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grid [2017/10/03 15:21]
popel [Other] |
grid [2017/10/05 20:41]
popel 1837 CPU cores including GPU-machines |
| ====== ÚFAL Grid Engine (LRC) ====== | ====== ÚFAL Grid Engine (LRC) ====== |
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| LRC (Linguistic Research Cluster) is a name of ÚFAL's computational grid/cluster, which has (as of 2017/09) about 1600 CPU cores (115 servers + 2 submission heads), with a total 10 TiB of RAM. It uses [[https://en.wikipedia.org/wiki/Oracle_Grid_Engine|(Sun/Oracle/Son of) Grid Engine]] software (SGE) for job scheduling etc. You can submit many computing tasks (jobs) at once, they will be placed in a queue and once a machine (slot) with the required capabilities (e.g. RAM, number of cores) is available, your job will be executed (scheduled) on this machine. This way we can maximize the usefulness of the computing resources and divide it among users in a fair way. | LRC (Linguistic Research Cluster) is a name of ÚFAL's computational grid/cluster, which has (as of 2017/09) about 1800 CPU cores (115 servers + 2 submission heads), with a total 10 TiB of RAM. It uses [[https://en.wikipedia.org/wiki/Oracle_Grid_Engine|(Sun/Oracle/Son of) Grid Engine]] software (SGE) for job scheduling etc. You can submit many computing tasks (jobs) at once, they will be placed in a queue and once a machine (slot) with the required capabilities (e.g. RAM, number of cores) is available, your job will be executed (scheduled) on this machine. This way we can maximize the usefulness of the computing resources and divide it among users in a fair way. |
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| If you need GPU processing, see a special page about our [[:gpu|GPU cluster called DLL]] (which is actually a subsystem of LRC with an independent queue ''gpu.q''). | If you need GPU processing, see a special page about our [[:gpu|GPU cluster called DLL]] (which is actually a subsystem of LRC with an independent queue ''gpu.q''). |
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| ===== Other ===== | ===== Other ===== |
| * There is a great course [[http://ufal.mff.cuni.cz/courses/npfl102|Data intensive computing]], see the 2016 handouts if you missed the course. It covers the usage of [[http://spark.apache.org/|Spark]] (MapReduce/Hadoop alternative, but better) and HDFS (Hadoop filesystem). | * There is a **great course [[http://ufal.mff.cuni.cz/courses/npfl102|Data intensive computing]]**, see the 2016 handouts if you missed the course. It covers the usage of [[http://spark.apache.org/|Spark]] (MapReduce/Hadoop alternative, but better) and HDFS (Hadoop filesystem). |
| * This course had used a special DLRC (Demo LRC) cluster (students had to login with ''ssh -p 11422 ufallab.ms.mff.cuni.cz'' and special NPFL102-only LDAP logins) with six virtual machines on one physical. During the years when NPFL102 is not taught (e.g. 2017), the DLRC cluster has just one virtual machine. | * This course had used a special **DLRC (Demo LRC) cluster** (students had to login with ''ssh -p 11422 ufallab.ms.mff.cuni.cz'' and special NPFL102-only LDAP logins) with six virtual machines on one physical. During the years when NPFL102 is not taught (e.g. 2017), the DLRC cluster has just one virtual machine. |
| * You can use environment variables ''$JOB_ID'', ''$JOB_NAME''. | * You can use environment variables ''$JOB_ID'', ''$JOB_NAME''. |
| * One job can submit other jobs (but be careful with recursive:-)). A job submitted to the CPU cluster may submit GPU jobs (to the ''qpu.q'' queue). | * One job can submit other jobs (but be careful with recursive:-)). A job submitted to the CPU cluster may submit GPU jobs (to the ''qpu.q'' queue). |
