$(TARGET): $(SOURCES) $(NVCC) $(NVCC_FLAGS) -o $@ $^

all: $(TARGET)

.PHONY: all run clean | Operation | Function | |-----------|----------| | Allocate GPU memory | cudaMalloc(&ptr, size) | | Free GPU memory | cudaFree(ptr) | | Copy to GPU | cudaMemcpy(dst, src, size, cudaMemcpyHostToDevice) | | Copy to CPU | cudaMemcpy(dst, src, size, cudaMemcpyDeviceToHost) | | Get GPU count | cudaGetDeviceCount(&count) | | Set active GPU | cudaSetDevice(device_id) | | Synchronize | cudaDeviceSynchronize() | | Error checking | cudaGetLastError() | Installation Check # Check CUDA version nvcc --version Check GPU driver & CUDA capability nvidia-smi Check available GPUs nvidia-smi -L This gives you a working starting point. Need a specific CUDA library example (cuBLAS for matrix multiplication, cuFFT for FFTs, or multi-GPU programming)?

// Launch kernel int threadsPerBlock = 256; int blocksPerGrid = (n + threadsPerBlock - 1) / threadsPerBlock; vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(d_a, d_b, d_c, n);

// Allocate host memory float *h_a = new float[n]; float *h_b = new float[n]; float *h_c = new float[n];

// Cleanup cudaFree(d_a); cudaFree(d_b); cudaFree(d_c); delete[] h_a; delete[] h_b; delete[] h_c;

// Copy data to device cudaMemcpy(d_a, h_a, bytes, cudaMemcpyHostToDevice); cudaMemcpy(d_b, h_b, bytes, cudaMemcpyHostToDevice);