우리 중 코더들에게 흥미로운 질문이 있습니다.
제가 실행 중인 시뮬레이션에는 두 가지 종류가 있습니다. 알고리즘은 For Each() 루프를 사용하여 하나를 실행하고 다른 하나는 병렬 할당을 실행하는 것 외에는 동일합니다. For Each 루프는 8개의 코어가 모두 최대치인 경우에도 Parallel Assign과 동일한 계산 세트를 수행하는 데 일관되게 훨씬 적은 시간이 걸립니다.
왜 그럴 수 있는지 생각하는 사람이 있습니까? 시뮬레이션의 각 버전에 대한 코드는 다음과 같습니다.
병렬 할당 버전
// 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>
각 () 버전에 대해
// 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>