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Hi all,</div>
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I’m running a Gamma-point-only SCF calculation for a nanoparticle system using Quantum ESPRESSO with GPU support. The HPC node has 4 NVIDIA GH200 GPUs, and I observe very poor scaling behavior when increasing the number of GPUs.</div>
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<b>Setup</b></div>
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I use 1 MPI task per GPU, with 72 OpenMP threads per task:</div>
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<div style="margin-top: 1em; margin-bottom: 1em;"><code>OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK</code></div>
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<div style="margin-top: 1em; margin-bottom: 1em;">QE version: 7.4 (GPU build with OpenMP)</div>
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<div style="margin-top: 1em; margin-bottom: 1em;">MPI: OpenMPI (GPU-aware)</div>
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Here is the relevant SLURM configuration:</div>
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#SBATCH --nodes=1</div>
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#SBATCH --ntasks-per-node=4</div>
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#SBATCH --cpus-per-task=72</div>
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#SBATCH --gpus=4</div>
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#SBATCH --gpu-bind=map_gpu:0,1,2,3</div>
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#SBATCH --time=00:30:00</div>
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module use Spack</div>
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module load nvhpc/24.11 openmpi/main-7zgw-GH200-gpu quantum-espresso/7.4-gpu-omp</div>
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srun pw.x -pd .true. -npool 1 -in aiida.in > aiida.out</div>
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<b>Performance Results (time per SCF iteration)</b></div>
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Configuration</div>
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Time (sec)</div>
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1 task, 1 GPU</div>
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11.9</div>
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2 tasks, 2 GPUs</div>
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17.0</div>
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3 tasks, 3 GPUs</div>
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15.3</div>
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4 tasks, 4 GPUs</div>
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9.7</div>
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As you can see, the best performance is with just 1 task + 1 GPU. Increasing the number of GPUs initially
<i>worsens</i> the performance, and only with 4 GPUs does it slightly improve.</div>
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>From the output:</div>
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GPU acceleration is ACTIVE. 1 visible GPUs per MPI rank</div>
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GPU-aware MPI enabled</div>
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Message from routine print_cuda_info:</div>
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High GPU oversubscription detected. Are you sure this is what you want?</div>
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I tried <code>--gpu-bind=map_gpu:0,1,2,3</code> to explicitly bind GPUs to ranks, but the warning still appears, and performance doesn’t change. I’ve also experimented with
<code>-nb</code> and <code>-ndiag</code> parameters, but they either don’t help or make things worse.</div>
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<b>Question:</b><br>
Does anyone have experience optimizing Gamma-point-only calculations on multiple GPUs? Is there a known bottleneck or best practice for using multiple GPUs efficiently in such a case?</div>
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Any insights would be greatly appreciated.</div>
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Best,</div>
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Xing</div>
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