update

P2/distributedmatrix.cpp

@@ -216,20 +216,9 @@ DistributedMatrix multiply(const Matrix& left, const DistributedMatrix& right) {
 Matrix DistributedMatrix::multiplyTransposed(const DistributedMatrix &other) const {
 
     Matrix result = (*this).localData * other.getLocalData().transpose();
-    int localSize = result.numRows() * result.numCols();
-
-    std::vector<int> counts(numProcesses);
-    std::vector<int> displacements(numProcesses);
-
-    MPI_Allgather(&localSize, 1, MPI_INT, counts.data(), 1, MPI_INT, MPI_COMM_WORLD);
-
-    displacements[0] = 0;
-    for (int i = 1; i < numProcesses; ++i)
-        displacements[i] = displacements[i - 1] + counts[i - 1];
 
     std::vector<double> buffer(this->globalRows * other.globalRows);
 
-
     MPI_Allreduce(
         result.getData().data(),
         buffer.data(),
@@ -250,6 +239,13 @@ Matrix DistributedMatrix::multiplyTransposed(const DistributedMatrix &other) con
     return fullMatrix;
 }
 
+void sync_matrix(Matrix *matrix, int rank, int src) {
+    // Diffuse la matrice de manière synchrone du processus `src` vers tous les autres processus.
+    MPI_Bcast(matrix->getData().data(), matrix->numRows() * matrix->numCols(), MPI_DOUBLE, src, MPI_COMM_WORLD);
+}
+
+
+
 
 
 

P2/distributedtests.cpp

@@ -430,6 +430,63 @@ void testMultiplyTransposed() {
     }
 }
 
+void test_distributed_mlp_training()
+{
+    int rank, size;
+    MPI_Comm_rank(MPI_COMM_WORLD, &rank);
+    MPI_Comm_size(MPI_COMM_WORLD, &size);
+    
+    // Print info about the MPI environment
+    if (rank == 0) {
+        std::cout << "Running with " << size << " MPI processes." << std::endl;
+    }
+
+    // Create a simple XOR dataset
+    // Create data
+    Matrix X(3, 4);
+    Matrix Y(1, 4);
+
+    // 0
+    X.set(0, 0, 0.0);
+    X.set(1, 0, 0.0);
+    X.set(2, 0, 1.0);
+    Y.set(0, 0, 0.0);
+    
+    // 1
+    X.set(0, 1, 0.0);
+    X.set(1, 1, 1.0);
+    X.set(2, 1, 1.0);
+    Y.set(0, 1, 1.0);
+    
+    // 2
+    X.set(0, 2, 1.0);
+    X.set(1, 2, 0.0);
+    X.set(2, 2, 1.0);
+    Y.set(0, 2, 1.0);
+    
+    // 3
+    X.set(0, 3, 1.0);
+    X.set(1, 3, 1.0);
+    X.set(2, 3, 1.0);
+    Y.set(0, 3, 0.0);
+    
+    // Distribute the data
+    Dataset data = Dataset(DistributedMatrix(X, size), DistributedMatrix(Y, size));
+    
+    // Create and train the model
+    MLP model(3, 128, 1, 0.1);
+    
+    if (rank == 0) {
+        std::cout << "Training distributed MLP for XOR problem..." << std::endl;
+    }
+    
+    model.train(data, 5000);
+    
+    if (rank==0) {
+        std::cout << "Distributed MLP training test completed." << std::endl;
+    }
+}
+
 int main(int argc, char** argv) {
     // Initialize MPI
     int initialized;
@@ -457,7 +514,7 @@ int main(int argc, char** argv) {
         testGather();
         testGetAndSet();
         testCopyConstructor();
-        //test_distributed_mlp_training();
+        test_distributed_mlp_training();
         
         if (rank == 0) {
             std::cout << "All tests passed successfully!" << std::endl;