From: SHIVAM TIWARI (t.shivam_at_iitg.ac.in)
Date: Sat Sep 10 2022 - 01:45:25 CDT
Maybe that is the reason. Thanks for the reply.
________________________________
From: Giacomo Fiorin <giacomo.fiorin_at_gmail.com>
Sent: Friday, September 9, 2022 9:47 PM
To: NAMD list <namd-l_at_ks.uiuc.edu>; SHIVAM TIWARI <t.shivam_at_iitg.ac.in>
Cc: Vermaas, Josh <vermaasj_at_msu.edu>
Subject: Re: namd-l: No performance improvement with NAMD 3.0 alpha8 over namd 2.14 cuda
>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<mailto: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
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________________________________
From: Vermaas, Josh <vermaasj_at_msu.edu<mailto:vermaasj_at_msu.edu>>
Sent: Friday, September 9, 2022 8:21:34 PM
To: namd-l_at_ks.uiuc.edu<mailto:namd-l_at_ks.uiuc.edu> <namd-l_at_ks.uiuc.edu<mailto:namd-l_at_ks.uiuc.edu>>; SHIVAM TIWARI <t.shivam_at_iitg.ac.in<mailto: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!6TL1RR4Bgl2I9k8oz5H0IqsptikrfACwjTvgnujXRctdX-2sKA8uDfza_FrCH5Rf9NH95gjGwSLcE4qOEaDJTQ$ <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$>
-Josh
From: <owner-namd-l_at_ks.uiuc.edu<mailto:owner-namd-l_at_ks.uiuc.edu>> on behalf of SHIVAM TIWARI <t.shivam_at_iitg.ac.in<mailto:t.shivam_at_iitg.ac.in>>
Reply-To: "namd-l_at_ks.uiuc.edu<mailto:namd-l_at_ks.uiuc.edu>" <namd-l_at_ks.uiuc.edu<mailto:namd-l_at_ks.uiuc.edu>>, SHIVAM TIWARI <t.shivam_at_iitg.ac.in<mailto:t.shivam_at_iitg.ac.in>>
Date: Friday, September 9, 2022 at 4:39 AM
To: namd-l <namd-l_at_ks.uiuc.edu<mailto:namd-l_at_ks.uiuc.edu>>
Subject: namd-l: No performance improvement with NAMD 3.0 alpha8 over namd 2.14 cuda
Dear all,
I am comparing the performances of namd 3.0 (alpha 8) and namd 2.14 on a system of 257k atoms with nvidia tesla k40 as my GPU. However, I don't see any performance improvement with namd 3.0 and both codes(3 & 2.14) are giving a benchmark of around 4 ns/day. How to get the best performance out of the available computational resources? Following is the SLURM script I am using for NAMD 3.0.
#!/bin/sh
#BATCH -A
#SBATCH --nodes 1
#SBATCH --ntasks-per-node=4
#SBATCH --account=project
#SBATCH --gres=gpu:1
#SBATCH --qos=project
#SBATCH --partition=high
#SBATCH --mem=10gb
#SBATCH --error=job.%J.err
#SBATCH --output=job.%J.out
#SBATCH --time=10-00:00:00
#SBATCH --no-requeue
module purge
#PATH=$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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