Hello PyFR community,
I recently met Brian Vermere at a conference, who told me in conversation about PyFR and all its sleek features. I’m having some issues running large ANSYS CFX simulations on a SHARCNET computing cluster with memory allocation and poor cpu usage, and I wanted to take PyFR for a test drive as a possible substitute. The key here is I want to test out a bunch of stuff on my local machine before I decide to submit a formal request to have it installed on a HPC cluster.
I very quickly got the impression when browsing the website that PyFR is Linux focused (for good reason too!). So I tried installing PyFR (and dependancies) on Ubuntu (Xenial) running as a guest OS in VirtualBox hosted by Windows 7. Since my work PC is equipped with a GTX 460, it made sense to try the CUDA backend. Everything went smoothly until the part where I had to install CUDA. Since the guest OS doesn’t have direct access to the underlying hardware, a workaround is needed: PCI-passthrough. There’s not a lot of clear information on how this can be applied to the CUDA library, and I didn’t see anyone else who has done this with PyFR so I decided to try Windows instead.
I decided to try it on my home PC (because why not ), equipped with AMD Ryzen 1600X and Nvidia GeForce GTX 1060 SC running Windows 10.
Below is a summary of (from my experience) the installation steps necessary to install PyFR on Windows 10:
KEEP IN MIND:
-install/build 64-bit applications and libraries wherever possible.
-test software / module installations as you go.
-After doing this, I discovered Microsoft now supports a package manager called vcpkg, which can be used like apt-get on Ubuntu. I tested it with box2d, Lua, and METIS all of which downloaded and built without issue! Also on the list of packages is MS-MPI and CUDA (all latest versions).
Download and install Visual Studio 2015 / 2017 (I already had 2017 installed, community editions will probably suffice).
Download and install the Visual Studio 2015 Redistributable packages (I don’t think it’s necessary if you installed VS2015). This is necessary because we need to install a 64-bit version of Python (see step ), and Python 3.5 and 3.6 are the first two versions which are distributed in 64-bit flavors on Windows.* This is also necessary for CUDA (and MS-MPI I think).
Download and install Microsoft MPI**. You’ll need both the library and the executables. Here is the link to version 8.1
Add the directory where the executables were installed to your PATH.
Download and install CUDA. Make sure that you let CUDA install the its own graphics drivers using the express install (lest you run into the issue in this thread) (I installed version 8.0.61). No need for Visual Studio Integration.
Navigate to %CUDA_PATH%\bin\ and make a copy of the file cublas64_80.dll, name the copy cublas.dll (PyFR looks for cublas.dll, and we don’t want to dissapoint )
Download and install a 64-bit version of Python 3.5+ (I got 3.6.1 from here).
Add <PYTHON_ROOT>\Scripts to your PATH.
Install the following modules using pip, letting it find and install dependencies as necessary:
Install numpy from here (I also want to use scipy for other projects, but scipy depends on the Intel Math Kernel Library dependent functions in numpy).
Install mpi4py (allow pip to find online). I didn’t have any problems on my home PC, but I had to edit the configuration file (C:\Program Files\Python36\Lib\site-packages\mpi4py\mpi.cfg) to point to my MS-MPI library and executable directories on my work pc.
Install pycuda from here (I initially tried to install using pip, but there is a strange issue where
import pycuda.autoinitcauses Python to crash).
Install pyfr (allow pip to find online).1. Test the couette_flow_2d example in pyfr! The example cases aren’t included in the installed pyfr module, so just download the version from the PyFR website, and follow the instructions at the bottom of the User Guide page. Hopefully it works!
Download and install cmake (I got version 3.9.0-rc5)
Download and unpack METIS (I got version 5.1.0)
Follow the BUILD-Windows instructions cmake-gui option, and tick the SHARED checkbox before hitting generate (PyFR needs the shared library or .dll file, not the static .lib)1. Create a new environment variable called PYFR_METIS_LIBRARY_PATH, and set the value to the fully qualified path of the METIS .dll (e.g. C:/Program Files/METIS/metis.dll). PyFR looks using this environment variable before searching anywhere else.
This step involves editing the PyFR installed source (hopefully it will be obselete soon). For Python >= 3.5 the ctypes module is unable to find the Visual Studio C Runtime Library using the find_msvcrt() function (see this bug report), and it looks like the method for accessing those standard libraries in Windows has changed substantially. I just messed around with the ctypes module until I was able to access the required function (fflush). The result is a tweaked constructor for the
Silenceobject in util.py (see below).
Run the euler_vortex_2d and inc_cylinder_2d examples. Visualize them in Paraview if desired.
def init(self, stdout=os.devnull, stderr=os.devnull):
self.outfiles = stdout, stderr
self.combine = (stdout == stderr)
if sys.platform == ‘win32’:
self.libc_fflush = ctypes.windll.msvcrt.fflush
self.libc_fflush = CDLL(find_libc()).fflush
self.libc_fflush.argtypes = [c_void_p]
Hopefully the creators can implement this change a little more elegantly than I can.
- The PyFR website currently says that a 64-bit version of python is required because of a bug in numpy. I’m not sure what that bug is. Even if that bug is fixed, we still need the 64-bit version of python. This is because, as of CUDA version 7.0 the 32-bit version of cublas is no longer supported on Windows (it’s even deprecated on Linux!), and to work with the 64-bit cublas dll in PyFR, the python installation needs to be 64-bit. I guess everything could work with 32-bit if you used CUDA <= 6.X?
** I’ve looked into several versions of MPI for Windows:
- OpenMPI: Hasn’t supported Windows since version 1.6.5, which means it doesn’t meet PyFR’s requirements. I even downloaded it and tried to see if I could build it myself, but it has a heavy dependence on make+unix commands
- IntelMPI: Starts from $499… Nope nope nope. I got a free trial which I’m going to test out anyway (on my work PC which has an i7)
- IBM Platform MPI is the poor man’s IBM Spectrum MPI. Spectrum MPI is cuda-aware, and I’ll find out soon enough if Platform MPI is as well
- MS-MPI easy to install, free, MPI 2.? standard according to Wikipedia.
Anything missing from the instructions or other hangups people encountered?
Has anyone tried the PCI-Passthrough for their VM?
Next I’m going to try and fix the weird command-line printout while running (see below).
Also, I’d like to do some informal benchmarking on my system. All I can say right now is that the first two examples take a few minutes and the last one takes ~20 minutes or so? I’ll probably create a separate thread for that.