From 02e06000e2d87cc7ae1551928673411c56d31928 Mon Sep 17 00:00:00 2001
From: nhatdongdang <144138246+nhatdongdang@users.noreply.github.com>
Date: Fri, 5 Jul 2024 03:54:40 +0000
Subject: [PATCH 1/4] Update

---
 benchmark/matrix_add/Makefile           |  19 +++
 benchmark/matrix_add/benchmark.cu       |  13 ++
 benchmark/matrix_add/benchmark_plot.png | Bin 0 -> 40488 bytes
 benchmark/matrix_add/plot.py            |  50 +++++++
 benchmark/matrix_add/template.cuh       |  10 ++
 benchmark/matrix_add/versions/1.cu      |  44 +++++++
 benchmark/matrix_add/versions/cpu.cu    |  37 ++++++
 src/main.cu                             | 168 ++++++++++++------------
 src/matrix.cu                           |  39 ++++--
 src/matrix.cuh                          |  12 +-
 10 files changed, 288 insertions(+), 104 deletions(-)
 create mode 100644 benchmark/matrix_add/Makefile
 create mode 100644 benchmark/matrix_add/benchmark.cu
 create mode 100644 benchmark/matrix_add/benchmark_plot.png
 create mode 100644 benchmark/matrix_add/plot.py
 create mode 100644 benchmark/matrix_add/template.cuh
 create mode 100644 benchmark/matrix_add/versions/1.cu
 create mode 100644 benchmark/matrix_add/versions/cpu.cu

diff --git a/benchmark/matrix_add/Makefile b/benchmark/matrix_add/Makefile
new file mode 100644
index 0000000..c754193
--- /dev/null
+++ b/benchmark/matrix_add/Makefile
@@ -0,0 +1,19 @@
+compile = nvcc -O3 -arch=sm_75 --use_fast_math 
+SRC_DIR := versions
+BIN_DIR := bin
+SRC_FILES := $(wildcard $(SRC_DIR)/*.cu)
+EXECUTABLES := $(patsubst $(SRC_DIR)/%.cu, $(BIN_DIR)/%, $(SRC_FILES))
+
+all: clean $(EXECUTABLES) 
+
+clean:
+	rm -f -r bin
+	mkdir bin
+
+$(BIN_DIR)/%: $(SRC_DIR)/%.cu
+	$(compile) $< benchmark.cu -o $@.exe 
+
+plot: all
+	python3 ./plot.py
+
+
diff --git a/benchmark/matrix_add/benchmark.cu b/benchmark/matrix_add/benchmark.cu
new file mode 100644
index 0000000..cc2ce0d
--- /dev/null
+++ b/benchmark/matrix_add/benchmark.cu
@@ -0,0 +1,13 @@
+#include "template.cuh"
+#include <stdio.h>
+#include <time.h>
+
+int main(int argc, char* argv[]) {
+    long n;
+    if (argc > 1) {
+        n = atol(argv[1]);
+    } else {
+        n = 100000;
+    }
+    printf("%f", time(n));
+}
\ No newline at end of file
diff --git a/benchmark/matrix_add/benchmark_plot.png b/benchmark/matrix_add/benchmark_plot.png
new file mode 100644
index 0000000000000000000000000000000000000000..72e786c34257599c2c3cfbdb60e861681085c569
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literal 0
HcmV?d00001

diff --git a/benchmark/matrix_add/plot.py b/benchmark/matrix_add/plot.py
new file mode 100644
index 0000000..703c9a8
--- /dev/null
+++ b/benchmark/matrix_add/plot.py
@@ -0,0 +1,50 @@
+import os
+import subprocess
+import matplotlib.pyplot as plt
+
+result = subprocess.run(['make'], capture_output=True, text=True)
+# Define the folder containing the executables
+folder_path = './bin'  # Change this to your bin folder path
+
+# Define the input sizes to test
+start=10000
+end=10000
+step=100000
+
+input_sizes = list(range(start, end+1, step))
+# Initialize a dictionary to store runtimes for each executable
+runtimes = {exe: [] for exe in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, exe))}
+
+# Loop through each executable
+for exe in runtimes.keys():
+    exe_path = os.path.join(folder_path, exe)
+    
+    # Loop through each input size
+    for n in range(start,end+1,step):
+        # Run the executable with the input size and capture its output
+        result = subprocess.run([exe_path, str(n)], capture_output=True, text=True)
+        
+        # Parse the output to get the runtime
+        runtime = float(result.stdout.strip())
+        print(exe,runtime)
+        
+        # Append the runtime to the corresponding executable list
+        runtimes[exe].append(runtime)
+
+# Plot the data
+plt.figure(figsize=(12, 6))
+
+# Loop through each executable and plot the runtimes
+for exe, times in runtimes.items():
+    plt.plot(input_sizes, times, marker='o', label=exe)
+
+plt.xlabel('Iterations')
+plt.ylabel('Runtime (s)')
+plt.title('Benchmark of Function Versions')
+plt.legend()
+plt.grid(True)
+plt.tight_layout()
+
+output_file = 'benchmark_plot.png'  # Specify your desired output file name and format
+plt.savefig(output_file)
+# Show the plot
\ No newline at end of file
diff --git a/benchmark/matrix_add/template.cuh b/benchmark/matrix_add/template.cuh
new file mode 100644
index 0000000..baa94a5
--- /dev/null
+++ b/benchmark/matrix_add/template.cuh
@@ -0,0 +1,10 @@
+#pragma once
+
+typedef struct {
+    int rows;
+    int cols;
+    float* data; // array
+} matrix;
+
+double time(int n);
+matrix* new_matrix_d(int rows, int cols);
\ No newline at end of file
diff --git a/benchmark/matrix_add/versions/1.cu b/benchmark/matrix_add/versions/1.cu
new file mode 100644
index 0000000..cd4b001
--- /dev/null
+++ b/benchmark/matrix_add/versions/1.cu
@@ -0,0 +1,44 @@
+#include "../template.cuh"
+
+matrix* new_matrix(int rows, int cols) {
+    matrix* res = (matrix*)malloc(sizeof(matrix));
+    res->rows = rows;
+    res->cols = cols;
+    res->data = (float*)malloc((rows * cols) * sizeof(float));
+    return res;
+}
+
+matrix* new_matrix_d(int rows, int cols) {
+    matrix* res = (matrix*)malloc(sizeof(matrix));
+    res->rows = rows;
+    res->cols = cols;
+    res->cols = cols;
+    cudaMalloc((void**)&(res->data), rows * cols * sizeof(float));
+    return res;
+}
+
+__global__ void matrix_add(float *a, float*b ,int rows)
+{
+    int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx<rows){
+        a[idx]+=b[idx];
+    }
+}
+
+double time(int n) {
+    int row=100000;
+    matrix* a = new_matrix_d(row, 1);
+    matrix* b = new_matrix_d(row, 1);
+    cudaStream_t stream1;
+    cudaStreamCreate ( &stream1);
+    
+    int thread=1024;
+    int block=((row+thread-1)/thread);
+
+    clock_t start = clock();
+    for(int i=0;i<n;i++){
+        matrix_add<<<1,1,0,stream1>>>(a->data,b->data,row);
+    }
+    double seconds = (double)(clock() - (double)start) / CLOCKS_PER_SEC;
+    return seconds;
+}
\ No newline at end of file
diff --git a/benchmark/matrix_add/versions/cpu.cu b/benchmark/matrix_add/versions/cpu.cu
new file mode 100644
index 0000000..ce36a1c
--- /dev/null
+++ b/benchmark/matrix_add/versions/cpu.cu
@@ -0,0 +1,37 @@
+#include "../template.cuh"
+
+matrix* new_matrix(int rows, int cols) {
+    matrix* res = (matrix*)malloc(sizeof(matrix));
+    res->rows = rows;
+    res->cols = cols;
+    res->data = (float*)malloc((rows * cols) * sizeof(float));
+    return res;
+}
+
+matrix* new_matrix_d(int rows, int cols) {
+    matrix* res = (matrix*)malloc(sizeof(matrix));
+    res->rows = rows;
+    res->cols = cols;
+    res->cols = cols;
+    cudaMalloc((void**)&(res->data), rows * cols * sizeof(float));
+    return res;
+}
+
+void matrix_add(float* a, float* b, int rows) {
+    for (int i = 0; i < rows; i++) {
+        a[i] += b[i];
+    }
+}
+
+double time(int n) {
+    int row=100000;
+    matrix* a = new_matrix(row, 1);
+    matrix* b = new_matrix(row, 1);
+
+    clock_t start = clock();
+    for (int i = 0; i < n; i++) {
+        matrix_add(a->data, b->data,row);
+    }
+    double seconds = (double)(clock() - (double)start) / CLOCKS_PER_SEC;
+    return seconds;
+}
\ No newline at end of file
diff --git a/src/main.cu b/src/main.cu
index 1a12d40..59ada7b 100644
--- a/src/main.cu
+++ b/src/main.cu
@@ -21,11 +21,13 @@ matrix* weights[NUM_LAYERS];
 matrix* biases[NUM_LAYERS];
 
 // device weights and biases;
-matrix* d_weights[7];
-matrix* d_biases[7];
+matrix** d_weights;
+matrix** d_biases;
+
 matrix** d_inputs;
 
 int* results;
+int* d_results;
 
 char letters[52] = {'A', 'a', 'B', 'b', 'C', 'c', 'D', 'd', 'E', 'e', 'F', 'f', 'G', 'g', 'H', 'h', 'I', 'i',
                     'J', 'j', 'K', 'k', 'L', 'l', 'M', 'm', 'N', 'n', 'O', 'o', 'P', 'p', 'Q', 'q', 'R', 'r',
@@ -102,74 +104,72 @@ void read_tensor(matrix* a, const char* fileName) {
     fclose(file);
 }
 
-void propagate_fwd(matrix* weights, matrix* input_layer, matrix* output_layer, matrix* biases) {
-    matrix_mul<<<1, 1>>>(weights->data, input_layer->data, output_layer->data, weights->rows, weights->cols);
-    cudaDeviceSynchronize();
-    matrix_add<<<1, 1>>>(output_layer->data, biases->data, biases->rows);
-    cudaDeviceSynchronize();
+__device__ void propagate_fwd(matrix* weights, matrix* input_layer, matrix* output_layer, matrix* biases) {
+    matrix_mul(weights->data, input_layer->data, output_layer->data, weights->rows, weights->cols);
+    matrix_add(output_layer->data, biases->data, biases->rows);
 }
 
-int infer(matrix* d_input) {
-    matrix* outputs[2];
-    outputs[0] = new_matrix_d(98, 1);
-    outputs[1] = new_matrix_d(65, 1);
-
-    propagate_fwd(d_weights[0], d_input, outputs[0], d_biases[0]);
-    relu<<<1, 1>>>(outputs[0]->data, 98);
-    cudaDeviceSynchronize();
-
-    propagate_fwd(d_weights[1], outputs[0], outputs[1], d_biases[1]);
-    cudaMemsetAsync(outputs[0], 0, 50 * sizeof(float));
-    relu<<<1, 1>>>(outputs[1]->data, 65);
-    cudaDeviceSynchronize();
-
-    propagate_fwd(d_weights[2], outputs[1], outputs[0], d_biases[2]);
-    cudaMemsetAsync(outputs[1], 0, 30 * sizeof(float));
-    relu<<<1, 1>>>(outputs[0]->data, 50);
-    cudaDeviceSynchronize();
-
-    propagate_fwd(d_weights[3], outputs[0], outputs[1], d_biases[3]);
-    cudaMemsetAsync(outputs[0], 0, 25 * sizeof(float));
-    relu<<<1, 1>>>(outputs[1]->data, 30);
-    cudaDeviceSynchronize();
-
-    propagate_fwd(d_weights[4], outputs[1], outputs[0], d_biases[4]);
-    cudaMemsetAsync(outputs[1], 0, 40 * sizeof(float));
-    relu<<<1, 1>>>(outputs[0]->data, 25);
-    cudaDeviceSynchronize();
+// __global__ void infer(int a) {
+__global__ void infer(matrix** d_inputs,int* d_results,matrix** d_weights, matrix** d_biases,int size,int iter) {
+    
 
-    propagate_fwd(d_weights[5], outputs[0], outputs[1], d_biases[5]);
-    cudaMemsetAsync(outputs[0], 0, 52 * sizeof(float));
-    relu<<<1, 1>>>(outputs[1]->data, 40);
-    cudaDeviceSynchronize();
+    int idx = (blockIdx.x * blockDim.x + threadIdx.x);
 
-    propagate_fwd(d_weights[6], outputs[1], outputs[0], d_biases[6]);
-    softmax<<<1, 1>>>(outputs[0]->data, 52);
-    cudaDeviceSynchronize();
+    int total=iter*size;
+    int stride=(blockDim.x*gridDim.x);
 
-    int* d_res;
-    cudaMalloc(&d_res, sizeof(int));
+    matrix* gay_input=d_inputs[0];
 
-    argmax<<<1, 1>>>(outputs[0]->data, 52, d_res);
-    cudaDeviceSynchronize();
-
-    cudaFree(outputs[0]->data);
+    matrix* outputs[2];
+    outputs[0] = new_matrix(98, 1);
+    outputs[1] = new_matrix(65, 1);
+
+    for (int i=idx;i<total;i+=stride){
+        int resultIdx=i%size;
+
+        propagate_fwd(d_weights[0], gay_input, outputs[0], d_biases[0]);
+        relu(outputs[0]->data, 98);
+
+        propagate_fwd(d_weights[1], outputs[0], outputs[1], d_biases[1]);
+        memset(outputs[0]->data, 0, 50 * sizeof(float));
+        relu(outputs[1]->data, 65);
+        
+        propagate_fwd(d_weights[2], outputs[1], outputs[0], d_biases[2]);
+        memset(outputs[1]->data, 0, 30 * sizeof(float));
+        relu(outputs[0]->data, 50);
+
+        propagate_fwd(d_weights[3], outputs[0], outputs[1], d_biases[3]);
+        memset(outputs[0]->data, 0, 25 * sizeof(float));
+        relu(outputs[1]->data, 30);
+
+        propagate_fwd(d_weights[4], outputs[1], outputs[0], d_biases[4]);
+        memset(outputs[1]->data, 0, 40 * sizeof(float));
+        relu(outputs[0]->data, 25);
+
+        propagate_fwd(d_weights[5], outputs[0], outputs[1], d_biases[5]);
+        memset(outputs[0]->data, 0, 52 * sizeof(float));
+        relu(outputs[1]->data, 40);
+
+        propagate_fwd(d_weights[6], outputs[1], outputs[0], d_biases[6]);
+        softmax(outputs[0]->data, 52);
+        
+        int res=argmax(outputs[0]->data, 52);
+        d_results[resultIdx]=res;
+        
+        memset(outputs[0]->data,0,98*sizeof(float));
+        memset(outputs[1]->data,0,65*sizeof(float));
+    }
+    free(outputs[0]->data);
     free(outputs[0]);
-    cudaFree(outputs[1]->data);
+    free(outputs[1]->data);
     free(outputs[1]);
-
-    int h_res;
-    cudaMemcpy(&h_res, d_res, sizeof(int), cudaMemcpyDeviceToHost);
-    return h_res;
 }
 
-void process(int input_size) {
-    for (int i = 1; i <= input_size; i++) {
-        results[i] = infer(d_inputs[i]);
-    }
-}
 
+int iter=1000;
 int main(int argc, char* argv[]) {
+    
+
     if (argc < 3) {
         printf("Not enough arguments.");
         return EXIT_FAILURE;
@@ -189,6 +189,7 @@ int main(int argc, char* argv[]) {
     weights[5] = new_matrix(40, 25);
     weights[6] = new_matrix(52, 40);
 
+
     biases[0] = new_matrix(98, 1);
     biases[1] = new_matrix(65, 1);
     biases[2] = new_matrix(50, 1);
@@ -198,21 +199,15 @@ int main(int argc, char* argv[]) {
     biases[6] = new_matrix(52, 1);
     read_model(argv[1]);
 
-    d_weights[0] = copy_to_device(weights[0]);
-    d_weights[1] = copy_to_device(weights[1]);
-    d_weights[2] = copy_to_device(weights[2]);
-    d_weights[3] = copy_to_device(weights[3]);
-    d_weights[4] = copy_to_device(weights[4]);
-    d_weights[5] = copy_to_device(weights[5]);
-    d_weights[6] = copy_to_device(weights[6]);
-
-    d_biases[0] = copy_to_device(biases[0]);
-    d_biases[1] = copy_to_device(biases[1]);
-    d_biases[2] = copy_to_device(biases[2]);
-    d_biases[3] = copy_to_device(biases[3]);
-    d_biases[4] = copy_to_device(biases[4]);
-    d_biases[5] = copy_to_device(biases[5]);
-    d_biases[6] = copy_to_device(biases[6]);
+    CUDA_CHECK(cudaMalloc(&d_weights,NUM_LAYERS*sizeof(matrix*)));
+    CUDA_CHECK(cudaMalloc(&d_biases,NUM_LAYERS*sizeof(matrix*)));
+    for (int i=0;i<NUM_LAYERS;i++){
+        matrix* a=copy_to_device(weights[i]);
+        matrix* b= copy_to_device(biases[i]);
+        matrix** z=&(d_weights[i]);
+        CUDA_CHECK(cudaMemcpy(z,&a,sizeof(matrix*),cudaMemcpyHostToDevice));
+        CUDA_CHECK(cudaMemcpy(&(d_biases[i]),&b,sizeof(matrix*),cudaMemcpyHostToDevice));
+    }
 
     const char* directory_path = argv[2];
     struct dirent* entry;
@@ -227,14 +222,15 @@ int main(int argc, char* argv[]) {
     while ((entry = readdir(dir)) != NULL) {
         if (entry->d_type == DT_REG) {
             size++;
-        }
+       }
     }
 
-    results = (int*)malloc((size + 1) * sizeof(int));
-    d_inputs = (matrix**)malloc((size + 1) * sizeof(matrix*));
+    results = (int*)malloc((size) * sizeof(int));
+    memset(results,0,sizeof(int)*(size));
+    cudaMalloc(&d_results,(size)*sizeof(int));
+    cudaMalloc(&d_inputs,(size)*sizeof(matrix*));
 
     dir = opendir(directory_path);
-
     while ((entry = readdir(dir)) != NULL) {
         if (entry->d_type == DT_REG) {
             matrix* input = new_matrix(225, 1);
@@ -245,23 +241,27 @@ int main(int argc, char* argv[]) {
             strcat(file_name, "/");
             strcat(file_name, entry->d_name);
             read_tensor(input, file_name);
-            d_inputs[file_num] = copy_to_device(input);
+            matrix *temp=copy_to_device(input);
+            cudaMemcpy(&d_inputs[file_num-1],&temp,sizeof(matrix*),cudaMemcpyHostToDevice);
             free(input);
         }
     }
-
     free(file_name);
     free(file_num_str);
     closedir(dir);
 
-    // Process
-    process(size);
 
-    // Write to csv file
+    int threadsPerBlock = 512;
+    int blocksPerGrid = (size + threadsPerBlock - 1) / threadsPerBlock;
+
+    infer<<<blocksPerGrid,512>>>(d_inputs,d_results,d_weights,d_biases,size,iter);
+    cudaDeviceSynchronize();
+    cudaMemcpy(results,d_results,(size)*(sizeof(int)),cudaMemcpyDeviceToHost);
+    
     FILE* csv_file = fopen("results.csv", "w+");
     fprintf(csv_file, "image_number, guess\n");
-    for (int i = 1; i <= size; i++) {
-        fprintf(csv_file, "%d, %c\n", i, letters[results[i]]);
+    for (int i = 0; i < size; i++) {
+        fprintf(csv_file, "%d, %c\n", i+1, letters[results[i]]);
     }
     fclose(csv_file);
 
diff --git a/src/matrix.cu b/src/matrix.cu
index e8ed37d..205e62b 100644
--- a/src/matrix.cu
+++ b/src/matrix.cu
@@ -6,7 +6,7 @@
 
 #define UNROLL_FACTOR 8
 
-matrix* new_matrix(int rows, int cols) {
+__host__ __device__ matrix* new_matrix(int rows, int cols) {
     matrix* res = (matrix*)malloc(sizeof(matrix));
     res->rows = rows;
     res->cols = cols;
@@ -14,22 +14,33 @@ matrix* new_matrix(int rows, int cols) {
     return res;
 }
 
+__global__ void alloc(matrix* res,float* data,int rows,int cols){
+    res->rows=rows;
+    res->cols=cols;
+    res->data=data;
+}
+
 matrix* new_matrix_d(int rows, int cols) {
-    matrix* res = (matrix*)malloc(sizeof(matrix));
-    res->rows = rows;
-    res->cols = cols;
-    res->cols = cols;
-    cudaMalloc((void**)&(res->data), rows * cols * sizeof(float));
+    matrix* res;
+    CUDA_CHECK(cudaMalloc(&res,sizeof(matrix)));
+    float* data;
+    cudaMalloc(&data,rows*cols*sizeof(float));
+    alloc<<<1,1>>>(res,data,rows,cols);
+    cudaDeviceSynchronize();
     return res;
 }
 
 matrix* copy_to_device(matrix* h_mat) {
-    matrix* res = new_matrix_d(h_mat->rows, h_mat->cols);
-    CUDA_CHECK(cudaMemcpy(res->data, h_mat->data, h_mat->rows * h_mat->cols * sizeof(float), cudaMemcpyHostToDevice));
+    matrix* res ;
+    CUDA_CHECK(cudaMalloc(&res,sizeof(matrix)));
+    float* data;
+    cudaMalloc(&data,h_mat->rows*h_mat->cols*sizeof(float));
+    cudaMemcpy(data, h_mat->data, h_mat->rows * h_mat->cols * sizeof(float), cudaMemcpyHostToDevice);
+    alloc<<<1,1>>>(res,data,h_mat->rows,h_mat->cols);
     return res;
 }
 
-__global__ void matrix_mul(float* weight, float* input, float* result, int w_rows, int w_cols) {
+__device__ void matrix_mul(float* weight, float* input, float* result, int w_rows, int w_cols) {
     for (int i = 0; i < w_rows; i++) {
         float sum = 0;
         for (int j = 0; j < w_cols; j++) {
@@ -39,20 +50,20 @@ __global__ void matrix_mul(float* weight, float* input, float* result, int w_row
     }
 }
 
-__global__ void matrix_add(float* a, float* b, int rows) {
+__device__ void matrix_add(float* a, float* b, int rows) {
     for (int i = 0; i < rows; i++) {
         a[i] += b[i];
     }
 }
 
-__global__ void relu(float* a, int rows) {
+__device__ void relu(float* a, int rows) {
     for (int i = 0; i < rows; i++) {
         if ((a)[i] < (float)0)
             (a)[i] = (float)0;
     }
 }
 
-__global__ void softmax(float* a, int rows) {
+__device__ void softmax(float* a, int rows) {
     float res = (float)0;
     for (int i = 0; i < rows; i++) {
         res += exp(a[i]);
@@ -62,7 +73,7 @@ __global__ void softmax(float* a, int rows) {
     }
 }
 
-__global__ void argmax(float* a, int rows, int* des) {
+__device__ int argmax(float* a, int rows) {
     int res = a[0];
     int idx = 0;
     for (int i = 0; i < rows; i++) {
@@ -71,5 +82,5 @@ __global__ void argmax(float* a, int rows, int* des) {
             idx = i;
         }
     }
-    *des = idx;
+    return idx;
 }
\ No newline at end of file
diff --git a/src/matrix.cuh b/src/matrix.cuh
index 83005cf..235ae78 100644
--- a/src/matrix.cuh
+++ b/src/matrix.cuh
@@ -6,18 +6,18 @@ typedef struct {
     float* data; // array
 } matrix;
 
-matrix* new_matrix(int rows, int cols);
+ __host__ __device__ matrix* new_matrix(int rows, int cols);
 
 matrix* copy_to_device(matrix* h_mat);
 
 matrix* new_matrix_d(int rows, int cols);
 
-__global__ void matrix_mul(float* a, float* b, float* c, int rows, int cols);
+__device__ void matrix_mul(float* a, float* b, float* c, int rows, int cols);
 
-__global__ void matrix_add(float* a, float* b, int rows);
+__device__ void matrix_add(float* a, float* b, int rows);
 
-__global__ void relu(float* a, int rows);
+__device__ void relu(float* a, int rows);
 
-__global__ void softmax(float* a, int rows);
+__device__ void softmax(float* a, int rows);
 
-__global__ void argmax(float* a, int rows, int* res);
\ No newline at end of file
+__device__ int argmax(float* a, int rows);
\ No newline at end of file

From 2ab6689441596465628f2d9cc844ce3a7ed2bef1 Mon Sep 17 00:00:00 2001
From: nhatdongdang <144138246+nhatdongdang@users.noreply.github.com>
Date: Fri, 5 Jul 2024 06:27:36 +0000
Subject: [PATCH 2/4] Change optimal block size

---
 src/main.cu    | 95 ++++++++++++++++++++++++++++++++------------------
 src/matrix.cu  |  9 +++++
 src/matrix.cuh |  4 ++-
 3 files changed, 73 insertions(+), 35 deletions(-)

diff --git a/src/main.cu b/src/main.cu
index 59ada7b..5b4b779 100644
--- a/src/main.cu
+++ b/src/main.cu
@@ -110,54 +110,45 @@ __device__ void propagate_fwd(matrix* weights, matrix* input_layer, matrix* outp
 }
 
 // __global__ void infer(int a) {
-__global__ void infer(matrix** d_inputs,int* d_results,matrix** d_weights, matrix** d_biases,int size,int iter) {
-    
+__global__ void infer(matrix** d_inputs, int* d_results, matrix** d_weights, matrix** d_biases, int it_per_input, int in_num) {
+    int num_threads = blockDim.x * gridDim.x;
+    int thread_idx = (blockIdx.x * blockDim.x + threadIdx.x);
 
-    int idx = (blockIdx.x * blockDim.x + threadIdx.x);
+    if (thread_idx > it_per_input) return;
 
-    int total=iter*size;
-    int stride=(blockDim.x*gridDim.x);
+    // printf("Thread %d at input %d\n", thread_idx, in_num);
 
-    matrix* gay_input=d_inputs[0];
+    matrix* input = d_inputs[in_num];
 
     matrix* outputs[2];
     outputs[0] = new_matrix(98, 1);
     outputs[1] = new_matrix(65, 1);
 
-    for (int i=idx;i<total;i+=stride){
-        int resultIdx=i%size;
-
-        propagate_fwd(d_weights[0], gay_input, outputs[0], d_biases[0]);
+    for (int i = thread_idx; i < it_per_input; i += num_threads) {
+        // printf("Working on inference %d\n", i);
+        propagate_fwd(d_weights[0], input, outputs[0], d_biases[0]);
         relu(outputs[0]->data, 98);
 
         propagate_fwd(d_weights[1], outputs[0], outputs[1], d_biases[1]);
-        memset(outputs[0]->data, 0, 50 * sizeof(float));
         relu(outputs[1]->data, 65);
         
         propagate_fwd(d_weights[2], outputs[1], outputs[0], d_biases[2]);
-        memset(outputs[1]->data, 0, 30 * sizeof(float));
         relu(outputs[0]->data, 50);
 
         propagate_fwd(d_weights[3], outputs[0], outputs[1], d_biases[3]);
-        memset(outputs[0]->data, 0, 25 * sizeof(float));
         relu(outputs[1]->data, 30);
 
         propagate_fwd(d_weights[4], outputs[1], outputs[0], d_biases[4]);
-        memset(outputs[1]->data, 0, 40 * sizeof(float));
         relu(outputs[0]->data, 25);
 
         propagate_fwd(d_weights[5], outputs[0], outputs[1], d_biases[5]);
-        memset(outputs[0]->data, 0, 52 * sizeof(float));
         relu(outputs[1]->data, 40);
 
         propagate_fwd(d_weights[6], outputs[1], outputs[0], d_biases[6]);
         softmax(outputs[0]->data, 52);
         
         int res=argmax(outputs[0]->data, 52);
-        d_results[resultIdx]=res;
-        
-        memset(outputs[0]->data,0,98*sizeof(float));
-        memset(outputs[1]->data,0,65*sizeof(float));
+        d_results[in_num] = res;
     }
     free(outputs[0]->data);
     free(outputs[0]);
@@ -166,7 +157,8 @@ __global__ void infer(matrix** d_inputs,int* d_results,matrix** d_weights, matri
 }
 
 
-int iter=1000;
+#define IT_PER_IN 1000000
+
 int main(int argc, char* argv[]) {
     
 
@@ -178,9 +170,7 @@ int main(int argc, char* argv[]) {
     // Start timing
     struct timeval stop, start;
     gettimeofday(&start, NULL);
-
-    // TODO: find a way to load static weights and biases
-    // Load model (The memory of those code should be initialize during compile time to enchance the speed)
+    
     weights[0] = new_matrix(98, 225);
     weights[1] = new_matrix(65, 98);
     weights[2] = new_matrix(50, 65);
@@ -218,17 +208,17 @@ int main(int argc, char* argv[]) {
     char* file_num_str = (char*)malloc((100) * sizeof(char));
 
     int file_num;
-    int size = 0;
+    int input_count = 0;
     while ((entry = readdir(dir)) != NULL) {
         if (entry->d_type == DT_REG) {
-            size++;
+            input_count++;
        }
     }
 
-    results = (int*)malloc((size) * sizeof(int));
-    memset(results,0,sizeof(int)*(size));
-    cudaMalloc(&d_results,(size)*sizeof(int));
-    cudaMalloc(&d_inputs,(size)*sizeof(matrix*));
+    results = (int*)malloc((input_count) * sizeof(int));
+    memset(results,0,sizeof(int)*(input_count));
+    cudaMalloc(&d_results,(input_count)*sizeof(int));
+    cudaMalloc(&d_inputs,(input_count)*sizeof(matrix*));
 
     dir = opendir(directory_path);
     while ((entry = readdir(dir)) != NULL) {
@@ -250,17 +240,54 @@ int main(int argc, char* argv[]) {
     free(file_num_str);
     closedir(dir);
 
+    int deviceCount;
+    cudaError_t err = cudaGetDeviceCount(&deviceCount);
+    if (err != cudaSuccess) {
+        printf("Error: %s\n", cudaGetErrorString(err));
+        return -1;
+    }
+
+    for (int i = 0; i < deviceCount; ++i) {
+        cudaDeviceProp prop;
+        cudaGetDeviceProperties(&prop, i);
+        printf("Device %d:\n", i);
+        printf("  Device Name: %s\n", prop.name);
+        printf("  Compute Capability: %d.%d\n", prop.major, prop.minor);
+        printf("  Total Global Memory: %lu bytes\n", prop.totalGlobalMem);
+        printf("  Shared Memory per Block: %lu bytes\n", prop.sharedMemPerBlock);
+        printf("  Registers per Block: %d\n", prop.regsPerBlock);
+        printf("  Warp Size: %d\n", prop.warpSize);
+        printf("  Max Threads per Block: %d\n", prop.maxThreadsPerBlock);
+        printf("  Max Threads Dim: (%d, %d, %d)\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
+        printf("  Max Grid Size: (%d, %d, %d)\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
+        printf("  Clock Rate: %d kHz\n", prop.clockRate);
+        printf("  Total Constant Memory: %lu bytes\n", prop.totalConstMem);
+        printf("  Multiprocessor Count: %d\n", prop.multiProcessorCount);
+        printf("  Memory Clock Rate: %d kHz\n", prop.memoryClockRate);
+        printf("  Memory Bus Width: %d bits\n", prop.memoryBusWidth);
+        printf("  L2 Cache Size: %d bytes\n", prop.l2CacheSize);
+        printf("\n");
+    }
 
-    int threadsPerBlock = 512;
-    int blocksPerGrid = (size + threadsPerBlock - 1) / threadsPerBlock;
+    cudaMemset(d_results,0,sizeof(int)*input_count);
 
-    infer<<<blocksPerGrid,512>>>(d_inputs,d_results,d_weights,d_biases,size,iter);
+    int minGridSize, blockSize;
+    cudaOccupancyMaxPotentialBlockSize(&minGridSize, &blockSize, infer, 0, 0);
+    printf("Recommended block size: %d Grid size: %d\n", blockSize, minGridSize);
+
+    for (int i = 0; i < input_count; i++) {
+        infer<<<108,69>>>(d_inputs, d_results, d_weights, d_biases, IT_PER_IN, i);
+        err = cudaGetLastError();
+            if (err != cudaSuccess) {
+            printf("CUDA error: %s\n", cudaGetErrorString(err));
+        }
+    }
     cudaDeviceSynchronize();
-    cudaMemcpy(results,d_results,(size)*(sizeof(int)),cudaMemcpyDeviceToHost);
+    cudaMemcpy(results,d_results,(input_count)*(sizeof(int)),cudaMemcpyDeviceToHost);
     
     FILE* csv_file = fopen("results.csv", "w+");
     fprintf(csv_file, "image_number, guess\n");
-    for (int i = 0; i < size; i++) {
+    for (int i = 0; i < input_count; i++) {
         fprintf(csv_file, "%d, %c\n", i+1, letters[results[i]]);
     }
     fclose(csv_file);
diff --git a/src/matrix.cu b/src/matrix.cu
index 205e62b..0188c2c 100644
--- a/src/matrix.cu
+++ b/src/matrix.cu
@@ -40,6 +40,15 @@ matrix* copy_to_device(matrix* h_mat) {
     return res;
 }
 
+__device__ __host__ matrix* create_copy(matrix* mat){
+    matrix* res = (matrix*)malloc(sizeof(matrix));
+    res->rows = mat->rows;
+    res->cols = mat->cols;
+    res->data = (float*)malloc((res->rows * res->cols) * sizeof(float));
+    memcpy(res->data,mat->data,res->rows*res->cols*sizeof(float));
+    return res;
+}
+
 __device__ void matrix_mul(float* weight, float* input, float* result, int w_rows, int w_cols) {
     for (int i = 0; i < w_rows; i++) {
         float sum = 0;
diff --git a/src/matrix.cuh b/src/matrix.cuh
index 235ae78..b2191cb 100644
--- a/src/matrix.cuh
+++ b/src/matrix.cuh
@@ -20,4 +20,6 @@ __device__ void relu(float* a, int rows);
 
 __device__ void softmax(float* a, int rows);
 
-__device__ int argmax(float* a, int rows);
\ No newline at end of file
+__device__ int argmax(float* a, int rows);
+
+__device__ __host__ matrix* create_copy(matrix* mat);

From c1639bc6fc61254dff46133c1fe91f6dbbf46ab5 Mon Sep 17 00:00:00 2001
From: nhatdongdang <144138246+nhatdongdang@users.noreply.github.com>
Date: Fri, 5 Jul 2024 06:39:26 +0000
Subject: [PATCH 3/4] Tune block size and grid size

---
 src/main.cu | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/main.cu b/src/main.cu
index 5b4b779..edee095 100644
--- a/src/main.cu
+++ b/src/main.cu
@@ -125,7 +125,6 @@ __global__ void infer(matrix** d_inputs, int* d_results, matrix** d_weights, mat
     outputs[1] = new_matrix(65, 1);
 
     for (int i = thread_idx; i < it_per_input; i += num_threads) {
-        // printf("Working on inference %d\n", i);
         propagate_fwd(d_weights[0], input, outputs[0], d_biases[0]);
         relu(outputs[0]->data, 98);
 
@@ -278,7 +277,7 @@ int main(int argc, char* argv[]) {
     for (int i = 0; i < input_count; i++) {
         infer<<<108,69>>>(d_inputs, d_results, d_weights, d_biases, IT_PER_IN, i);
         err = cudaGetLastError();
-            if (err != cudaSuccess) {
+        if (err != cudaSuccess) {
             printf("CUDA error: %s\n", cudaGetErrorString(err));
         }
     }

From e8db37a68836952cc541723c7051bfdacebeebc9 Mon Sep 17 00:00:00 2001
From: nhatdongdang <144138246+nhatdongdang@users.noreply.github.com>
Date: Fri, 5 Jul 2024 06:46:44 +0000
Subject: [PATCH 4/4] Fix style

---
 src/main.cu | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/main.cu b/src/main.cu
index edee095..486495f 100644
--- a/src/main.cu
+++ b/src/main.cu
@@ -109,14 +109,12 @@ __device__ void propagate_fwd(matrix* weights, matrix* input_layer, matrix* outp
     matrix_add(output_layer->data, biases->data, biases->rows);
 }
 
-// __global__ void infer(int a) {
 __global__ void infer(matrix** d_inputs, int* d_results, matrix** d_weights, matrix** d_biases, int it_per_input, int in_num) {
     int num_threads = blockDim.x * gridDim.x;
     int thread_idx = (blockIdx.x * blockDim.x + threadIdx.x);
 
     if (thread_idx > it_per_input) return;
 
-    // printf("Thread %d at input %d\n", thread_idx, in_num);
 
     matrix* input = d_inputs[in_num];
 
@@ -178,7 +176,6 @@ int main(int argc, char* argv[]) {
     weights[5] = new_matrix(40, 25);
     weights[6] = new_matrix(52, 40);
 
-
     biases[0] = new_matrix(98, 1);
     biases[1] = new_matrix(65, 1);
     biases[2] = new_matrix(50, 1);