Thursday, June 5, 2014

Flux Adds GPU-Attached Nodes

We're happy to announce the availability of the Flux GPU machines (fluxg). Currently, there are 5 16-core machines each with 8 NVIDIA K20X GPUs. The Flux GPU allocations are only different in that the atomic unit for an allocation includes 2 processors, 8GB of memory, and 1 GPU.  For more information, see the ARC website.

Purchasing an allocation for fluxg is like any other - send a ticket to requesting how many GPUs you would like.   The costs per GPU can be found on the same ARC webpage.

Assuming you have access to a Flux GPU Account, you would access it with (using the LSA GPU Flux account as an example):

#PBS -A lsa_fluxg
#PBS -l qos=flux
#PBS -q fluxg
To get started, here are some links for accessing the CUDA libraries with Modulesusing GPUs,  NVIDIA's CUDA page, using Matlab with GPUs,  or the Theano Python library.

As always, email Flux Support with any questions.

One HP SL270 with 8 Nvidia K20X Cards