Add convolution benchmark [CUDA]

src/scripts/benchmark_mul.py

@@ -17,8 +17,8 @@ def avg(vals):
         "depth": vals[0]["depth"]
     }
 
-def mul_matrix(n, p, q):
+def mul_matrix(n, p, q, executable="./a.out"):
-    output = subprocess.check_output(["./a.out", str(n), str(p), str(q)])
+    output = subprocess.check_output([executable, str(n), str(p), str(q)])
     result = [float(i.split(":")[-1]) for i in output.decode("utf8").split("\n") if i != ""]
     return {
         "GPUtime": result[0],
@@ -29,14 +29,39 @@ def mul_matrix(n, p, q):
         "depth": p
     }
 
-def generate_data():
+def conv_matrix(n, p, q, r, executable="./a.out"):
+    output = subprocess.check_output([executable, str(n), str(p), str(q), str(r)])
+    result = [float(i.split(":")[-1]) for i in output.decode("utf8").split("\n") if i != ""]
+    return {
+        "GPUtime": result[0],
+        "CPUtime": result[1],
+        "errMax": result[2],
+        "errMoy": result[3],
+        "width": q,
+        "depth": p
+    }
+
+def generate_data_mul():
     values = []
     depth = 40
     for i in range(60):
         values.append(avg([mul_matrix((i+1)*100, depth, (i+1)*100) for j in range(10)]))
         print(f"Added M({(i+1)*100}x{depth}) x M({depth}x{(i+1)*100})")
 
-    with open("result.json", "w") as file:
+    with open("result_mul.json", "w") as file:
+        json.dump(values, file, indent=4)
+
+
+def generate_data_conv():
+    values = []
+    output_dim = 40
+    rows = 40
+    columns = 40
+    for i in range(10):
+        values.append(avg([conv_matrix((i+1)*100, output_dim, rows, columns) for j in range(10)]))
+        print(f"Added ({(i+1)*100}, output_dim, rows, columns)")
+
+    with open("result_conv.json", "w") as file:
         json.dump(values, file, indent=4)
 
 
@@ -58,7 +83,7 @@ def plot_erreur(data):
     plt.plot(x, CPUtime)
     plt.show()
 
-def load_data():
+def load_data(filename="result.json"):
-    with open("result.json", 'r') as f:
+    with open(filename, 'r') as f:
         data = json.load(f)
     return data

src/scripts/compilation.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+
+BUILDDIR="../../build"
+WD=$PWD
+
+cd $BUILDDIR/..
+make all
+cd $WD
+
+echo "Compiling matrix_multiplication_benchmark.cu"
+nvcc -ljpeg \
+    matrix_multiplication_benchmark.cu \
+    "$BUILDDIR/"cnn_cuda_matrix_multiplication.o \
+    "$BUILDDIR/"cuda_utils.o \
+    -o benchmark-matrix-multiplication
+
+echo "Compiling matrix_multiplication_benchmark.cu"
+nvcc -ljpeg \
+    convolution_benchmark.cu \
+    "$BUILDDIR/"cnn_cuda_matrix_multiplication.o \
+    "$BUILDDIR/"cuda_utils.o \
+    -o benchmark-convolution

src/scripts/convolution_benchmark.cu

@@ -0,0 +1,201 @@
+#include <stdlib.h>
+#include <stdio.h>
+#include <stdbool.h>
+#include <assert.h>
+#include <math.h>
+#include <time.h>
+
+#include "../src/cnn/include/convolution.h"
+#include "../src/cnn/include/struct.h"
+#include "../src/include/colors.h"
+#include "../src/include/utils.h"
+
+
+float random_float(float low, float high) {
+    float t = (float)rand() / (float)RAND_MAX;
+    return (1.0f - t) * low + t * high;
+}
+
+
+void fill_matrix_random(float ***matrix, int n, int p, int q, float max_val) {
+    for (int i=0; i < n; i++) {
+        for (int j=0; j < p; j++) {
+            for (int k=0; k < q; k++) {
+                matrix[i][j][k] = random_float(0.0f, max_val);
+            }
+        }
+    }
+}
+
+
+void print_matrix(float** mat, int n, int p) {
+    for (int i=0; i < n; i++) {
+        printf("[\t");
+        for (int j=0; j < p; j++) {
+            printf("%0.1f\t", mat[i][j]);
+        }
+        printf("]\n");
+    }
+}
+
+
+float*** create_matrix(int n, int p, int q, float max_val) {
+    float*** matrix = (float***)malloc(n*sizeof(float**));
+    for (int i=0; i < n; i++) {
+        matrix[i] = (float**)malloc(sizeof(float*)*p);
+        for (int j=0; j < p; j++) {
+            matrix[i][j] = (float*)malloc(sizeof(float)*q);
+        }
+    }
+
+    fill_matrix_random(matrix, n, p, q, max_val);
+    return matrix;
+}
+
+
+float*** create_empty_matrix(int n, int p, int q) {
+    float*** matrix = (float***)malloc(n*sizeof(float**));
+    for (int i=0; i < n; i++) {
+        matrix[i] = (float**)malloc(sizeof(float*)*p);
+        for (int j=0; j < p; j++) {
+            matrix[i][j] = (float*)malloc(sizeof(float)*q);
+            for (int k=0; k < q; k++) {
+                matrix[i][j][k] = 0.;
+            }
+        }
+    }
+    return matrix;
+}
+
+void free_matrix(float*** matrix, int n, int p) {
+    for (int i=0; i < n; i++) {
+        for (int j=0; j < p; j++) {
+            free(matrix[i][j]);
+        }
+        free(matrix[i]);
+    }
+    free(matrix);
+}
+
+float max_float(float a, float b) {
+    return a > b ? a : b;
+}
+
+bool check_matrices_equality(float*** m1, float*** m2, int n, int p, int q, int acceptation) {
+    float err_max = 0.;
+    float err_moy = 0.;
+    float err_percent = 0.;
+    for (int i=0; i < n; i++) {
+        for (int j=0; j < p; j++) {
+            for (int k=0; k < q; k++) {
+                if (fabs(m1[i][j][k] - m2[i][j][k]) > 0.01*acceptation) {
+                    //printf(RED "diff %d %d %d: %f val: %f et %f\n" RESET, i, j, k, fabs(m1[i][j][k] - m2[i][j][k]), m1[i][j][k], m2[i][j][k]);
+                    //return false;
+                }
+                err_percent = 2*fabs(m1[i][j][k] - m2[i][j][k])/fabs(m1[i][j][k] + m2[i][j][k]);
+                err_max = max_float(err_max, err_percent);
+                err_moy += err_percent;
+            }
+        }
+    }
+    printf("err_max:%lf\n", err_max);
+    printf("err_moy:%lf\n", err_moy/(n*p*q));
+    return true;
+}
+
+void run_convolution_test(int input_dim, int output_dim, int rows, int columns) {
+    assert(input_dim >= output_dim);
+    int k_size = input_dim - output_dim +1;
+
+    // Génération des données aléatoires
+    Kernel_cnn* kernel = (Kernel_cnn*)malloc(sizeof(Kernel_cnn));
+    
+    kernel->k_size = k_size;
+    kernel->rows = rows;
+    kernel->columns = columns;
+
+    // bias[kernel->columns][dim_output][dim_output]
+    kernel->bias = create_matrix(kernel->columns, output_dim, output_dim, 15.0f);
+    kernel->d_bias = create_matrix(kernel->columns, output_dim, output_dim, 1.5f);
+
+    // w[rows][columns][k_size][k_size]
+    kernel->w = (float****)malloc(sizeof(float***)*kernel->rows);
+    kernel->d_w = (float****)malloc(sizeof(float***)*kernel->rows);
+    for (int i=0; i < kernel->rows; i++) {
+        kernel->w[i] = create_matrix(kernel->columns, kernel->k_size, kernel->k_size, 15.0f);
+        kernel->d_w[i] = create_matrix(kernel->columns, kernel->k_size, kernel->k_size, 1.5f);
+    }
+
+    float*** input = create_matrix(kernel->rows, input_dim, input_dim, 5.0f);
+    float*** output_cpu = create_empty_matrix(kernel->columns, output_dim, output_dim);
+    float*** output_gpu = create_empty_matrix(kernel->columns, output_dim, output_dim);
+
+    //printf("(%d, %d, %d, %d) Data generation complete\n", rows, columns, input_dim, output_dim);
+
+
+    // Lancement des calculs
+    clock_t start, end;
+    double cpu_time_used, gpu_time_used;
+
+    start = clock();
+    make_convolution_device(kernel, input, output_gpu, output_dim);
+    end = clock();
+
+    gpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
+    printf("GPU: %lf\n", gpu_time_used);
+
+
+    start = clock();
+    make_convolution_cpu(kernel, input, output_cpu, output_dim);
+    end = clock();
+
+    cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
+    printf("CPU: %lf\n", rows, columns, input_dim, output_dim, cpu_time_used);    
+
+    // Vérification de l'égalité des matrices
+    //printf("(%d, %d, %d, %d) Checking equality.\n", rows, columns, input_dim, output_dim);
+    if (!check_matrices_equality(output_gpu, output_cpu, kernel->columns, output_dim, output_dim, kernel->k_size)) {// TODO: change acceptation
+        //exit(1);
+    }
+    //printf(GREEN "OK\n" RESET);
+
+    free_matrix(kernel->bias, kernel->columns, output_dim);
+    free_matrix(kernel->d_bias, kernel->columns, output_dim);
+
+    for (int i=0; i < kernel->rows; i++) {
+        free_matrix(kernel->w[i], kernel->columns, kernel->k_size);
+        free_matrix(kernel->d_w[i], kernel->columns, kernel->k_size);
+    }
+    free(kernel->w);
+    free(kernel->d_w);
+
+    free_matrix(input, kernel->rows, input_dim);
+    free_matrix(output_cpu, kernel->columns, output_dim);
+    free_matrix(output_gpu, kernel->columns, output_dim);
+}
+
+
+int main(int argc, char* argv[]) {
+    if (argc < 5) {
+        return 1;
+    }
+    int n = strtol(argv[1], NULL, 10);
+    int p = strtol(argv[2], NULL, 10);
+    int q = strtol(argv[3], NULL, 10);
+    int r = strtol(argv[4], NULL, 10);
+
+    /*
+    printf("Checking CUDA compatibility.\n");
+    bool cuda_compatible = check_cuda_compatibility();
+    if (!cuda_compatible) {
+        printf(RED "CUDA not compatible, skipping tests.\n" RESET);
+        return 0;
+    }
+    */
+    
+    srand(time(NULL));
+
+    run_convolution_test(n, p, q, r);
+    
+    return 0;
+}

src/scripts/matrix_multiplication_benchmark.cu

@@ -63,14 +63,16 @@ float max_float(float a, float b) {
 bool check_matrices_equality(float** m1, float** m2, int n, int p) {
     float err_max = 0.;
     float err_moy = 0.;
+    float err_percent = 0.;
     for (int i=0; i < n; i++) {
         for (int j=0; j < p; j++) {
             if (fabs(m1[i][j] - m2[i][j]) > 0.8) {
                 //printf("%d %d\n", i, j);
                 //return false;
             }
-            err_max = max_float(err_max, fabs(m1[i][j] - m2[i][j]));
+            err_percent = 2*fabs(m1[i][j] - m2[i][j])/fabs(m1[i][j] + m2[i][j]);
-            err_moy += fabs(m1[i][j] - m2[i][j]);
+            err_max = max_float(err_max, err_percent);
+            err_moy += err_percent;
         }
     }
     printf("err_max:%lf\n", err_max);