Does h2o in a single node cluster do parallel processing or is it only in multi node cluster that parallel processing kicks in?

Question

We are running h2o as a single node cluster inside in AWS:

R is connected to the H2O cluster: 
    H2O cluster uptime:         5 seconds 217 milliseconds 
    H2O cluster timezone:       Etc/UTC 
    H2O data parsing timezone:  UTC 
    H2O cluster version:        3.17.0.4153 
    H2O cluster version age:    10 months and 4 days !!! 
    H2O cluster name:           h2o-8ba55ebb-7d49-41bd-b4e2-d7be45b5f53e 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   22.20 GB 
    H2O cluster total cores:    8 
    H2O cluster allowed cores:  8 
    H2O cluster healthy:        TRUE 
    H2O Connection ip:          localhost 
    H2O Connection port:        54321 
    H2O Connection proxy:       NA 
    H2O Internal Security:      FALSE 
    H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4 
    R Version:                  R version 3.4.3 (2017-11-30) 

And starting h2o from java with nthreads -1:

java -ea -Xmx25g -jar /path/to/h2o.jar -name unique-cloud-name 
     -ip localhost -ice_root /tmp/h2o-tmp -nthreads -1

We're wondering if with a single node cluster that h2o is doing parallel processing / using all available and allowed cores. When we do top -H in the commandline we do see coincidentally 8 active java processes and wondering if those are from h2o and are helping generate our model.

Answer 1

Yes, H2O will use all the cores on a single node to train one model.

nthreads lets you explicitly set the thread pool size that controls the amount of parallelism per process.