From: Giacomo Fiorin (giacomo.fiorin_at_gmail.com)
Date: Fri Sep 09 2022 - 11:17:23 CDT
>From the webpage:
https://www.ks.uiuc.edu/Research/namd/alpha/3.0alpha/
*This scheme is intended for modern GPUs, and it might slow your simulation
down if you are not running on a Volta, Turing, or Ampere GPU! If your GPU
is older, we recommend that you stick to NAMD 2.x. *
On Fri, Sep 9, 2022 at 11:50 AM SHIVAM TIWARI <t.shivam_at_iitg.ac.in> wrote:
> Hi Josh,
>
> Thanks for your reply, yes, I am using "CUDASOAintegrate on" in my
> configuration file.
>
> regards
> Shivam
>
> Get Outlook for Android
> <https://urldefense.com/v3/__https://aka.ms/AAb9ysg__;!!DZ3fjg!6bejqz4cpnYiAqX0WhDH1tjFAb2_NFe85s2s02Ldw7_mgUjsMdKJmz7QnQv_eH7aPcyIXoJvlB4iSTG_cBlyRw$>
> ------------------------------
> *From:* Vermaas, Josh <vermaasj_at_msu.edu>
> *Sent:* Friday, September 9, 2022 8:21:34 PM
> *To:* namd-l_at_ks.uiuc.edu <namd-l_at_ks.uiuc.edu>; SHIVAM TIWARI <
> t.shivam_at_iitg.ac.in>
> *Subject:* Re: namd-l: No performance improvement with NAMD 3.0 alpha8
> over namd 2.14 cuda
>
>
> Are you using the CUDASOAIntegrate codepath? Otherwise, NAMD 3 will fall
> back to the NAMD 2 code path, which is why the performance is identical.
> See the NVIDIA blogpost about what you need to change in your configuration
> files.
> https://urldefense.com/v3/__https://developer.nvidia.com/blog/delivering-up-to-9x-throughput-with-namd-v3-and-a100-gpu/__;!!DZ3fjg!4sKkwSUhTjol1CPwQJfrjCwxj0Ac4FOWEYqw5K1hcGoVEUiOKw2aoRgeTfrSmoQOsSDHhwoWDkpAU-Ims1LbarE0iQ$
> <https://urldefense.com/v3/__https://developer.nvidia.com/blog/delivering-up-to-9x-throughput-with-namd-v3-and-a100-gpu/__;!!DZ3fjg!6bejqz4cpnYiAqX0WhDH1tjFAb2_NFe85s2s02Ldw7_mgUjsMdKJmz7QnQv_eH7aPcyIXoJvlB4iSTERv4zlOA$>
>
>
>
> -Josh
>
>
>
> *From: *<owner-namd-l_at_ks.uiuc.edu> on behalf of SHIVAM TIWARI <
> t.shivam_at_iitg.ac.in>
> *Reply-To: *"namd-l_at_ks.uiuc.edu" <namd-l_at_ks.uiuc.edu>, SHIVAM TIWARI <
> t.shivam_at_iitg.ac.in>
> *Date: *Friday, September 9, 2022 at 4:39 AM
> *To: *namd-l <namd-l_at_ks.uiuc.edu>
> *Subject: *namd-l: No performance improvement with NAMD 3.0 alpha8 over
> namd 2.14 cuda
>
>
>
> Dear all,
>
>
>
> I am comparing the performances of namd 3.0 (alpha 8) and namd 2.14 on a
> system of 257k atoms with nvidia tesla k40 as my GPU. However, I don't see
> any performance improvement with namd 3.0 and both codes(3 & 2.14) are
> giving a benchmark of around 4 ns/day. How to get the best performance out
> of the available computational resources? Following is the SLURM script I
> am using for NAMD 3.0.
>
>
>
> #!/bin/sh
>
> #BATCH -A
>
> #SBATCH --nodes 1
>
> #SBATCH --ntasks-per-node=4
>
> #SBATCH --account=project
>
> #SBATCH --gres=gpu:1
>
> #SBATCH --qos=project
>
> #SBATCH --partition=high
>
> #SBATCH --mem=10gb
>
> #SBATCH --error=job.%J.err
>
> #SBATCH --output=job.%J.out
>
> #SBATCH --time=10-00:00:00
>
> #SBATCH --no-requeue
>
> module purge
>
> #PATH=$PATH:/apps/precompiled/namd/NAMD_2.14_Linux-x86_64-multicore-CUDA
>
>
> PATH=$PATH:/scratch/proj/nsmswamireddy/NAMD_3.0alpha8_Linux-x86_64-multicore-CUDA
>
>
>
> ##charmexec=../NAMD_3.0alpha12_Linux-x86_64-multicore-CUDA/charmrun
>
> ##namdexec=../NAMD_3.0alpha12_Linux-x86_64-multicore-CUDA/namd3
>
>
>
> charmrun namd3 +p $SLURM_NTASKS +setcpuaffinity +idlepoll prod.conf >
> pasr21.log
>
>
>
> regards
>
> shivam
>
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