cuda-samples/cpp/0_Introduction/vectorAdd/README.md

Sample: Vector Addition (Unified Memory)

Description

A first-look CUDA sample: add two float vectors element-wise on the GPU. This version uses Unified Memory (cudaMallocManaged) so the same pointer is accessible from both host and device, eliminating the need for explicit cudaMemcpy calls. The GPU result is verified against a CPU reference computed on the same buffers.

The sample mirrors the corresponding Unified Memory example in the CUDA Programming Guide and uses the //unified-memory-begin / //unified-memory-end markers so the guide can extract the snippet directly.

What You'll Learn

• Allocating memory accessible to both host and device with cudaMallocManaged
• Launching a 1D CUDA kernel that operates on a Unified Memory buffer with no explicit transfers
• Computing grid dimensions with cuda::ceil_div (from <cuda/cmath>)
• Synchronizing host execution with device work via cudaDeviceSynchronize
• Validating GPU output against a serial CPU reference

Key Concepts

• Unified Memory — one pointer for both CPU and GPU; the CUDA runtime migrates pages on demand
• 1D thread indexing — threadIdx.x + blockIdx.x * blockDim.x
• Bounds checking in the kernel so the vector length need not be a multiple of the block size

Key APIs

CUDA Runtime

• cudaMallocManaged — allocate Unified Memory accessible to both host and device
• cudaDeviceSynchronize — block the host until all submitted device work completes
• cudaFree — release a Unified Memory allocation

libcudacxx

• cuda::ceil_div — ceil(a / b) used to compute the number of blocks; from <cuda/cmath>

Requirements

Hardware

• NVIDIA GPU with Compute Capability 7.5 or higher

Software

• CMake 3.20 or newer
• A C++17-capable host compiler

How to Build

See the top-level README for full build instructions, including how to build all samples or a single sample standalone.

How to Run

Basic usage (default vector length = 1024)

./vectorAdd

Custom vector length

./vectorAdd 1000000

Expected Output

On success:

Unified Memory: CPU and GPU answers match

On failure, the first mismatching index and the two diverging values are printed, followed by an error line:

Index 0 mismatch: 0.123456 != 0.123400
Unified Memory: Error - CPU and GPU answers do not match

Files

• vectorAdd.cu — Unified Memory vector addition implementation (kernel + host driver)
• README.md — this file
• CMakeLists.txt — build configuration

See Also

• CUDA C++ Programming Guide — vectorAdd example with Unified Memory