There’s a new GPU node in the gpu partition!

It’s notable for a list of reasons. This is the first node on Sherlock to feature both:

1. the latest generation of Intel CPUs (Skylake),
2. the latest generation of computing-optimized NVIDIA GPUs,

and it’s also the first node on Sherlock with 32GB GPUs, which is particularly interesting for a lot of Deep-Learning oriented workloads.

Specs

This compute node features:

• 24x Intel Skylake CPU cores (2x Xeon 5118, 2.30GHz),
• 192GB of memory (RAM),
• 4x NVIDIA Tesla V100 GPUs with 32GB of GPU memory each.

Details

Nodes in the gpu partition are now available to everyone on Sherlock, and the new node can be requested by adding the following flag to your job submission options: -C "GPU_SKU:V100_SXM2&GPU_MEM:32GB"

To request an interactive session on a Tesla V100 GPU with 32GB of memory, you can run:

$ srun -p gpu --gres gpu:1 -C "GPU_SKU:V100_SXM2&GPU_MEM:32GB" --pty bash

To see the list of all the available GPU features and characteristics that can be requested in the gpu partition:

$ sh_node_feat -p gpu | grep GPU GPU_BRD:GEFORCE GPU_BRD:TESLA GPU_CC:3.5 GPU_CC:3.7 GPU_CC:5.2 GPU_CC:6.0 GPU_CC:6.1 GPU_CC:7.0 GPU_GEN:KPL GPU_GEN:MXW GPU_GEN:PSC GPU_GEN:VLT GPU_MEM:12GB GPU_MEM:16GB GPU_MEM:24GB GPU_MEM:32GB GPU_MEM:6GB GPU_SKU:K20X GPU_SKU:K80 GPU_SKU:P100_PCIE GPU_SKU:P100_SXM2 GPU_SKU:P40 GPU_SKU:TITAN_BLACK GPU_SKU:TITAN_X GPU_SKU:TITAN_Xp GPU_SKU:V100_SXM2 

For more details about GPUs on Sherlock, see the GPU user guide.

If you have any question, feel free to send us a note at srcc-support@stanford.edu.