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The nodes with GPUs are relatively uncommon so that any job which asks for a GPU will wait significantly longer in most cases. | The nodes with GPUs are relatively uncommon so that any job which asks for a GPU will wait significantly longer in most cases. | ||
* Be sure that this GPU you had to wait so much longer to obtain is | * Be sure that this GPU you had to wait so much longer to obtain is <b>being used as efficiently as possible</b> and that it is really contributing to improved performance in your jobs. | ||
** A considerable amount of software does have a GPU option, for example such widely used packages as [[NAMD]] and [[GROMACS]], but only a small part of these programs' functionality has been modified to make use of GPUs. For this reason, it is wiser to | ** A considerable amount of software does have a GPU option, for example such widely used packages as [[NAMD]] and [[GROMACS]], but only a small part of these programs' functionality has been modified to make use of GPUs. For this reason, it is wiser to <b>first test a small sample calculation both with and without a GPU</b> to see what kind of speed-up you obtain from the use of this GPU. | ||
** Because of the high cost of GPU nodes, a job using | ** Because of the high cost of GPU nodes, a job using <b>a single GPU</b> should run significantly faster than if it was using a full CPU node. | ||
** If your job | ** If your job <b>only finishes 5% or 10% more quickly with a GPU, it's probably not worth</b> the effort of waiting to get a node with a GPU as it will be idle during much of your job's execution. | ||
* | * <b>Other tools for monitoring the efficiency</b> of your GPU-based jobs include <code>[https://developer.nvidia.com/nvidia-system-management-interface nvidia-smi]</code>, <code>nvtop</code> and, if you're using software based on [[TensorFlow]], the [[TensorFlow#TensorBoard|TensorBoard]] utility. |