cluster:103
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| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| cluster:103 [2011/12/21 19:47] – [Best Practices] hmeij | cluster:103 [2011/12/22 19:34] (current) – [Submit 2] hmeij | ||
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| On the back end compute nodes, unless specified, the job runs inside your home directory. | On the back end compute nodes, unless specified, the job runs inside your home directory. | ||
| + | |||
| + | In the SAS program we add the following lines | ||
| + | |||
| + | < | ||
| + | %let jobpid = %sysget(LSB_JOBID); | ||
| + | libname here "/ | ||
| + | </ | ||
| + | |||
| + | And change this line to use local disks for storage | ||
| + | |||
| + | < | ||
| + | data here.one; | ||
| + | </ | ||
| + | |||
| + | In the submission script we change the following | ||
| * new submission file with edits | * new submission file with edits | ||
| Line 138: | Line 153: | ||
| * -R reserves memory, for example, reserve 200 MB of memory on target compute node | * -R reserves memory, for example, reserve 200 MB of memory on target compute node | ||
| * scheduler creates unique dirs in scratch by JOBPID for you, so we'll stage the job there | * scheduler creates unique dirs in scratch by JOBPID for you, so we'll stage the job there | ||
| - | * but now we must copy relevant to scratch dir and results back to home dir | + | * but now we must copy relevant |
| < | < | ||
| Line 180: | Line 196: | ||
| * Reserve resources if you know what you need, especially memory | * Reserve resources if you know what you need, especially memory | ||
| * Use /sanscratch for large data jobs with heavy read/write operations | * Use /sanscratch for large data jobs with heavy read/write operations | ||
| - | * Queue ehwfd is preferentially for Gaussian users and stay off the stata and matlab queues | + | * Queue ehwfd is preferentially for Gaussian users and stay off the stata and matlab queues |
| * Write smart SAS code, for example, use data set indexes and PROC SQL (this can be your best friend) | * Write smart SAS code, for example, use data set indexes and PROC SQL (this can be your best friend) | ||
| + | * ... suggestions will be added to this page | ||
| \\ | \\ | ||
| **[[cluster: | **[[cluster: | ||
cluster/103.1324496864.txt.gz · Last modified: by hmeij
