add answers for may-june 2025 + november-december 2025 pyqs for unit 6 (High Performance Computing Applications)

Codes/Code-4/.gitattributes

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+attachments/change-runtime.png filter=lfs diff=lfs merge=lfs -text
+attachments/runtime-navbar.png filter=lfs diff=lfs merge=lfs -text
+attachments/select-t4-gpu.png filter=lfs diff=lfs merge=lfs -text

Codes/Code-4/Code-4-Steps.md

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+# Practical-4 (Vector Addition and Matrix Multiplication)
+
+Problem Statement:
+Write a CUDA Program for:
+1. Addition of two large vectors
+2. Matrix Multiplication using CUDA C
+
+---
+
+## Pre-requisities
+
+1. Open [Google Colab](https://colab.research.google.com/)
+2. Create a new Jupyter Notebook
+
+---
+
+## Steps
+
+### 1. After creating a new Jupyter notebook, click on "Runtime" in the navbar:
+
+<img src="attachments/runtime-navbar.png" alt="Runtime in navbar in Google Colab" width=350>
+
+### 2. Then, choose "Change runtime type":
+
+<img src="attachments/change-runtime.png" alt="Change runtime type option in Runtime section on Google Colab" width=300>
+
+### 3. Select "T4 GPU", and save:
+
+<img src="attachments/select-t4-gpu.png" alt="T4 GPU option selected in Google Colab as Runtime" width=300>
+
+### 4. Check if `nvcc` is installed:
+
+```python3
+!nvcc --version
+```
+
+### 5. Install `nvcc4jupyter`:
+
+```python3
+!pip install nvcc4jupyter
+# Or if the above command fails, comment the above line and run
+# !pip install git+https://git.kska.io/notkshitij/nvcc.git
+```
+
+### 6. Load it:
+
+```python3
+%load_ext nvcc4jupyter
+```
+
+### 7. Paste the below code in a new code block:
+
+```cu
+%%writefile cuda_program.cu
+#include <iostream>
+#include <cuda.h>
+
+using namespace std;
+
+#define BLOCK_SIZE 2
+
+// Vector Addition Kernel
+// Each thread computes a single element of C = A + B.
+__global__ void vectorAdd(int *A, int *B, int *C, int N) {
+    int i = blockIdx.x * blockDim.x + threadIdx.x;
+    // Guard against threads beyond the vector size (when N is not a multiple
+    // of the block size, some threads in the last block are out of range).
+    if (i < N)
+        C[i] = A[i] + B[i];
+}
+
+// Matrix Multiplication Kernel
+// Each thread computes a single element of C = A * B.
+// Thread (row, col) sums the dot product of row `row` of A with column `col` of B.
+__global__ void matrixMul(float *A, float *B, float *C, int N) {
+    int row = blockIdx.y * blockDim.y + threadIdx.y;
+    int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+    float sum = 0.0f;
+    for (int n = 0; n < N; ++n)
+        sum += A[row * N + n] * B[n * N + col];
+
+    C[row * N + col] = sum;
+}
+
+// Vector Addition
+void runVectorAddition() {
+    int N;
+    cout << "\n=== Vector Addition ===" << endl;
+    cout << "Enter vector size: ";
+    cin >> N;
+
+    int size = N * sizeof(int);
+
+    // Host allocation and initialisation
+    int *hA = new int[N];
+    int *hB = new int[N];
+    int *hC = new int[N];
+
+    for (int i = 0; i < N; i++) {
+        hA[i] = i;
+        hB[i] = i * 2;
+    }
+
+    cout << "\nVector A: ";
+    for (int i = 0; i < N; i++) cout << hA[i] << " ";
+    cout << "\nVector B: ";
+    for (int i = 0; i < N; i++) cout << hB[i] << " ";
+    cout << endl;
+
+    // Device allocation and transfer
+    int *dA, *dB, *dC;
+    cudaMalloc(&dA, size);
+    cudaMalloc(&dB, size);
+    cudaMalloc(&dC, size);
+
+    cudaMemcpy(dA, hA, size, cudaMemcpyHostToDevice);
+    cudaMemcpy(dB, hB, size, cudaMemcpyHostToDevice);
+
+    // Launch with enough blocks to cover all N elements.
+    // (N + BLOCK_SIZE - 1) / BLOCK_SIZE rounds up so we don't miss the tail.
+    int numBlocks = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
+    vectorAdd<<<numBlocks, BLOCK_SIZE>>>(dA, dB, dC, N);
+
+    cudaMemcpy(hC, dC, size, cudaMemcpyDeviceToHost);
+
+    cout << "Result A + B: ";
+    for (int i = 0; i < N; i++) cout << hC[i] << " ";
+    cout << endl;
+
+    delete[] hA;
+    delete[] hB;
+    delete[] hC;
+    cudaFree(dA);
+    cudaFree(dB);
+    cudaFree(dC);
+}
+
+// Matrix Multiplication
+void runMatrixMultiplication() {
+    int K, N;
+    cout << "\n=== Matrix Multiplication ===" << endl;
+    cout << "Enter K (matrix will be N x N where N = K * " << BLOCK_SIZE << "): ";
+    cin >> K;
+    N = K * BLOCK_SIZE;
+
+    cout << "Matrix size: " << N << " x " << N << endl;
+    int size = N * N * sizeof(float);
+
+    // Host allocation and initialisation
+    float *hA = new float[N * N];
+    float *hB = new float[N * N];
+    float *hC = new float[N * N];
+
+    for (int j = 0; j < N; j++) {
+        for (int i = 0; i < N; i++) {
+            hA[j * N + i] = 2;
+            hB[j * N + i] = 4;
+        }
+    }
+
+    cout << "\nMatrix A:\n";
+    for (int row = 0; row < N; row++) {
+        for (int col = 0; col < N; col++)
+            cout << hA[row * N + col] << " ";
+        cout << endl;
+    }
+
+    cout << "\nMatrix B:\n";
+    for (int row = 0; row < N; row++) {
+        for (int col = 0; col < N; col++)
+            cout << hB[row * N + col] << " ";
+        cout << endl;
+    }
+
+    // Device allocation and transfer
+    float *dA, *dB, *dC;
+    cudaMalloc(&dA, size);
+    cudaMalloc(&dB, size);
+    cudaMalloc(&dC, size);
+
+    cudaMemcpy(dA, hA, size, cudaMemcpyHostToDevice);
+    cudaMemcpy(dB, hB, size, cudaMemcpyHostToDevice);
+
+    // threadBlock: BLOCK_SIZE x BLOCK_SIZE threads per block.
+    // grid: K x K blocks, so total threads = N x N (one per output element).
+    dim3 threadBlock(BLOCK_SIZE, BLOCK_SIZE);
+    dim3 grid(K, K);
+    matrixMul<<<grid, threadBlock>>>(dA, dB, dC, N);
+
+    cudaMemcpy(hC, dC, size, cudaMemcpyDeviceToHost);
+
+    cout << "\nResult C = A * B:\n";
+    for (int row = 0; row < N; row++) {
+        for (int col = 0; col < N; col++)
+            cout << hC[row * N + col] << " ";
+        cout << endl;
+    }
+
+    delete[] hA;
+    delete[] hB;
+    delete[] hC;
+    cudaFree(dA);
+    cudaFree(dB);
+    cudaFree(dC);
+}
+
+int main() {
+    runVectorAddition();
+    runMatrixMultiplication();
+
+    cout << "\nFinished." << endl;
+    return 0;
+}
+```
+
+### 8. Compile and run:
+
+```python3
+!nvcc cuda_program.cu -o cuda_program && ./cuda_program
+```
+
+---
+
+## Sample output
+
+```md
+=== Vector Addition ===
+Enter vector size: 2
+
+Vector A: 0 1 
+Vector B: 0 2 
+Result A + B: 0 3 
+
+=== Matrix Multiplication ===
+Enter K (matrix will be N x N where N = K * 2): 3
+Matrix size: 6 x 6
+
+Matrix A:
+2 2 2 2 2 2 
+2 2 2 2 2 2 
+2 2 2 2 2 2 
+2 2 2 2 2 2 
+2 2 2 2 2 2 
+2 2 2 2 2 2 
+
+Matrix B:
+4 4 4 4 4 4 
+4 4 4 4 4 4 
+4 4 4 4 4 4 
+4 4 4 4 4 4 
+4 4 4 4 4 4 
+4 4 4 4 4 4 
+
+Result C = A * B:
+48 48 48 48 48 48 
+48 48 48 48 48 48 
+48 48 48 48 48 48 
+48 48 48 48 48 48 
+48 48 48 48 48 48 
+48 48 48 48 48 48 
+
+Finished.
+```
+
+---
+

Codes/Code-4/Notebook-4.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sSZ5XEy-IFoj",
+        "outputId": "8bac00c3-0327-4682-f636-b6b253db5201"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "nvcc: NVIDIA (R) Cuda compiler driver\n",
+            "Copyright (c) 2005-2025 NVIDIA Corporation\n",
+            "Built on Fri_Feb_21_20:23:50_PST_2025\n",
+            "Cuda compilation tools, release 12.8, V12.8.93\n",
+            "Build cuda_12.8.r12.8/compiler.35583870_0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!nvcc --version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install nvcc4jupyter\n",
+        "# Or if the above command fails, comment the above line and run\n",
+        "# !pip install git+https://git.kska.io/notkshitij/nvcc.git"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1jHq-AKfIINd",
+        "outputId": "818ccbb0-9383-4be5-cd79-c9527c5806c9"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: nvcc4jupyter in /usr/local/lib/python3.12/dist-packages (1.2.1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%load_ext nvcc4jupyter"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9nuQsRZMIROH",
+        "outputId": "e8508f1c-9895-4e8c-e7f0-d9e34aab21a1"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The nvcc4jupyter extension is already loaded. To reload it, use:\n",
+            "  %reload_ext nvcc4jupyter\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%writefile cuda_program.cu\n",
+        "#include <iostream>\n",
+        "#include <cuda.h>\n",
+        "\n",
+        "using namespace std;\n",
+        "\n",
+        "#define BLOCK_SIZE 2\n",
+        "\n",
+        "// Vector Addition Kernel\n",
+        "// Each thread computes a single element of C = A + B.\n",
+        "__global__ void vectorAdd(int *A, int *B, int *C, int N) {\n",
+        "    int i = blockIdx.x * blockDim.x + threadIdx.x;\n",
+        "    // Guard against threads beyond the vector size (when N is not a multiple\n",
+        "    // of the block size, some threads in the last block are out of range).\n",
+        "    if (i < N)\n",
+        "        C[i] = A[i] + B[i];\n",
+        "}\n",
+        "\n",
+        "// Matrix Multiplication Kernel\n",
+        "// Each thread computes a single element of C = A * B.\n",
+        "// Thread (row, col) sums the dot product of row `row` of A with column `col` of B.\n",
+        "__global__ void matrixMul(float *A, float *B, float *C, int N) {\n",
+        "    int row = blockIdx.y * blockDim.y + threadIdx.y;\n",
+        "    int col = blockIdx.x * blockDim.x + threadIdx.x;\n",
+        "\n",
+        "    float sum = 0.0f;\n",
+        "    for (int n = 0; n < N; ++n)\n",
+        "        sum += A[row * N + n] * B[n * N + col];\n",
+        "\n",
+        "    C[row * N + col] = sum;\n",
+        "}\n",
+        "\n",
+        "// Vector Addition\n",
+        "void runVectorAddition() {\n",
+        "    int N;\n",
+        "    cout << \"\\n=== Vector Addition ===\" << endl;\n",
+        "    cout << \"Enter vector size: \";\n",
+        "    cin >> N;\n",
+        "\n",
+        "    int size = N * sizeof(int);\n",
+        "\n",
+        "    // Host allocation and initialisation\n",
+        "    int *hA = new int[N];\n",
+        "    int *hB = new int[N];\n",
+        "    int *hC = new int[N];\n",
+        "\n",
+        "    for (int i = 0; i < N; i++) {\n",
+        "        hA[i] = i;\n",
+        "        hB[i] = i * 2;\n",
+        "    }\n",
+        "\n",
+        "    cout << \"\\nVector A: \";\n",
+        "    for (int i = 0; i < N; i++) cout << hA[i] << \" \";\n",
+        "    cout << \"\\nVector B: \";\n",
+        "    for (int i = 0; i < N; i++) cout << hB[i] << \" \";\n",
+        "    cout << endl;\n",
+        "\n",
+        "    // Device allocation and transfer\n",
+        "    int *dA, *dB, *dC;\n",
+        "    cudaMalloc(&dA, size);\n",
+        "    cudaMalloc(&dB, size);\n",
+        "    cudaMalloc(&dC, size);\n",
+        "\n",
+        "    cudaMemcpy(dA, hA, size, cudaMemcpyHostToDevice);\n",
+        "    cudaMemcpy(dB, hB, size, cudaMemcpyHostToDevice);\n",
+        "\n",
+        "    // Launch with enough blocks to cover all N elements.\n",
+        "    // (N + BLOCK_SIZE - 1) / BLOCK_SIZE rounds up so we don't miss the tail.\n",
+        "    int numBlocks = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;\n",
+        "    vectorAdd<<<numBlocks, BLOCK_SIZE>>>(dA, dB, dC, N);\n",
+        "\n",
+        "    cudaMemcpy(hC, dC, size, cudaMemcpyDeviceToHost);\n",
+        "\n",
+        "    cout << \"Result A + B: \";\n",
+        "    for (int i = 0; i < N; i++) cout << hC[i] << \" \";\n",
+        "    cout << endl;\n",
+        "\n",
+        "    delete[] hA;\n",
+        "    delete[] hB;\n",
+        "    delete[] hC;\n",
+        "    cudaFree(dA);\n",
+        "    cudaFree(dB);\n",
+        "    cudaFree(dC);\n",
+        "}\n",
+        "\n",
+        "// Matrix Multiplication\n",
+        "void runMatrixMultiplication() {\n",
+        "    int K, N;\n",
+        "    cout << \"\\n=== Matrix Multiplication ===\" << endl;\n",
+        "    cout << \"Enter K (matrix will be N x N where N = K * \" << BLOCK_SIZE << \"): \";\n",
+        "    cin >> K;\n",
+        "    N = K * BLOCK_SIZE;\n",
+        "\n",
+        "    cout << \"Matrix size: \" << N << \" x \" << N << endl;\n",
+        "    int size = N * N * sizeof(float);\n",
+        "\n",
+        "    // Host allocation and initialisation\n",
+        "    float *hA = new float[N * N];\n",
+        "    float *hB = new float[N * N];\n",
+        "    float *hC = new float[N * N];\n",
+        "\n",
+        "    for (int j = 0; j < N; j++) {\n",
+        "        for (int i = 0; i < N; i++) {\n",
+        "            hA[j * N + i] = 2;\n",
+        "            hB[j * N + i] = 4;\n",
+        "        }\n",
+        "    }\n",
+        "\n",
+        "    cout << \"\\nMatrix A:\\n\";\n",
+        "    for (int row = 0; row < N; row++) {\n",
+        "        for (int col = 0; col < N; col++)\n",
+        "            cout << hA[row * N + col] << \" \";\n",
+        "        cout << endl;\n",
+        "    }\n",
+        "\n",
+        "    cout << \"\\nMatrix B:\\n\";\n",
+        "    for (int row = 0; row < N; row++) {\n",
+        "        for (int col = 0; col < N; col++)\n",
+        "            cout << hB[row * N + col] << \" \";\n",
+        "        cout << endl;\n",
+        "    }\n",
+        "\n",
+        "    // Device allocation and transfer\n",
+        "    float *dA, *dB, *dC;\n",
+        "    cudaMalloc(&dA, size);\n",
+        "    cudaMalloc(&dB, size);\n",
+        "    cudaMalloc(&dC, size);\n",
+        "\n",
+        "    cudaMemcpy(dA, hA, size, cudaMemcpyHostToDevice);\n",
+        "    cudaMemcpy(dB, hB, size, cudaMemcpyHostToDevice);\n",
+        "\n",
+        "    // threadBlock: BLOCK_SIZE x BLOCK_SIZE threads per block.\n",
+        "    // grid: K x K blocks, so total threads = N x N (one per output element).\n",
+        "    dim3 threadBlock(BLOCK_SIZE, BLOCK_SIZE);\n",
+        "    dim3 grid(K, K);\n",
+        "    matrixMul<<<grid, threadBlock>>>(dA, dB, dC, N);\n",
+        "\n",
+        "    cudaMemcpy(hC, dC, size, cudaMemcpyDeviceToHost);\n",
+        "\n",
+        "    cout << \"\\nResult C = A * B:\\n\";\n",
+        "    for (int row = 0; row < N; row++) {\n",
+        "        for (int col = 0; col < N; col++)\n",
+        "            cout << hC[row * N + col] << \" \";\n",
+        "        cout << endl;\n",
+        "    }\n",
+        "\n",
+        "    delete[] hA;\n",
+        "    delete[] hB;\n",
+        "    delete[] hC;\n",
+        "    cudaFree(dA);\n",
+        "    cudaFree(dB);\n",
+        "    cudaFree(dC);\n",
+        "}\n",
+        "\n",
+        "int main() {\n",
+        "    runVectorAddition();\n",
+        "    runMatrixMultiplication();\n",
+        "\n",
+        "    cout << \"\\nFinished.\" << endl;\n",
+        "    return 0;\n",
+        "}\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nvCj8UmhIh3o",
+        "outputId": "54a31aec-f860-4b72-a4e1-03a392def6f6"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Overwriting cuda_program.cu\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!nvcc cuda_program.cu -o cuda_program && ./cuda_program"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F7fC0LtbJ5o8",
+        "outputId": "9d5988b2-ad42-4b0b-c84d-c1698e778bb9"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "nvcc warning : Support for offline compilation for architectures prior to '<compute/sm/lto>_75' will be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).\n",
+            "\n",
+            "=== Vector Addition ===\n",
+            "Enter vector size: 2\n",
+            "\n",
+            "Vector A: 0 1 \n",
+            "Vector B: 0 2 \n",
+            "Result A + B: 0 3 \n",
+            "\n",
+            "=== Matrix Multiplication ===\n",
+            "Enter K (matrix will be N x N where N = K * 2): 3\n",
+            "Matrix size: 6 x 6\n",
+            "\n",
+            "Matrix A:\n",
+            "2 2 2 2 2 2 \n",
+            "2 2 2 2 2 2 \n",
+            "2 2 2 2 2 2 \n",
+            "2 2 2 2 2 2 \n",
+            "2 2 2 2 2 2 \n",
+            "2 2 2 2 2 2 \n",
+            "\n",
+            "Matrix B:\n",
+            "4 4 4 4 4 4 \n",
+            "4 4 4 4 4 4 \n",
+            "4 4 4 4 4 4 \n",
+            "4 4 4 4 4 4 \n",
+            "4 4 4 4 4 4 \n",
+            "4 4 4 4 4 4 \n",
+            "\n",
+            "Result C = A * B:\n",
+            "48 48 48 48 48 48 \n",
+            "48 48 48 48 48 48 \n",
+            "48 48 48 48 48 48 \n",
+            "48 48 48 48 48 48 \n",
+            "48 48 48 48 48 48 \n",
+            "48 48 48 48 48 48 \n",
+            "\n",
+            "Finished.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "HjhrulSNKHkq"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Codes/Code-4/cuda_program.cu

@@ -0,0 +1,161 @@
+# %%writefile cuda_program.cu
+#include <iostream>
+#include <cuda.h>
+
+using namespace std;
+
+#define BLOCK_SIZE 2
+
+// ─── Vector Addition Kernel ──────────────────────────────────────────────────
+// Each thread computes a single element of C = A + B.
+__global__ void vectorAdd(int *A, int *B, int *C, int N) {
+    int i = blockIdx.x * blockDim.x + threadIdx.x;
+    // Guard against threads beyond the vector size (when N is not a multiple
+    // of the block size, some threads in the last block are out of range).
+    if (i < N)
+        C[i] = A[i] + B[i];
+}
+
+// ─── Matrix Multiplication Kernel ────────────────────────────────────────────
+// Each thread computes a single element of C = A * B.
+// Thread (row, col) sums the dot product of row `row` of A with column `col` of B.
+__global__ void matrixMul(float *A, float *B, float *C, int N) {
+    int row = blockIdx.y * blockDim.y + threadIdx.y;
+    int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+    float sum = 0.0f;
+    for (int n = 0; n < N; ++n)
+        sum += A[row * N + n] * B[n * N + col];
+
+    C[row * N + col] = sum;
+}
+
+// ─── Vector Addition ─────────────────────────────────────────────────────────
+void runVectorAddition() {
+    int N;
+    cout << "\n=== Vector Addition ===" << endl;
+    cout << "Enter vector size: ";
+    cin >> N;
+
+    int size = N * sizeof(int);
+
+    // Host allocation and initialisation
+    int *hA = new int[N];
+    int *hB = new int[N];
+    int *hC = new int[N];
+
+    for (int i = 0; i < N; i++) {
+        hA[i] = i;
+        hB[i] = i * 2;
+    }
+
+    cout << "\nVector A: ";
+    for (int i = 0; i < N; i++) cout << hA[i] << " ";
+    cout << "\nVector B: ";
+    for (int i = 0; i < N; i++) cout << hB[i] << " ";
+    cout << endl;
+
+    // Device allocation and transfer
+    int *dA, *dB, *dC;
+    cudaMalloc(&dA, size);
+    cudaMalloc(&dB, size);
+    cudaMalloc(&dC, size);
+
+    cudaMemcpy(dA, hA, size, cudaMemcpyHostToDevice);
+    cudaMemcpy(dB, hB, size, cudaMemcpyHostToDevice);
+
+    // Launch with enough blocks to cover all N elements.
+    // (N + BLOCK_SIZE - 1) / BLOCK_SIZE rounds up so we don't miss the tail.
+    int numBlocks = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
+    vectorAdd<<<numBlocks, BLOCK_SIZE>>>(dA, dB, dC, N);
+
+    cudaMemcpy(hC, dC, size, cudaMemcpyDeviceToHost);
+
+    cout << "Result A + B: ";
+    for (int i = 0; i < N; i++) cout << hC[i] << " ";
+    cout << endl;
+
+    delete[] hA;
+    delete[] hB;
+    delete[] hC;
+    cudaFree(dA);
+    cudaFree(dB);
+    cudaFree(dC);
+}
+
+// ─── Matrix Multiplication ───────────────────────────────────────────────────
+void runMatrixMultiplication() {
+    int K, N;
+    cout << "\n=== Matrix Multiplication ===" << endl;
+    cout << "Enter K (matrix will be N x N where N = K * " << BLOCK_SIZE << "): ";
+    cin >> K;
+    N = K * BLOCK_SIZE;
+
+    cout << "Matrix size: " << N << " x " << N << endl;
+    int size = N * N * sizeof(float);
+
+    // Host allocation and initialisation
+    float *hA = new float[N * N];
+    float *hB = new float[N * N];
+    float *hC = new float[N * N];
+
+    for (int j = 0; j < N; j++) {
+        for (int i = 0; i < N; i++) {
+            hA[j * N + i] = 2;
+            hB[j * N + i] = 4;
+        }
+    }
+
+    cout << "\nMatrix A:\n";
+    for (int row = 0; row < N; row++) {
+        for (int col = 0; col < N; col++)
+            cout << hA[row * N + col] << " ";
+        cout << endl;
+    }
+
+    cout << "\nMatrix B:\n";
+    for (int row = 0; row < N; row++) {
+        for (int col = 0; col < N; col++)
+            cout << hB[row * N + col] << " ";
+        cout << endl;
+    }
+
+    // Device allocation and transfer
+    float *dA, *dB, *dC;
+    cudaMalloc(&dA, size);
+    cudaMalloc(&dB, size);
+    cudaMalloc(&dC, size);
+
+    cudaMemcpy(dA, hA, size, cudaMemcpyHostToDevice);
+    cudaMemcpy(dB, hB, size, cudaMemcpyHostToDevice);
+
+    // threadBlock: BLOCK_SIZE x BLOCK_SIZE threads per block.
+    // grid: K x K blocks, so total threads = N x N (one per output element).
+    dim3 threadBlock(BLOCK_SIZE, BLOCK_SIZE);
+    dim3 grid(K, K);
+    matrixMul<<<grid, threadBlock>>>(dA, dB, dC, N);
+
+    cudaMemcpy(hC, dC, size, cudaMemcpyDeviceToHost);
+
+    cout << "\nResult C = A * B:\n";
+    for (int row = 0; row < N; row++) {
+        for (int col = 0; col < N; col++)
+            cout << hC[row * N + col] << " ";
+        cout << endl;
+    }
+
+    delete[] hA;
+    delete[] hB;
+    delete[] hC;
+    cudaFree(dA);
+    cudaFree(dB);
+    cudaFree(dC);
+}
+
+int main() {
+    runVectorAddition();
+    runMatrixMultiplication();
+
+    cout << "\nFinished." << endl;
+    return 0;
+}

README.md

@@ -10,6 +10,11 @@ This repository compiles essential resources for the SPPU Computer Engineering P
 
 ### Codes
 
+1. [Code-1 (Parallel BFS and DFS)](Codes/Code-1.cpp)
+2. [Code-2 (Sequential and Parallel Bubble Sort and Merge Sort)](Codes/Code-2.cpp)
+3. [Code-3 (Min, Max, Sum, Average)](Codes/Code-3.cpp)
+4. [Code-4 (Vector Addition and Matrix Multiplication)](Codes/Code-4/)
+
 ### Practical
 
 1. [Practical-1](Practical/Practical-1/)
@@ -34,6 +39,8 @@ This repository compiles essential resources for the SPPU Computer Engineering P
 
 ### [IN-SEM PYQ Answers](Notes/IN-SEM%20PYQ%20Answers/)
 
+### [END-SEM PYQ Answers](Notes/END-SEM%20PYQ%20Answers/)
+
 ---
 
 ## Miscellaneous