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Does For Each() beat out Parallel Assign()??

Here's an interesting question for the coders among us.

I have two flavors of a simulation I'm running.  The algorithm is identical other than running one using a For Each() Loop and the other running Parallel Assign.  The For Each loop consistently takes much less time to do the same calculation set as the Parallel Assign, even with all 8 of my cores maxed out.  

 

Anybody have thoughts as to why that might be?  Code for each version of the simulation is below: 

Parallel Assign Version

// JSL Code for Simulation of Harmonized Compendial Dissolution Criteria USP <711>

// Simulation function for USP dissolution - three stage test
// reps =   number of simulated lots for simulation
// mean =   the process mean
// sd =     the process standard deviation
// Q =      value as specified in the mongraph for the product
//
// Summary Matrix Key
// ==================
// 1 "PassS1"
// 2 "PassS2" 
// 3 "PassS3"
// 4 "#<Q s1="">= Q & sum_matrix[i, 7] == 0,
     sum_matrix[i, 2] = 1;
     sum_matrix[i, 12] = 1;
     sum_matrix[i, 14] = 0;
    ,
     sum_matrix[i, 2] = 0;
     If( sum_matrix[i, 7] <= 2 & sum_matrix[i, 8] == 0,
      sum_matrix[i, 14] = 1,
      sum_matrix[i, 14] = 0;
      sum_matrix[i, 12] = 0;
     );
    )
   );
    
   // Stage 3
   If( sum_matrix[i, 14] == 1,
    If( sum_matrix[i, 9] >= Q & sum_matrix[i, 10] <= 2 & sum_matrix[i, 11] == 0,
     sum_matrix[i, 3] = 1;
     sum_matrix[i, 12] = 1;
    ,
     sum_matrix[i, 3] = 0;
     sum_matrix[i, 12] = 0;
    )
   );
  );
 
  // return the value
  Mean( sum_matrix[0, 12] );
 )},
 simMat[a, b] = simDissoTest( repN, 80 + cs[1] + cs[3] * (b - 1), a * rs[3] )
);
stop = Tick Seconds();
time = stopWatch( start, stop, "min" );
Show( time, ntests );</Q>

For Each () Version

// JSL Code for Simulation of Harmonized Compendial Dissolution Criteria USP <711>


// Simulation function for USP dissolution - three stage test
// reps =   number of simulated lots for simulation
// mean =   the process mean
// sd =     the process standard deviation
// Q =      value as specified in the mongraph for the product
//
// Summary Matrix Key
// ==================
// 1 "PassS1"
// 2 "PassS2" 
// 3 "PassS3"
// 4 "#<Q s1="">= Q & sum_matrix[i, 7] == 0,
    sum_matrix[i, 2] = 1;
    sum_matrix[i, 12] = 1;
    sum_matrix[i, 14] = 0;
   ,
    sum_matrix[i, 2] = 0;
    If( sum_matrix[i, 7] <= 2 & sum_matrix[i, 8] == 0,
     sum_matrix[i, 14] = 1,
     sum_matrix[i, 14] = 0;
     sum_matrix[i, 12] = 0;
    );
   )
  );
    
   // Stage 3
  If( sum_matrix[i, 14] == 1,
   If( sum_matrix[i, 9] >= Q & sum_matrix[i, 10] <= 2 & sum_matrix[i, 11] == 0,
    sum_matrix[i, 3] = 1;
    sum_matrix[i, 12] = 1;
   ,
    sum_matrix[i, 3] = 0;
    sum_matrix[i, 12] = 0;
   )
  );
 );
 
  // return the value
 Mean( sum_matrix[0, 12] );
);


//---------------------------------------------------------------------------------
// Parallelized Simulation
//---------------------------------------------------------------------------------
// define the size of the matrix ans the step size
rows = vecSize( 0.1, 12, 0.1 );
rowShape = [0.1, 12, 0.1];
cols = vecSize( -1, 10, 0.5 );
colShape = [-1, 10, 0.5];
reps = 100;
ntests = rows * cols;

//create the Parallel test matrix
simMat = J( rows, cols, Empty() );

// run the simulation (testing scalability)
//New Window( "test", lb = Lineup Box( N Col( 2 ) ) );

/*For Each( {repN, i}, {1, 50, 100, 500},
 start = Tick Seconds();
 For Each( {v, {i, j}}, simMat,
  simMat[i, j] = simDissoTest( repN, 80 + colShape[1] + colShape[3] * (j - 1), i * rowShape[3] )
 );
 stop = Tick Seconds();
 time = stopWatch( start, stop, "min" );
 Show( repN, time, ntests );

 //lb << Append( V List Box( Text Box( "Number of reps: " || Char( repN ) ), New Heat Image( simMat ) ) );
);*/

// run the simulation 
start = Tick Seconds();
For Each( {v, {i, j}}, simMat,
 simMat[i, j] = simDissoTest( reps, 80 + colShape[1] + colShape[3] * (j - 1), i * rowShape[3] )
);
stop = Tick Seconds();
time = stopWatch( start, stop, "min" );
Show( reps, time, ntests );

// make the data table from the matrix
tallMatrix = J( ntests, 3, Empty() );
k = 1; // an indexer
For Each( {v, {i, j}}, simMat,
 tallMatrix[k, 1] = 80 + colShape[1] + colShape[3] * (j - 1);
 tallMatrix[k, 2] = i * rowShape[3];
 tallMatrix[k, 3] = v;
 k++;
);

dt = As Table( tallMatrix );
Column( dt, 1 ) << Set Name( "Mean" );
Column( dt, 2 ) << Set Name( "Std Dev" );
Column( dt, 3 ) << Set Name( "Pass Rate" );

dt << New Script(
 "Graph",
 Graph Builder(
  Size( 742, 817 ),
  Show Control Panel( 0 ),
  Fit to Window,
  Variables( X( :Mean ), Y( :Std Dev ), Color( :Pass Rate ) ),
  Elements( Contour( X, Y, Legend( 6 ), Number of Levels( 5 ), Smoothness( 0.146217717445672 ) ) ),
  SendToReport(
   Dispatch(
    {},
    "Mean",
    ScaleBox,
    {Min( 79.0036098075223 ), Max( 89.9788461538462 ), Inc( 2 ), Minor Ticks( 1 )}
   ),
   Dispatch(
    {},
    "Std Dev",
    ScaleBox,
    {Min( 0.0888646043293861 ), Max( 11.971158234127 ), Inc( 2 ), Minor Ticks( 1 )}
   ),
   Dispatch(
    {},
    "400",
    ScaleBox,
    {Legend Model(
     6,
     Properties(
      0,
      {gradient(
       {Color Theme( "Green Yellow Red"(1) ), Scale Values( [0.8 .] ),
       Reverse Gradient( 1 )}
      )},
      Item ID( "Pass Rate", 1 )
     )
    )}
   )
  )
 )
);

dt << Run Script("Graph");

disclaimer;</Q>