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aeab82ff30
- Added Python samples for CUDA Python 1.0 release - Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
141 lines
4.7 KiB
Markdown
141 lines
4.7 KiB
Markdown
# ipcMemoryPool (Python)
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## Description
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This sample demonstrates how to share GPU memory between Python
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processes using CUDA Inter-Process Communication (IPC) and
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`cuda.core`'s IPC-enabled memory pools.
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By default each process has its own CUDA virtual address space and
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cannot see allocations made by another process. With an IPC-enabled
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`DeviceMemoryResource` the parent allocates once, and the child
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process maps that same physical GPU memory into its own address space
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so both read and write the same bytes. The sample performs a
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round-trip test:
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1. Parent creates an IPC-enabled `DeviceMemoryResource` and allocates
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a `Buffer`.
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2. Parent fills the buffer with a known pattern.
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3. Parent sends the `Buffer` to a child process through an
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`multiprocessing.Queue`. cuda.core's pickle reducers re-create the
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memory resource and map the buffer in the child.
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4. Child verifies the parent's pattern, writes a new pattern, and
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signals completion.
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5. Parent verifies the child's writes.
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## What You'll Learn
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- Enabling IPC on a `DeviceMemoryResource` with `ipc_enabled=True`
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- Sending `Buffer` objects across process boundaries via `mp.Queue`
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- How cuda.core's pickle reducers rebuild the MR and map the buffer
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in the receiving process
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- Why `multiprocessing` must use the `"spawn"` start method with CUDA
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- Detecting IPC support at runtime (POSIX file-descriptor handle
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type, memory-pool support, Linux-only)
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## Key Libraries
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- [`cuda.core`](https://nvidia.github.io/cuda-python/cuda-core/latest/) - IPC-enabled memory resources and buffer reducers
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- `cupy` - zero-copy views over the shared device memory via DLPack
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- `multiprocessing` - standard library process management
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## Key APIs
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### From `cuda.core`
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- `DeviceMemoryResource(device, options=DeviceMemoryResourceOptions(ipc_enabled=True))` - create an IPC-enabled memory pool
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- `DeviceMemoryResourceOptions(max_size=..., ipc_enabled=True)` - configure the underlying pool
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- `mr.allocate(nbytes)` - allocate a `Buffer` from the IPC pool
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- `Buffer.is_mapped` - True when the buffer is usable in the current process
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- `Device.properties.memory_pools_supported` - runtime feature check
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- `Device.properties.handle_type_posix_file_descriptor_supported` - runtime feature check
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### From `cuda_samples_utils`
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- `print_gpu_info()` - print device name and compute capability
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## Requirements
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### Hardware
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- NVIDIA GPU with Compute Capability 7.0 or higher
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- Device that supports CUDA memory pools and POSIX file-descriptor IPC handles (the sample detects and reports this at startup)
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- Minimum GPU memory: 512 MB
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### Software
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- Linux x86_64 (POSIX file-descriptor IPC handles are not available on Windows or macOS)
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- CUDA Toolkit 13.0 or newer (matches `cuda-python` 13.x)
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- Python 3.10 or newer
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- `cuda-python` (>=13.0.0)
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- `cuda-core` (>=0.6.0)
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- `cupy-cuda13x` (>=13.0.0)
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## Installation
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Install the required packages from `requirements.txt`:
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```bash
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cd /path/to/cuda-samples/python/4_DistributedComputing/ipcMemoryPool
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pip install -r requirements.txt
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```
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The `requirements.txt` installs:
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- `cuda-python` (>=13.0.0)
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- `cuda-core` (>=0.6.0)
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- `cupy-cuda13x` (>=13.0.0)
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## How to Run
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### Basic usage
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```bash
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cd cuda-samples/python/4_DistributedComputing/ipcMemoryPool
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python ipcMemoryPool.py
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```
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### With custom parameters
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```bash
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# Larger shared buffer
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python ipcMemoryPool.py --elements 65536
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# Use a specific GPU
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python ipcMemoryPool.py --device 1
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```
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On platforms or devices that do not support CUDA IPC, the sample
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prints a diagnostic and exits cleanly with status 0.
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## Expected Output
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```
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Device: <Your GPU Name>
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Compute Capability: <X.Y>
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Created IPC-enabled DeviceMemoryResource (is_ipc_enabled=True)
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Parent wrote pattern (first 5 values): [100. 101. 102. 103. 104.]
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Parent sent buffer to child pid=<pid>; waiting...
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[child pid=<pid>] received buffer: is_mapped=True, size=4096
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Parent sees child's pattern (first 5 values): [-0. -1. -2. -3. -4.]
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IPC round-trip: OK
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```
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**Note:** Device name, compute capability, and child PID will vary
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based on your system.
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## Files
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- `ipcMemoryPool.py` - Python implementation using `cuda.core` IPC memory pools
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- `README.md` - This file
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- `requirements.txt` - Sample dependencies
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- `../../Utilities/cuda_samples_utils.py` - Common utilities (imported by this sample)
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## See Also
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- [CUDA Python Documentation](https://nvidia.github.io/cuda-python/)
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- [`cuda.core` memory API](https://nvidia.github.io/cuda-python/cuda-core/latest/api.html#memory)
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- Upstream `cuda.core` IPC tests: [`test_memory_ipc.py`](https://github.com/NVIDIA/cuda-python/blob/main/cuda_core/tests/memory_ipc/test_memory_ipc.py)
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- [CUDA IPC programming guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#interprocess-communication)
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