The software in this repo generates monthly sponsor and user reports for the Research Computing clusters. Below is an example sponsor report:
Sponsor: Garegin Andrea (gandrea)
Period: Nov 1, 2021 - Jan 31, 2022
You are receiving this report because you sponsor researchers on the
Research Computing systems. The report below shows the researchers
that you sponsor as well as their cluster usage. Only researchers
that ran at least one job during the reporting period appear in the
tables below. There are no financial costs for using the systems.
Della
----------------------------------------------------------------------------
NetID Name CPU-hours GPU-hours Jobs Account Partition(s)
-----------------------------------------------------------------------------
edevonte Egino Devonte 125017 (59%) 0 3465 phys cpu
mlakshmi Moacir Lakshmi 82638 (39%) 0 63 lit cpu,datasci
rgozde Robert Gözde 4238 (2%) 1018 255 lit cpu,gpu
Your group used 211893 CPU-hours or 1.7% of the 12321247 total CPU-hours
on Della. Your group is ranked 20 of 169 by CPU-hours used. Similarly,
your group used 1018 GPU-hours or 1.2% of the 88329 total GPU-hours
yielding a ranking of 18 of 169 by GPU-hours used.
Tiger
-----------------------------------------------------------------------------
NetID Name CPU-hours GPU-hours Jobs Account Partition(s)
-----------------------------------------------------------------------------
jiryna Jaxson Iryna 1065273 (92%) 0 152 math serial,cpu
sime Shahnaz Ime 98071 (8%) 3250 11092 pol gpu
Your group used 1163344 CPU-hours or 3.0% of the 35509100 total CPU-hours
on Tiger. Your group is ranked 7 of 101 by CPU-hours used. Similarly,
your group used 3250 GPU-hours or 0.6% of the 554101 total GPU-hours
yielding a ranking of 45 of 101 by GPU-hours used.
Detailed Breakdown
--------------------------------------------------------------------------------------
Cluster NetID Partition CPU-hours CPU-rank CPU-eff GPU-hours GPU-rank GPU-eff Jobs
--------------------------------------------------------------------------------------
Della edevonte cpu 125017 12/231 88% N/A N/A N/A 3465
Della mlakshmi cpu 80638 121/231 68% N/A N/A N/A 11
Della mlakshmi ds 2000 2/16 71% N/A N/A N/A 22
Della rgozde cpu 3238 6/79 95% N/A N/A N/A 41
Della rgozde gpu 1000 16/49 91% 250 7/17 52% 101
Tiger jiryna serial 1065273 17/22 91% N/A N/A N/A 252
Tiger sime gpu 98071 26/41 9% 3250 29/41 17% 192
Definitions: A 2-hour job (wall-clock time) that allocates 4 CPU-cores
consumes 8 CPU-hours. Similarly, a 2-hour job that allocates 4 GPUs
consumes 8 GPU-hours. If a group is ranked 5 of 20 then it used the
fifth most CPU-hours or GPU-hours of the 20 groups.
Replying to this email will open a ticket with CSES. Please reply
with questions/comments or to unsubscribe from these reports.
The names above were created randomly.
Obtain the code:
$ ssh <YourNetID>@della.princeton.edu
$ git clone https://github.com/PrincetonUniversity/monthly_sponsor_reports.git
$ cd monthly_sponsor_reports
$ wget https://raw.githubusercontent.com/jdh4/job_defense_shield/main/efficiency.pyExamine the help menu:
$ module load anaconda3/2023.3
$ python monthly_sponsor_reports.py -h
usage: monthly_sponsor_reports.py [-h] --report-type {sponsors,users} \
--months N \
--basepath PATH \
[--email]
Monthly Sponsor and User Reports
options:
-h, --help show this help message and exit
--report-type {sponsors,users}
Specify the report type
--months N Reporting period covers N months
--basepath PATH Specify the path to this script
--email Flag to send reports via emailRun the unit tests:
$ python -uB -m unittest tests/test_monthly_sponsor_reports.py -vIf all of the tests pass then do a dry run (which takes a few minutes):
$ python monthly_sponsor_reports.py --report-type=sponsors \
--months=3 \
--basepath=$(pwd)It is normal to see warnings like the following during the dry run:
...
W: Sponsor entry of mbreixo (Mari Breixo) found for jiryna on stellar. Corrected to mbreixo.
W: Sponsor entry of USER found for ec2342 on della. Corrected to ecmorale.
W: Primary sponsor for kb4172 taken from CSV file.
W: User mlakshmi has multiple primary sponsors: gandrea,cbus. Using gandrea.
...
The output will be sent to stdout instead of email for the dry run. If the output looks good then run once more with emails enabled:
$ python monthly_sponsor_reports.py --report-type=sponsors \
--months=3 \
--basepath=$(pwd) \
--emailFor user reports, one uses:
$ python monthly_sponsor_reports.py --report-type=users \
--months=1 \
--basepath=$(pwd)And then:
$ python monthly_sponsor_reports.py --report-type=users \
--months=1 \
--basepath=$(pwd) \
--email
- hard code the date range at the top
- comment out the assert statement which checks for 1st or 15th of month
- run it
- then remove brakefile, uncomment assert, comment date range and remove cache files
A 2-hour job (wall-clock time) that allocates 4 CPU-cores consumes 8 CPU-hours. Similarly, a 2-hour job that allocates 4 GPUs consumes 8 GPU-hours. If a group is ranked 5 of 20 then it used the fifth most CPU-hours or GPU-hours of the 20 groups.
CPU-eff is the CPU efficiency. It is computed as the ratio of the sums of the CPU time used by all of the CPU-cores and the CPU time allocated, which is the product of the elapsed time of the job and the number of CPU-cores, over all jobs. A 4 CPU-core job that runs for 100 minutes where each CPU-core uses the CPU for 90 minutes has an efficiency of 90%.
These reports run under cron on tigergpu:
[jdh4@tigergpu ~]$ crontab -l
SHELL=/bin/bash
[email protected]
MTH=/home/jdh4/bin/monthly_sponsor_reports
52 8 1 * * ${MTH}/sponsors.sh
52 8 15 * * ${MTH}/users.shThe scripts are:
$ cat sponsors.sh
#!/bin/bash
PY3=/usr/licensed/anaconda3/2023.3/bin
MTH=/home/jdh4/bin/monthly_sponsor_reports
SECS=$(date +%s)
${PY3}/python -uB ${MTH}/monthly_sponsor_reports.py \
--report-type=sponsors \
--months=3 \
--basepath=${MTH} \
--email > ${MTH}/archive/sponsors.log.${SECS} 2>&1$ cat users.sh
#!/bin/bash
PY3=/usr/licensed/anaconda3/2023.3/bin
MTH=/home/jdh4/bin/monthly_sponsor_reports
SECS=$(date +%s)
${PY3}/python -uB ${MTH}/monthly_sponsor_reports.py \
--report-type=users \
--months=1 \
--basepath=${MTH} \
--email > ${MTH}/archive/users.log.${SECS} 2>&1The code depends on the partitions across the clusters:
$ sacct -S 2022-04-01 -L -a -X -n -o cluster,partition%25 | sort | uniq
della callan
della cpu
della cpu,physics
della cryoem
della datascience
della donia
della gpu
della gpu-ee
della gputest
della malik
della motion
della orfeus
della physics
della physics,cpu
perseus all
stellar all
stellar cimes
stellar gpu
stellar pppl
stellar pu
stellar serial
tiger2 cpu
tiger2 cryoem
tiger2 ext
tiger2 gpu
tiger2 motion
tiger2 serial
traverse all
tukey all
It is a good idea to make sure that the partitions used in the code are up to date.
The commands below illustrates the essence of the software in this repo. To compute the CPU-seconds of all jobs on the cimes partition in April 2022:
$ sacct -a -X -n -S 2022-04-01T00:00:00 -E 2022-04-30T23:59:59 -o cputimeraw --partition cimes | awk '{sum += $1} END {print sum}'
To compute the CPU-seconds used by the user aturing in April 2022:
$ sacct -u aturing -X -n -S 2022-04-01T00:00:00 -E 2022-04-30T23:59:59 -o cputimeraw | awk '{sum += $1} END {print sum}'
To compute CPU-hours (not CPU-seconds) for user msbc on the cluster perseus:
$ sacct -u msbc -X -n -M perseus -S 2020-05-15T00:00:00 -E 2021-05-14T23:59:59 -o cputimeraw | awk '{sum += $1} END {print int(sum/3600)}'
List jobs of user aturing on the cluster tiger2 on the partition cpu sorted by CPU-seconds:
$ sacct -u aturing -X -P -n -M tiger2 -r cpu -S 2023-08-01T00:00:00 -E 2023-08-31T23:59:59 -o jobid,cputimeraw,start,nnodes,ncpus,jobname | sort -k2 -n -t'|' -r | sed 's/|/\t/g'
CPU-hours of GPU jobs on Stellar by users in cbe account (watch out for accounts with a comma in the name like "astro,kunz"):
sacct -o cputimeraw -a -P -X -n -M stellar --starttime=2021-10-15 -E 2022-10-14 --accounts=cbe --partition=gpu | awk '{sum += $1} END {print int(sum/3600)}'
CPU-hours for each account on Stellar:
for act in `sacct -S 2022-04-01 -M stellar -a -X -n -o account | sort | uniq`; do printf "$act "; sacct -o cputimeraw -a -P -X -n --starttime=2022-04-01 -E now --accounts=${act} | awk '{sum += $1} END {print int(sum/3600)}'; done
Compute cpu-hours for certain users:
#/bin/bash
# always examine the accounts since they can get combined like on della with "cpu,physics"
STARTDATE="2017-08-01"
ENDDATE="2020-10-01"
CLUSTER="perseus"
ACCOUNTS="astro,kunz"
USERS=$(sacct -S ${STARTDATE} -E ${ENDDATE} -M ${CLUSTER} -a -X -n -o user --accounts=${ACCOUNTS} | sort | uniq)
for USER in ${USERS}
do
sacct -S ${STARTDATE} -E ${ENDDATE} -M ${CLUSTER} -X -n -o cputimeraw -u ${USER} --accounts=${ACCOUNTS} 1>/dev/null 2>&1
if [ $? -ne 0 ]; then
echo "${USER} NULL"
continue
fi
printf "${USER} "
sacct -S ${STARTDATE} -E ${ENDDATE} -M ${CLUSTER} -X -n -o cputimeraw -u ${USER} --accounts=${ACCOUNTS} | awk '{sum += $1} END {print int(sum/3600)}'
donePercentage of available GPU-hours used on cryoem:
$ DAYS=90; GPUS=136; PARTITION=cryoem; sacct -M della -a -X -P -n -S $(date -d"${DAYS} days ago" +%F) -E now -o elapsedraw,alloctres,state --partition=${PARTITION} | grep gres/gpu=[1-9] | sed -E "s/\|.*gpu=/,/" | awk -v gpus=${GPUS} -v days=${DAYS} -F"," '{sum += $1*$2} END {print "GPU-hours used = "int(100*sum/3600/24/gpus/days)"%"}'
GPU-hours used = 10%
- A sponsor will only receive a report if one of their users ran at least one job in the reporting period.
- If the sponsor is not found for a given user on a given cluster then that record is omitted. These events can be seen in the output and should be addressed.
- The script must be executed on a machine that can talk to ldap1.rc.princeton.edu.
- The script is written to only send emails on the 1st and 15th of the month. It cannot runs twice in a 7 day period (see
.brakefilein "sanity checks and safeguards" in Python script). - Rankings on Stellar are over all groups, i.e., the PU/PPPL and CIMES portions are not separated.