Flink Iterations vs. While loop

Posted by Dan Drewes on
URL: http://deprecated-apache-flink-user-mailing-list-archive.369.s1.nabble.com/Flink-Iterations-vs-While-loop-tp8863.html

Hi,

for my bachelor thesis I'm testing an implementation of L-BFGS algorithm with Flink Iterations against a version without Flink Iterations but a casual while loop instead. Both programs use the same Map and Reduce transformations in each iteration. It was expected, that the performance of the Flink Iterations would scale better with increasing size of the input data set. However, the measured results on an ibm-power-cluster are very similar for both versions, e.g. around 30 minutes for 200 GB data. The cluster has 8 nodes, was configured with 4 slots per node and I used a total parallelism of 32.
In every Iteration of the while loop a new flink job is started and I thought, that also the data would be distributed over the network again in each iteration which should consume a significant and measurable amount of time. Is that thought wrong or what is the computional overhead of the flink iterations that is equalizing this disadvantage?
I include the relevant part of both programs and also attach the generated execution plans.
Thank you for any ideas as I could not find much about this issue in the flink docs.

Best, Dan

Flink Iterations:

DataSet<double[]> data = ...

State state = initialState(m, initweights,0,new double[initweights.length]);
DataSet<State> statedataset = env.fromElements(state);

//start of iteration section

IterativeDataSet<State> loop= statedataset.iterate(niter);;


DataSet<State> statewithnewlossgradient = data.map(difffunction).withBroadcastSet(loop, "state")
              .reduce(accumulate)
              .map(new NormLossGradient(datasize))
              .map(new SetLossGradient()).withBroadcastSet(loop,"state")
              .map(new LBFGS());


DataSet<State> converged = statewithnewlossgradient.filter(
   new FilterFunction<State>() {
      @Override
      public boolean filter(State value) throws Exception {
         if(value.getIflag()[0] == 0){
            return false;
         }
         return true;
      }
   }
);

DataSet<State> finalstate = loop.closeWith(statewithnewlossgradient,converged);

While loop:

DataSet<double[]> data =... 
State state = initialState(m, initweights,0,new double[initweights.length]);

int cnt=0;
do{
   LBFGS lbfgs = new LBFGS();
   statedataset=data.map(difffunction).withBroadcastSet(statedataset, "state")
      .reduce(accumulate)
      .map(new NormLossGradient(datasize))
      .map(new SetLossGradient()).withBroadcastSet(statedataset,"state")
      .map(lbfgs);
   cnt++;
}while (cnt<niter && statedataset.collect().get(0).getIflag()[0] != 0);