We have 2 Lenovo ThinkSystem SR670 nodes containing Nvidia Tesla V100 cards for GPU jobs.
|SBG||Lenovo ThinkSystem SR670|
|Processor||2 x 16 Core Intel Xeon Gold 6142 (Skylake)|
|GPU||2 x NVIDIA Tesla V100 (up to maximum of 4)|
|GPU Memory||16GiB per GPU|
|CUDA Compute||7.0 (CUDA version 9 or greater required)|
Accessing the GPU nodes¶
Access is permitted to QMUL researchers upon request. Note that access to GPU nodes is not permitted for Undergraduate and MSc students. Please raise a support ticket by emailing firstname.lastname@example.org with a brief overview of intended use, so that we can verify that the jobs will use the GPUs correctly. More detail is on the using GPUs section.
Please ensure that you specify system (as opposed to GPU) RAM carefully when
requesting a "half-node job" (i.e. to make use of a single GPU).
Remember that CPU memory is per core, so if you are using 1 GPU and 16 CPU
-l h_vmem=11.5G, will request 184GB of system RAM.