Implemented the master-worker pattern with addition on a slavee

src/Makefile

@@ -26,10 +26,10 @@ LDOPT = -lginac $(TAULIB)
 MPIEXEC = mpiexec
 NP = 5
 
-MPISRC = masterworker.cpp  \
+MPISRC = masterworker.cpp mw_addslave.cpp \
          perf.cpp  sequential.cpp  tensormatrix_mpi.cpp      \
          utils.cpp  utils_parall.cpp profiling.cpp
-#mw_combined.cpp mw_addslave.cpp hierarchical.cpp  
+#mw_combined.cpp  hierarchical.cpp  
 MPIOBJ= $(MPISRC:.cpp=.o)
 
 

src/masterworker.cpp

@@ -54,7 +54,6 @@ gi::ex multiply_1level_master( tensor3D_t& T, unsigned int size, MPI_Comm comm =
             }
 	    }
 	}
-
     
     /* Compute the set of symbols */
     /* Could be done while the first slave is working */

src/mw_addslave.cpp

@@ -15,9 +15,9 @@ namespace gi = GiNaC;
  *         Parallel 1-level decomposition with addition on a slave             *
  *******************************************************************************/
 
-gi::ex multiply_1level_master_addslave( tensor3D_t& T, matrix_int_t& J, unsigned int size, MPI_Comm comm = MPI_COMM_WORLD ) { 
+gi::ex multiply_1level_master_addslave( tensor3D_t& T, unsigned int size, MPI_Comm comm = MPI_COMM_WORLD ) { 
     gi::ex Tens = 0;
-    unsigned int a1, a2, a3, b1;
+    unsigned int a1, a2, a4;
     gi::ex A;
     gi::lst symbols;
 
@@ -37,24 +37,22 @@ gi::ex multiply_1level_master_addslave( tensor3D_t& T, matrix_int_t& J, unsigned
     j = 0;
 
     int receivedresults = 0;
+    unsigned int N = size/2;
 
     std::vector<parameters_t> input;
     std::vector<std::string> results; /* length and char* */
 
     /* Build a list of argument sets */
     
-    for( a1 = 0 ; a1 < size; a1++ ){
+    for( a4 = 0 ; a4 < N ; a4++ ){
         i=i+1; 
-		for( a2 = 0; a2 < size ; a2++ ){
-			j=j+1; 
-			for( a3 = 0 ; a3 < size ; a3++ ){
-				A = T[a1][a2][a3];
-				for( b1 = 0 ; b1 < size ; b1++ ){
-                    parameters_t p( A, a1, a2, a3, b1 );
-                    input.push_back( p );
-                }
-			}
-		}
+        for( a2 = 0; a2 < N ; a2++ ){
+            j=j+1; 
+            for( a1 = 0 ; a1 < N ; a1++ ){
+                parameters_t p( a4, a2, a1 );
+                input.push_back( p );
+            }
+	    }
 	}
 
     /* Compute the set of symbols */
@@ -137,9 +135,9 @@ gi::ex multiply_1level_master_addslave( tensor3D_t& T, matrix_int_t& J, unsigned
     return Tens;
 }
 
-void multiply_1level_slave_addslave( tensor3D_t& T, matrix_int_t& J, unsigned int size, MPI_Comm comm = MPI_COMM_WORLD ) {
+void multiply_1level_slave_addslave( tensor3D_t& T, unsigned int size, MPI_Comm comm = MPI_COMM_WORLD ) {
     gi::ex Tens;
-    int  a1, a2, a3, b1;
+    int  a1, a2, a4;
     //    gi::ex A;
     unsigned int len = 0;
     
@@ -164,13 +162,11 @@ void multiply_1level_slave_addslave( tensor3D_t& T, matrix_int_t& J, unsigned in
         MPI_Recv( &params, 1, DT_PARAMETERS, ROOT, MPI_ANY_TAG, comm, &status );
         
         if( status.MPI_TAG == TAG_WORK ){
-            a1 = params.a1;
+            a4 = params.a4;
             a2 = params.a2;
-            a3 = params.a3;
-            b1 = params.b1;
-            gi::symbol A( std::string( params.A  ) );
+            a1 = params.a1;
 
-            Tens = one_level1_product( &T, &J, A, size, a1, a2, a3, b1 );
+            Tens = one_level1_product( &T, size, a4, a2, a1 );
             send_result( Tens );
 
         } else {
@@ -178,7 +174,7 @@ void multiply_1level_slave_addslave( tensor3D_t& T, matrix_int_t& J, unsigned in
                 /* Receive a set of expressions to add */
 
                 /* Number of expressions received */
-                int nb = params.a1;
+                int nb = params.a4;
                 
                 /* Length of each string */
 
@@ -221,7 +217,7 @@ void multiply_1level_slave_addslave( tensor3D_t& T, matrix_int_t& J, unsigned in
    W -> M: send an unsigned int (size of the expression), then the expression (table of chars)
 */
         
-gi::ex multiply_1level_mw_addslave( tensor3D_t& T, matrix_int_t& J, int size ) {  // simpler: same dimension everywhere
+gi::ex multiply_1level_mw_addslave( tensor3D_t& T, int size ) {  // simpler: same dimension everywhere
     int rank;
     gi::ex Tens = 0;
     MPI_Comm_rank( MPI_COMM_WORLD, &rank );
@@ -233,9 +229,9 @@ gi::ex multiply_1level_mw_addslave( tensor3D_t& T, matrix_int_t& J, int size ) {
     /* Here we go */
 
     if( 0 == rank ) {
-        Tens = multiply_1level_master_addslave( T, J, size );
+        Tens = multiply_1level_master_addslave( T, size );
     } else {
-        multiply_1level_slave_addslave( T, J, size );
+        multiply_1level_slave_addslave( T, size );
     }
 
     /* Finalize */

src/tensormatrix.h

@@ -24,7 +24,7 @@ gi::ex multiply_1level( tensor3D_t&, matrix_int_t&, int );
 gi::ex multiply_2levels( tensor3D_t&, matrix_int_t&, int );
 // parallel
 gi::ex multiply_1level_mw( tensor3D_t&, int );
-gi::ex multiply_1level_mw_addslave( tensor3D_t&, matrix_int_t&, int );
+gi::ex multiply_1level_mw_addslave( tensor3D_t&, int );
 gi::ex multiply_1level_mw_hierarch( tensor3D_t&, matrix_int_t&, int );
 gi::ex multiply_combined( tensor3D_t&, matrix_int_t&, int );
 

src/tensormatrix_mpi.cpp

@@ -134,10 +134,10 @@ int main( int argc, char** argv ){
     case 'm':
         Tpara = multiply_1level_mw( T, N );
         break;
-        /*    case 'a':
-        Tpara = multiply_1level_mw_addslave( T, J, N );
+    case 'a':
+        Tpara = multiply_1level_mw_addslave( T, N );
         break;
-    case 'h':
+        /*case 'h':
         Tpara = multiply_1level_mw_hierarch( T, J, N );
         break;
     case 'c':

src/utils_parall.cpp

@@ -98,7 +98,7 @@ void send_expressions_to_add( std::vector<std::string>& results, int peer ) {
     /* Fill a bogus parameter object */
     int nb = results.size();
     int i;
-    parameters_t p( 0, 0, 0 );
+    parameters_t p( nb, 0, 0 );
     char* expr;
 
     MPI_Send( &p, 1, DT_PARAMETERS, peer, TAG_ADD, MPI_COMM_WORLD );
@@ -115,7 +115,7 @@ void send_expressions_to_add( std::vector<std::string>& results, int peer ) {
         expr = const_cast<char*>( results[i].c_str() );
         MPI_Send( expr, results[i].length(), MPI_CHAR, peer, TAG_ADD, MPI_COMM_WORLD );
     }
-
+    
     results.erase( results.begin(), results.end() );
    
     free( lengths );