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How to set up the parameter of a spark job according to the available nodes?
How to get the best LINPACK result and conquer the Top500?Management of available file descriptors within a Hadoop clusterHow can set up a multi nodes job for a spark application through a batch file?
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How I can set the --num-executors, --executor-cores and --executor-memory through the slurm job in the following scenario:
Suppose I have 2 nodes with 32 cores and 128g each. The best configuration (I think) would be:
- Let’s assign 4 cores
per executors=>--executor-cores = 4. - Leave
1 core per nodefor Hadoop/Yarn daemons => Num cores available per node = 32-1 = 31. So, Total available of cores in cluster = 31 x 2 = 62 - Number of available executors = (total cores/num-cores-per-executor) = 62/4 = 15
- Leaving 1 executor for ApplicationManager =>
--num-executors = 14 - Number of executors per node = 15/2 = 7
- Memory per executor = 127GB/7 = 18GB
- Counting off heap overhead = 7% of 18GB = 1.26GB~2g. So, actual
--executor-memory = 18 - 2 = 16GB
So using this (optimal) configuration how can I set up these parameters in an Slurm job?
hpc multi-threading mpio slurm
add a comment |
How I can set the --num-executors, --executor-cores and --executor-memory through the slurm job in the following scenario:
Suppose I have 2 nodes with 32 cores and 128g each. The best configuration (I think) would be:
- Let’s assign 4 cores
per executors=>--executor-cores = 4. - Leave
1 core per nodefor Hadoop/Yarn daemons => Num cores available per node = 32-1 = 31. So, Total available of cores in cluster = 31 x 2 = 62 - Number of available executors = (total cores/num-cores-per-executor) = 62/4 = 15
- Leaving 1 executor for ApplicationManager =>
--num-executors = 14 - Number of executors per node = 15/2 = 7
- Memory per executor = 127GB/7 = 18GB
- Counting off heap overhead = 7% of 18GB = 1.26GB~2g. So, actual
--executor-memory = 18 - 2 = 16GB
So using this (optimal) configuration how can I set up these parameters in an Slurm job?
hpc multi-threading mpio slurm
add a comment |
How I can set the --num-executors, --executor-cores and --executor-memory through the slurm job in the following scenario:
Suppose I have 2 nodes with 32 cores and 128g each. The best configuration (I think) would be:
- Let’s assign 4 cores
per executors=>--executor-cores = 4. - Leave
1 core per nodefor Hadoop/Yarn daemons => Num cores available per node = 32-1 = 31. So, Total available of cores in cluster = 31 x 2 = 62 - Number of available executors = (total cores/num-cores-per-executor) = 62/4 = 15
- Leaving 1 executor for ApplicationManager =>
--num-executors = 14 - Number of executors per node = 15/2 = 7
- Memory per executor = 127GB/7 = 18GB
- Counting off heap overhead = 7% of 18GB = 1.26GB~2g. So, actual
--executor-memory = 18 - 2 = 16GB
So using this (optimal) configuration how can I set up these parameters in an Slurm job?
hpc multi-threading mpio slurm
How I can set the --num-executors, --executor-cores and --executor-memory through the slurm job in the following scenario:
Suppose I have 2 nodes with 32 cores and 128g each. The best configuration (I think) would be:
- Let’s assign 4 cores
per executors=>--executor-cores = 4. - Leave
1 core per nodefor Hadoop/Yarn daemons => Num cores available per node = 32-1 = 31. So, Total available of cores in cluster = 31 x 2 = 62 - Number of available executors = (total cores/num-cores-per-executor) = 62/4 = 15
- Leaving 1 executor for ApplicationManager =>
--num-executors = 14 - Number of executors per node = 15/2 = 7
- Memory per executor = 127GB/7 = 18GB
- Counting off heap overhead = 7% of 18GB = 1.26GB~2g. So, actual
--executor-memory = 18 - 2 = 16GB
So using this (optimal) configuration how can I set up these parameters in an Slurm job?
hpc multi-threading mpio slurm
hpc multi-threading mpio slurm
edited May 1 at 23:27
Phillip -Zyan K Lee- Stockmann
804310
804310
asked May 1 at 12:50
moudimoudi
12
12
add a comment |
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