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linuxcloudnetlogohpc

Netlogo HPC CPU Percentage Use Increase

I submit jobs using headless NetLogo to a HPC server by the following code:

#!/bin/bash
#$ -N r20p
#$ -q all.q
#$ -pe mpi 24
/home/abhishekb/netlogo/netlogo-5.1.0/netlogo-headless.sh \
    --model /home/abhishekb/models/corrected-rk4-20presults.nlogo \
    --experiment test \
    --table /home/abhishekb/csvresults/corrected-rk4-20presults.csv

Below is the snapshot of a cluster queue using: 

qstat -g c

enter image description here

I wish to know can I increase the CQLOAD for my simulations and what does it signify too. I couldn't find an elucidate explanation online.

CPU USAGE CHECK:

qhost -u abhishekb

enter image description here

When I run the behaviour space on my PC through gui assigning high priority to the task makes it use nearly 99% of the CPU which makes it run faster. It uses a greater percentage of CPU processor. I wish to accomplish the same here.

EDIT: enter image description here

EDIT 2;

enter image description here

Solution

  • A typical HPC environment, is designed to run only one MPI process (or OpenMP thread) per CPU core, which has therefore access to 100% of CPU time, and this cannot be increased further. In contrast, on a classical desktop/server machine, a number of processes compete for CPU time, and it is indeed possible to increase performance of one of them by setting the appropriate priorities with nice.

    It appears that CQLOAD, is the mean load average for that computing queue. If you are not using all the CPU cores in it, it is not a useful indicator. Besides, even the load average per core for your runs just translates the efficiency of the code on this HPC cluster. For instance, a value of 0.7 per core, would mean that the code spends 70% of time doing calculations, while the remaining 30% are probably spent waiting to communicate with the other computing nodes (which is also necessary).

    Bottom line, the only way you can increase the CPU percentage use on an HPC cluster is by optimising your code. Normally though, people are more concerned about the parallel scaling (i.e. how the time to solution decreases with the number of CPU cores) than with the CPU percentage use.