[petsc-dev] PETSc init eats too much CUDA memory

[Barry Smith]

  I don't think this is right. We want the device initialized by PETSc , we just don't want the cublas and cusolve stuff initialized. In order to see how much memory initializing the blas and solvers takes.

  So I think you need to comment things in cupminterface.hpp like cublasCreate and cusolverDnCreate.

  Urgh, I hate C++ where huge chunks of real code are in header files.



> On Jan 7, 2022, at 11:34 AM, Jacob Faibussowitsch <jacob.fai at gmail.com> wrote:
> 
> Hit send too early…
> 
> If you don’t want to comment out, you can also run with "-device_enable lazy" option. Normally this is the default behavior but if -log_view or -log_summary is provided this defaults to “-device_enable eager”. See src/sys/objects/device/interface/device.cxx:398
> 
> Best regards,
> 
> Jacob Faibussowitsch
> (Jacob Fai - booss - oh - vitch)
> 
>> On Jan 7, 2022, at 11:29, Jacob Faibussowitsch <jacob.fai at gmail.com <mailto:jacob.fai at gmail.com>> wrote:
>> 
>>> You need to go into the PetscInitialize() routine find where it loads the cublas and cusolve and comment out those lines then run with -log_view
>> 
>> Comment out
>> 
>> #if (PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP) || PetscDefined(HAVE_SYCL))
>>   ierr = PetscDeviceInitializeFromOptions_Internal(PETSC_COMM_WORLD);CHKERRQ(ierr);
>> #endif
>> 
>> At src/sys/objects/pinit.c:956
>> 
>> Best regards,
>> 
>> Jacob Faibussowitsch
>> (Jacob Fai - booss - oh - vitch)
>> 
>>> On Jan 7, 2022, at 11:24, Barry Smith <bsmith at petsc.dev <mailto:bsmith at petsc.dev>> wrote:
>>> 
>>> 
>>> Without log_view it does not load any cuBLAS/cuSolve immediately with -log_view it loads all that stuff at startup. You need to go into the PetscInitialize() routine find where it loads the cublas and cusolve and comment out those lines then run with -log_view
>>> 
>>> 
>>>> On Jan 7, 2022, at 11:14 AM, Zhang, Hong via petsc-dev <petsc-dev at mcs.anl.gov <mailto:petsc-dev at mcs.anl.gov>> wrote:
>>>> 
>>>> When PETSc is initialized, it takes about 2GB CUDA memory. This is way too much for doing nothing. A test script is attached to reproduce the issue. If I remove the first line "import torch", PETSc consumes about 0.73GB, which is still significant. Does anyone have any idea about this behavior?
>>>> 
>>>> Thanks,
>>>> Hong
>>>> 
>>>> hongzhang at gpu02:/gpfs/jlse-fs0/users/hongzhang/Projects/pnode/examples (caidao22/update-examples)$ python3 test.py
>>>> CUDA memory before PETSc 0.000GB
>>>> CUDA memory after PETSc 0.004GB
>>>> hongzhang at gpu02:/gpfs/jlse-fs0/users/hongzhang/Projects/pnode/examples (caidao22/update-examples)$ python3 test.py -log_view :0.txt
>>>> CUDA memory before PETSc 0.000GB
>>>> CUDA memory after PETSc 1.936GB
>>>> 
>>>> import torch
>>>> import sys
>>>> import os
>>>> 
>>>> import nvidia_smi
>>>> nvidia_smi.nvmlInit()
>>>> handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
>>>> info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
>>>> print('CUDA memory before PETSc %.3fGB' % (info.used/1e9))
>>>> 
>>>> petsc4py_path = os.path.join(os.environ['PETSC_DIR'],os.environ['PETSC_ARCH'],'lib')
>>>> sys.path.append(petsc4py_path)
>>>> import petsc4py
>>>> petsc4py.init(sys.argv)
>>>> handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
>>>> info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
>>>> print('CUDA memory after PETSc %.3fGB' % (info.used/1e9))
>>>> 
>>> 
>> 
> 

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