Use OpenMP instead of clock time

It was previously not accurate when using threads

src/cnn/train.c

@@ -4,6 +4,7 @@
 #include <pthread.h>
 #include <sys/sysinfo.h>
 #include <time.h>
+#include <omp.h>
 
 #include "../mnist/include/mnist.h"
 #include "include/initialisation.h"
@@ -92,6 +93,13 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
     jpegDataset* dataset; // Structure de données décrivant un dataset d'images jpeg
     int* shuffle_index; // shuffle_index[i] contient le nouvel index de l'élément à l'emplacement i avant mélange
 
+    double start_time, end_time;
+    double elapsed_time;
+
+    double algo_start = omp_get_wtime();
+
+    start_time = omp_get_wtime();
+
     if (dataset_type == 0) { // Type MNIST
         // Chargement des images du set de données MNIST
         int* parameters = read_mnist_images_parameters(images_file);
@@ -113,7 +121,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
 
     // Initialisation du réseau
     if (!recover) {
-        network = create_network_lenet5(1, 0, TANH, GLOROT, input_dim, input_depth);
+        network = create_network_lenet5(0.1, 0, TANH, GLOROT, input_dim, input_depth);
     } else {
         network = read_network(recover);
     }
@@ -179,8 +187,14 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
     train_params->nb_images = BATCHES;
     train_params->index = shuffle_index;
     #endif
+    end_time = omp_get_wtime();
+
+    elapsed_time = end_time - start_time;
+    printf("Initialisation: %0.2lf s\n\n", elapsed_time);
 
     for (int i=0; i < epochs; i++) {
+
+        start_time = omp_get_wtime();
         // La variable accuracy permet d'avoir une ESTIMATION
         // du taux de réussite et de l'entraînement du réseau,
         // mais n'est en aucun cas une valeur réelle dans le cas
@@ -213,12 +227,21 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
                 train_parameters[k]->start = BATCHES*j + (BATCHES/nb_threads)*k;
                 train_parameters[k]->network = copy_network(network);
 
-                pthread_create( &tid[k], NULL, train_thread, (void*) train_parameters[k]);
+                if (train_parameters[k]->start+train_parameters[k]->nb_images >= nb_images_total) {
+                    train_parameters[k]->nb_images = nb_images_total - train_parameters[k]->start -1;
+                }
+                if (train_parameters[k]->nb_images > 0) {
+                    pthread_create( &tid[k], NULL, train_thread, (void*) train_parameters[k]);
+                } else {
+                    tid[k] = 0;
+                }
             }
             for (int k=0; k < nb_threads; k++) {
                 // On attend la terminaison de chaque thread un à un
-                pthread_join( tid[k], NULL );
+                if (tid[k] != 0) {
-                accuracy += train_parameters[k]->accuracy / (float) nb_images_total;
+                    pthread_join( tid[k], NULL );
+                    accuracy += train_parameters[k]->accuracy / (float) nb_images_total;
+                }
             }
             
             // On attend que tous les fils aient fini avant d'appliquer des modifications au réseau principal
@@ -228,7 +251,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
                 free_network(train_parameters[k]->network);
             }
             current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
-            printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.1f%%"RESET" ", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
+            printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.2f%%"RESET" ", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
             fflush(stdout);
             #else
             (void)nb_images_total_remaining; // Juste pour enlever un warning
@@ -248,14 +271,16 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
             update_weights(network, network, train_params->nb_images);
             update_bias(network, network, train_params->nb_images);
             
-            printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.1f%%"RESET" ", i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
+            printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.4f%%"RESET" ", i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
             fflush(stdout);
             #endif
         }
+        end_time = omp_get_wtime();
+        elapsed_time = end_time - start_time;
         #ifdef USE_MULTITHREADING
-        printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.1f%%"RESET" \n", nb_threads, i, epochs, nb_images_total, nb_images_total, accuracy*100);
+        printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.4f%%"RESET"\tTemps: %0.2f s\n", nb_threads, i, epochs, nb_images_total, nb_images_total, accuracy*100, elapsed_time);
         #else
-        printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.1f%%"RESET" \n", i, epochs, nb_images_total, nb_images_total, accuracy*100);
+        printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.4f%%"RESET"\tTemps: %0.2f s\n", i, epochs, nb_images_total, nb_images_total, accuracy*100, elapsed_time);
         #endif
         write_network(out, network);
     }
@@ -266,4 +291,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
     #else
     free(train_params);
     #endif
+    end_time = omp_get_wtime();
+    elapsed_time = end_time - algo_start;
+    printf("\nTemps total: %0.1f s\n", elapsed_time);
 }

test/cnn_convolution.cu

@@ -4,6 +4,7 @@
 #include <assert.h>
 #include <math.h>
 #include <time.h>
+#include <omp.h>
 
 #include "../src/cnn/include/convolution.h"
 #include "../src/cnn/include/struct.h"
@@ -122,22 +123,23 @@ void run_convolution_test(int input_dim, int output_dim, int rows, int columns)
 
 
     // Lancement des calculs
-    clock_t start, end;
+    double start_time, end_time;
     double cpu_time_used, gpu_time_used;
 
-    start = clock();
+    start_time = omp_get_wtime();
     make_convolution_device(kernel, input, output_gpu, output_dim);
-    end = clock();
+    end_time = omp_get_wtime();
 
-    gpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
+
+    gpu_time_used = end_time - start_time;
     printf("(%d, %d, %d, %d) Time used for GPU: %lf seconds\n", rows, columns, input_dim, output_dim, gpu_time_used);
 
 
-    start = clock();
+    start_time = omp_get_wtime();
     make_convolution_cpu(kernel, input, output_cpu, output_dim);
-    end = clock();
+    end_time = omp_get_wtime();
 
-    cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
+    cpu_time_used = end_time - start_time;
     printf("(%d, %d, %d, %d) Time used for CPU: %lf seconds\n", rows, columns, input_dim, output_dim, cpu_time_used);    
 
     // Vérification de l'égalité des matrices

test/cnn_matrix_multiplication.cu

@@ -3,6 +3,7 @@
 #include <stdbool.h>
 #include <math.h>
 #include <time.h>
+#include <omp.h>
 
 #include "../src/cnn/include/matrix_multiplication.h"
 #include "../src/include/colors.h"
@@ -70,7 +71,7 @@ bool check_matrices_equality(float** m1, float** m2, int n, int p, int acceptati
 }
 
 void run_matrices_test(int n, int p, int q) {
-    clock_t start, end;
+    double start_time, end_time;
     double cpu_time_used, gpu_time_used;
 
     float** matrix1 = create_matrix(n, p);
@@ -79,18 +80,18 @@ void run_matrices_test(int n, int p, int q) {
     float** result_cpu = create_empty_matrix(n, q);
 
     printf("(%d,%d)x(%d,%d) Data generation complete.\n", n, p, p, q);
-    start = clock();
+    start_time = omp_get_wtime();
     matrix_multiplication_device(matrix1, matrix2, result_gpu, n, p, q);
-    end = clock();
+    end_time = omp_get_wtime();
 
-    cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
+    cpu_time_used = end_time - start_time;
     printf("(%d,%d)x(%d,%d) Time used for GPU: %lf seconds\n", n, p, p, q, cpu_time_used);
     
-    start = clock();
+    start_time = omp_get_wtime();
     matrix_multiplication_host(matrix1, matrix2, result_cpu, n, p, q);
-    end = clock();
+    end_time = omp_get_wtime();
 
-    gpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
+    gpu_time_used = end_time - start_time;
     printf("(%d,%d)x(%d,%d) Time used for CPU: %lf seconds\n", n, p, p, q, gpu_time_used);
 
     // Vérification de l'égalité des matrices