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Release 13.4 of the CUDA Samples supported by CUDA Toolkit 13.4. See Changelog for more information.
83 lines
2.7 KiB
Markdown
83 lines
2.7 KiB
Markdown
# Sample: Vector Addition (Unified Memory)
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## Description
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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.
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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.
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## What You'll Learn
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- Allocating memory accessible to both host and device with `cudaMallocManaged`
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- Launching a 1D CUDA kernel that operates on a Unified Memory buffer with no explicit transfers
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- Computing grid dimensions with `cuda::ceil_div` (from `<cuda/cmath>`)
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- Synchronizing host execution with device work via `cudaDeviceSynchronize`
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- Validating GPU output against a serial CPU reference
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## Key Concepts
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- **Unified Memory** — one pointer for both CPU and GPU; the CUDA runtime migrates pages on demand
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- **1D thread indexing** — `threadIdx.x + blockIdx.x * blockDim.x`
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- **Bounds checking** in the kernel so the vector length need not be a multiple of the block size
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## Key APIs
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### CUDA Runtime
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- `cudaMallocManaged` — allocate Unified Memory accessible to both host and device
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- `cudaDeviceSynchronize` — block the host until all submitted device work completes
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- `cudaFree` — release a Unified Memory allocation
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### libcudacxx
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- `cuda::ceil_div` — `ceil(a / b)` used to compute the number of blocks; from `<cuda/cmath>`
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## Requirements
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### Hardware
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- NVIDIA GPU with Compute Capability 7.5 or higher
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### Software
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- CMake 3.20 or newer
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- A C++17-capable host compiler
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## How to Build
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See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone.
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## How to Run
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### Basic usage (default vector length = 1024)
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```bash
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./vectorAdd
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```
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### Custom vector length
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```bash
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./vectorAdd 1000000
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```
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## Expected Output
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On success:
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```text
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Unified Memory: CPU and GPU answers match
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```
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On failure, the first mismatching index and the two diverging values are printed, followed by an error line:
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```text
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Index 0 mismatch: 0.123456 != 0.123400
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Unified Memory: Error - CPU and GPU answers do not match
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```
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## Files
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- `vectorAdd.cu` — Unified Memory vector addition implementation (kernel + host driver)
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- `README.md` — this file
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- `CMakeLists.txt` — build configuration
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## See Also
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- [CUDA C++ Programming Guide — vectorAdd example with Unified Memory](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/intro-to-cuda-cpp.html)
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