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

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

@@ -1,4 +1,4 @@
-# Practical-4 (CUDA Programs for Addition and Multiplication)
+# Practical-4 (Vector Addition and Matrix Multiplication)
 
 Problem Statement:
 Write a CUDA Program for:

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

@@ -13,6 +13,7 @@ This repository compiles essential resources for the SPPU Computer Engineering P
 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
 
@@ -38,6 +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