OpenMM: Difference between revisions

249 bytes removed ,  2 years ago
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module purge
module purge
module load StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3  
module load StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3  
module load python/3.8.10 openmm/7.7.0 netcdf/4.7.4 hdf5/1.10.6 mpi4py/3.0.3
module load python/3.8.10 openmm/7.7.0 netcdf/4.7.4 hdf5/1.10.6 mpi4py/3.0.3
source $HOME/env-parmed/bin/activate
source $HOME/env-parmed/bin/activate
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Here openmm_input.py is a python script loading amber files, creating the OpenMM simulation system, setting up the integration, and running dynamics. Example openmm_input.py is available [https://mdbench.ace-net.ca/mdbench/idbenchmark/?q=129 here].
Here openmm_input.py is a python script loading amber files, creating the OpenMM simulation system, setting up the integration, and running dynamics. Example openmm_input.py is available [https://mdbench.ace-net.ca/mdbench/idbenchmark/?q=129 here].


OpenMM on the CUDA platform requires only one CPU per GPU because it does not use CPUs for calculations. While OpenMM can use several GPUs in one node, the most efficient way to run simulations is to use a single GPU. As you can see from  [https://mdbench.ace-net.ca/mdbench/bform/?software_contains=OPENMM.cuda&software_id=&module_contains=&module_version=&site_contains=Narval&gpu_model=&cpu_model=&arch=&dataset=6n4o Narval benchmarks] and [https://mdbench.ace-net.ca/mdbench/bform/?software_contains=OPENMM.cuda&software_id=&module_contains=&module_version=&site_contains=Cedar&gpu_model=V100-SXM2&cpu_model=&arch=&dataset=6n4o  Cedar benchmarks], on nodes with NvLink (where GPUs are connected directly) OpenMM runs slightly faster on multiple GPUs. Without NvLink there is no advantage of using more than one V100 GPU ([https://mdbench.ace-net.ca/mdbench/bform/?software_contains=OPENMM.cuda&software_id=&module_contains=&module_version=&site_contains=Siku&gpu_model=&cpu_model=&arch=&dataset=6n4o Siku benchmarks] ) and very little speed up of simulations on P100 GPUs ([https://mdbench.ace-net.ca/mdbench/bform/?software_contains=OPENMM.cuda&software_id=&module_contains=&module_version=&site_contains=Cedar&gpu_model=P100-PCIE&cpu_model=&arch=&dataset=6n4o Cedar benchmarks])
OpenMM on the CUDA platform requires only one CPU per GPU because it does not use CPUs for calculations. While OpenMM can use several GPUs in one node, the most efficient way to run simulations is to use a single GPU. As you can see from  [https://mdbench.ace-net.ca/mdbench/bform/?software_contains=OPENMM.cuda&software_id=&module_contains=&module_version=&site_contains=Narval&gpu_model=&cpu_model=&arch=&dataset=6n4o Narval benchmarks] and [https://mdbench.ace-net.ca/mdbench/bform/?software_contains=OPENMM.cuda&software_id=&module_contains=&module_version=&site_contains=Cedar&gpu_model=V100-SXM2&cpu_model=&arch=&dataset=6n4o  Cedar benchmarks], on nodes with NvLink (where GPUs are connected directly) OpenMM runs slightly faster on multiple GPUs. Without NvLink there is a very little speedup of simulations on P100 GPUs ([https://mdbench.ace-net.ca/mdbench/bform/?software_contains=OPENMM.cuda&software_id=&module_contains=&module_version=&site_contains=Cedar&gpu_model=P100-PCIE&cpu_model=&arch=&dataset=6n4o Cedar benchmarks]).
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