy > Use --ntasks or --nodes with --ntasks-per-node instead of --cpus-per-tasks to request CPUs on multiple nodes > Use environment variable SLURM_NTASKS to refer to the number CPUs allocated to the job 32
33 EX7: Hello World (Open MPI) in C > Change directory to OpenMPI folder and adapt mail options as before > Hello World example was compiled using OpenMPI , which itself was compile using gcc à load the correct dependencies! module load openmpi/ gcc > Use srun to start/manage the parallel execution (mpirun also possible): srun --mpi=pmi2 mpi_hello_world mpirun mpi_hello_world > OpenMPI compiled with Slurm support: No machine file needed Number of allocated CPUs implicitly known > Examine the output file to verify that the computation was really distributed among multiple nodes as requested by the allocation requested in the job script. 33
34 Bad Real-Life Examples How Not to Do It! 34
35 Example 1 (1/2) > Can you spot the problem? bash ~# scontrol -dd show job (...) Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=* Nodes=hnode26 CPU_IDs=13 Mem=32768 GRES_IDX= MinCPUsNode=1 MinMemoryCPU=32G MinTmpDiskNode=0 Features=(null) DelayBoot=00:00:00 (...) hnode26 ~# top -u ***** PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND ***** m 203m 12m R : Pcrystal ***** m 201m 13m R : Pcrystal ***** m 367m 12m R : Pcrystal ***** m 207m 13m R : Pcrystal ***** m 204m 13m R : Pcrystal (...) ***** m 204m 11m R : Pcrystal ***** m S : slurm_script ***** m S : runubelixcry ***** S : mpirun 35
36 Example 1 (2/2) > Job requests only 1 CPU (default if nothing specified), but spawns 22 processes: #!/bin/bash #SBATCH --mail-user=***** #SBATCH --mail-type=fail,end #SBATCH --job-name=***** #SBATCH --time=96:00:00 #SBATCH --mem-per-cpu=32g module load openmpi/ \ intel/home/ubelix/*****/software/crystal14/utils14/runubelixcry14 22 ***** ***** > Problem: 22 processes share a single CPU -> Context switching -> Performance loss! > This results in a high load on the node: hnode26 ~ # uptime 17:03:07 up 21 days, 8:05, 1 user, load average: 30.04, 29.97, > Take-Home Message: Always request the correct number of CPUs Use SLURM environment variables: module load openmpi/1.6.5-intel/home/ubelix/*****/software/ \ CRYSTAL14/utils14/runubelixcry14 $SLURM_NTASKS ***** ***** 36
37 Example 2 (1/2) > Can you spot the problem? #!/bin/bash # You must specify a valid address! #SBATCH --mail-user=***** # Runtime and memory #SBATCH --time=24:00:00 #SBATCH --mem-per-cpu=2g #SBATCH --nodes=96 #SBATCH --ntasks-per-node=1 #SBATCH --workdir=. # For array jobs # Array job containing 100 tasks, run max 10 tasks at the same time ##SBATCH --array=1-100%10 #### Your shell commands below this line #### module load openmpi/ gccmpirun pflotran -pflotranin ISP-Steel-all_hex.in 37
38 Example 2 (2/2) > Job requests 96 nodes but only 1 CPU and 2G of memory per node! > Job runs on heterogeneous set of nodes > Waste of resources: We advise our users to request all CPUs on a node. Requesting only one CPU on each of the 96 nodes renders the remaining CPUs unusable for other jobs > Bad job performance: Increased communication overhead between distributed MPI processes Processes running on fast CPUs wait for processes running on slower CPUs > Take-Home Message (for parallel jobs): Minimize the number of nodes by requesting all CPUs on a node Constrain job to use homogeneous set of nodes (--constraint) 38
39 Other Topics 39
40 Disk Space Management > We provide free storage to our users (default: 3TB) > Before you request more storage, clean up your home directory. Storage space is scarce and valuable! > UBELIX does not serve as an archive nor as a backup solution for your data! > Delete or move old data to your private storage. > Data in your home directory is NOT backed-up! It is your responsibility to backup your data. 40
41 Local Scratch > Relieve the shared file system by using the local harddisk > Use --tmp option to request nodes with a minimum amount of available disk space: --tmp=2048 (Request 2048MB (2GB) of disk space) > Environment variable TMPDIR refers to the job directory on the local scratch: cp r $HOME/<inputdata> $TMPDIR (perform computations on the local data) mv $TMPDIR/<results> $HOME/ > On job termination all remaining data on the local scratch will be deleted by Slurm. Move all results to your home directory before the job finishes! > Use this especially if you are dealing (reading and particularly writing!) thousands of files. Archive them before copying back to your home directory using the tar command. 41
42 Important Resources > UBELIX Documentation: > > UBELIX Job Monitoring: > > Support from the UBELIX admins: > Mail to: > Learning the Shell: > > Bash Shell Scripting: > 42
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