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@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@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))
