WIP: path_finder Search Priority v2 (#604)

* WIP (search priority updated in README.md but not in code)

* Completely replace cuda_paths.py to achieve the desired Search Priority (see updated README.md).

* Define `IS_WINDOWS = sys.platform == "win32"` in supported_libs.py

* Use os.path.samefile() to resolve issues with doubled backslashes.

* `load_in_subprocess(): Pass current environment

* Add run_python_code_safely.py as generated by perplexity, plus ruff format, bandit nosec

* Replace subprocess.run with run_python_code_safely

* Factor out `class Worker` to fix pickle issue.

* ChatGPT revisions based on Deep research:

https://chatgpt.com/share/681914ce-f274-8008-9e9f-4538716b4ed7

* Fix race condition in result queue handling by using timeout-based get()

The previous implementation checked result_queue.empty() before calling get(),
which introduces a classic race condition: the queue may become non-empty
immediately after the check, resulting in missed results or misleading errors.

This patch replaces the empty() check with result_queue.get(timeout=1.0),
allowing the parent process to robustly wait for results with a bounded delay.
Also switches from ctx.SimpleQueue() to ctx.Queue() for compatibility with
timeout-based get(), which SimpleQueue does not support on Python ≤3.12.

Note: The race condition was discovered by Gemini 2.5

* Resolve SIM108

* Change to "nppc" as ANCHOR_LIBNAME

* Implement CUDA_PYTHON_CUDA_HOME_PRIORITY first, last, with default first

* Remove retry_with_anchor_abs_path() and make retry_with_cuda_home_priority_last() the default.

* Update README.md to reflect new search priority

* SUPPORTED_LINUX_SONAMES does not need updates for CTK 12.9.0

* The only addition to SUPPORTED_WINDOWS_DLLS for CTK 12.9.0 is nvvm70.dll

* Make OSError in load_dl_windows.py abs_path_for_dynamic_library() more informative.

* run_cuda_bindings_path_finder.py: optionally use args as libnames (to aid debugging)

* Bug fix in load_dl_windows.py: ctypes.windll.kernel32.LoadLibraryW() returns an incompatible `handle`. Use win32api.LoadLibraryEx() instead to ensure self-consistency.

* Remove _find_nvidia_dynamic_library.retry_with_anchor_abs_path() method. Move run_python_code_safely.py to test/ directory.

Author: rwgk

cuda_bindings/cuda/bindings/_path_finder/README.md

@@ -24,52 +24,27 @@ strategy for locating NVIDIA shared libraries:
      The absolute path of the already loaded library will be returned, along
      with the handle to the library.
 
-1. **Python Package Ecosystem**
-   - Scans `sys.path` to find libraries installed via NVIDIA Python wheels.
+1. **NVIDIA Python wheels**
+   - Scans all site-packages to find libraries installed via NVIDIA Python wheels.
 
-2. **Conda Environments**
-   - Leverages Conda-specific paths through our fork of `get_cuda_paths()`
-     from numba-cuda.
-
-3. **Environment variables**
-   - Relies on `CUDA_HOME`/`CUDA_PATH` environment variables if set.
-
-4. **System Installations**
-   - Checks traditional system locations through these paths:
-     - Linux: `/usr/local/cuda/lib64`
-     - Windows: `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y\bin`
-       (where X.Y is the CTK version)
-   - **Notably does NOT search**:
-     - Versioned CUDA directories like `/usr/local/cuda-12.3`
-     - Distribution-specific packages (RPM/DEB)
-       EXCEPT Debian's `nvidia-cuda-toolkit`
-
-5. **OS Default Mechanisms**
+2. **OS default mechanisms / Conda environments**
    - Falls back to native loader:
      - `dlopen()` on Linux
      - `LoadLibraryW()` on Windows
+   - CTK installations with system config updates are expected to be discovered:
+     - Linux: Via `/etc/ld.so.conf.d/*cuda*.conf`
+     - Windows: Via `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y\bin` on system `PATH`
+   - Conda installations are expected to be discovered:
+     - Linux: Via `$ORIGIN/../lib` on `RPATH` (of the `python` binary)
+     - Windows: Via `%CONDA_PREFIX%\Library\bin` on system `PATH`
+
+3. **Environment variables**
+   - Relies on `CUDA_HOME` or `CUDA_PATH` environment variables if set
+     (in that order).
 
 Note that the search is done on a per-library basis. There is no centralized
 mechanism that ensures all libraries are found in the same way.
 
-## Implementation Philosophy
-
-The current implementation balances stability and evolution:
-
-- **Baseline Foundation:** Uses a fork of numba-cuda's `cuda_paths.py` that has been
-  battle-tested in production environments.
-
-- **Validation Infrastructure:** Comprehensive CI testing matrix being developed to cover:
-  - Various Linux/Windows environments
-  - Python packaging formats (wheels, conda)
-  - CUDA Toolkit versions
-
-- **Roadmap:** Planned refactoring to:
-  - Unify library discovery logic
-  - Improve maintainability
-  - Better enforce search priority
-  - Expand platform support
-
 ## Maintenance Requirements
 
 These key components must be updated for new CUDA Toolkit releases: