Hello,
I wanted to update my report below to mention that the issue with pbsa binaries breaking upon enabling the LibTorch & cudnn libraries still persists in Amber24 with AmberTools 24. I compiled with CUDA 10.2, gcc 8.5, OpenMPI 4.1.5, python 3.10, and cmake 3.25.2 on a Red Hat Enterprise Linux release 8.8 (Ootpa) system. I am not aware of any workaround or resolution for this issue.
Best,
Mayukh Chakrabarti (he/him)
COMPUTATIONAL SCIENTIST
From: Chakrabarti, Mayukh (NIH/NCI) [C] <mayukh.chakrabarti.nih.gov>
Date: Tuesday, October 31, 2023 at 11:28 AM
To: amber.ambermd.org <amber.ambermd.org>
Subject: Amber22/AmberTools23: Enabling of libtorch & cudnn libraries breaks pbsa binaries
Hello,
I am encountering a problem in which enabling LibTorch libraries with Amber22 and AmberTools23 causes segmentation fault errors (SIGSEGV) in the pbsa binaries upon running the serial tests (make test.serial). I have successfully compiled a version of Amber22/AmberTools23 in which this library is not enabled, and none of the pbsa tests in AmberTools break (i.e., no segmentation fault errors).
Further details:
I am running my compilation with CUDA 10.2, gcc 8.5, OpenMPI 4.1.5, python 3.6, and cmake 3.25.1 on a Red Hat Enterprise Linux release 8.8 (Ootpa) system. As per the manual, I have tried both “Built-in” mode and “User-installed” mode, both resulting in the same errors. For the “User-installed” mode, I manually downloaded and extracted “libtorch-shared-with-deps-1.9.1+cu102.zip” from the PyTorch website to correspond to CUDA 10.2, and “cudnn-linux-x86_64-8.7.0.84_cuda10-archive.tar.xz” directly from the NVIDIA website, and specified the following variables to CMAKE:
-DLIBTORCH=ON \
-DTORCH_HOME=/path_to_libtorch \
-DCUDNN=TRUE \
-DCAFFE2_USE_CUDNN=1 \
-DCUDNN_INCLUDE_PATH=/path_to_cudnn_include \
-DCUDNN_LIBRARY_PATH=/path_to_libcudnn.so \
Building and compiling proceed without issue. However, when running the serial tests, I get errors akin to the following (example shown after sourcing amber.sh and running AmberTools pbsa_ligand test):
Program received signal SIGSEGV: Segmentation fault - invalid memory reference.
Backtrace for this error:
#0 0x7f480cb65171 in ???
#1 0x7f480cb64313 in ???
#2 0x7f480c221c0f in ???
#3 0x7f4869667219 in ???
#4 0x7f480c224856 in ???
#5 0x7f4869648722 in ???
./Run.t4bnz.min: line 34: 2436022 Segmentation fault (core dumped) $DO_PARALLEL $TESTpbsa -O -i min.in -o $output < /dev/null
./Run.t4bnz.min: Program error
When running the exact same test with the version of Amber22 not containing the LibTorch libraries:
diffing mdout.lig.min.save with mdout.lig.min
PASSED
==============================================================
I have run ‘ldd’ on the pbsa binary to try to identify any libraries that may be missing, but there don’t appear to be any missing libraries. Could anybody please provide any insight into how to fix this issue?
Best,
Mayukh Chakrabarti
COMPUTATIONAL SCIENTIST
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Received on Tue May 07 2024 - 09:00:01 PDT
