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For Each() batte Parallel Assign()??

Ecco una domanda interessante per i programmatori tra noi.

Ho due tipi di simulazione che sto eseguendo. L'algoritmo è identico, a parte l'esecuzione di uno utilizzando un ciclo For Each() e l'altro che esegue Parallel Assign. Il ciclo For Each richiede costantemente molto meno tempo per eseguire lo stesso set di calcoli di Parallel Assign, anche con tutti gli 8 core al massimo.  

 

Qualcuno ha qualche idea sul perché potrebbe essere? Il codice per ciascuna versione della simulazione è riportato di seguito:

Versione con assegnazione parallela

// 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>

Per ogni () versione

// 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>