module avail cuda
GPU jobs run in a dedicated queue which must be requested in the job submission. Each slot in this queue represents a GPU that the job will use. Therefore, users must ensure that every job’s GPU use matches its submission request. For a job using a single GPU, the submission should look like:
qsub -q gpu.q ...
Jobs requiring more than one GPU must be submitted like this:
qsub -q gpu.q -pe smp N ...
where N is the number of GPUs the job will use.
If your application requires MPI, you should still use the proper parallel environment regardless of how many GPUs you’ll be using:
qsub -q gpu.q -pe mpi_onehost N ... mpirun -np M --oversubscribe ...
where N is the number of GPUs your job will use and M is the number of MPI processes your job will launch. M does not have to equal N (see below).
The GPU nodes in Wynton HPC contain many different generations and models of NVIDIA GPUs. In order to ensure that your GPU jobs run on GPUs with the proper capabilities, there are two SGE resource complexes assigned to each GPU node:
compute_cap - describes the Compute Capability (or SM version) of the GPUs in the node (see NVIDIA’s CUDA GPU page for more details).
compute_cap is an integer in keeping with the relevant flags to
nvcc. For example, a Compute Capability of 6.1 (e.g. GeForce GTX 1080) is represented by
gpu_mem - describes how much GPU memory the GPUs in the node have. It’s defined in units of MiB.
Specifying either of these resources is not required. If you do specify one, your job will be scheduled on a GPU node with resources >= those that you requested. As an example, if you wanted to only run on at least GeForce GTX 1080 generation nodes with more than 10 GB of GPU memory, you would specify:
Several CUDA runtimes are installed on the GPU nodes. They can be loaded via modules just as above on the development nodes, e.g.
module load cuda and
module load cuda/7.5.
When your job is assigned to a node, it will also be assigned specific GPUs on that node. The GPU assignment will be contained in the environment variable
SGE_GPU as a comma-delimited set of one or more non-negative integers where then number of integers corresponds to the number of GPU cores requested. For example, a 3-core GPU job (
-q gpu.q -pe smp 3) may get assigned GPU cores
SGE_GPU=2,0,6 whereas a 1-core GPU job (
-q gpu.q) may get assigned GPU core
SGE_GPU=5. Be sure to send this GPU-core assignment to your application using the proper format for your application.
For example, if your application uses CUDA, you should limit which GPUs are used with:
Since we are using gpu.q slots to represent GPUs rather than the usual CPU cores, there is no way to ensure that a GPU node’s CPU cores don’t get oversubscribed. For this reason, please limit your CPU core usage to 4 CPU cores per GPU requested. This will prevent CPU core overloading on all the GPU node types.
While it is not possible to log directly into the GPU nodes to monitor their usage, several statistics are available from the login hosts. For example:
[alice@dev3 ~]$ qconf -se msg-iogpu3 hostname msg-iogpu3 load_scaling NONE complex_values mem_free=128000M load_values arch=lx-amd64,num_proc=32,mem_total=128739.226562M, \ swap_total=4095.996094M,virtual_total=132835.222656M, \ m_topology=SCTTCTTCTTCTTCTTCTTCTTCTTSCTTCTTCTTCTTCTTCTTCTTCTT, \ m_socket=2,m_core=16,m_thread=32,load_avg=5.020000, \ load_short=4.640000,load_medium=5.020000, \ load_long=5.110000,mem_free=124798.726562M, \ swap_free=4095.996094M,virtual_free=128894.722656M, \ mem_used=3940.500000M,swap_used=0.000000M, \ virtual_used=3940.500000M,cpu=17.700000, \ m_topology_inuse=SCTTCTTCTTCTTCTTCTTCTTCTTSCTTCTTCTTCTTCTTCTTCTTCTT, \ np_load_avg=0.156875,np_load_short=0.145000, \ np_load_medium=0.156875,np_load_long=0.159688, \ gpu.ncuda=2,gpu.ndev=2,gpu.cuda.0.mem_free=758054912, \ gpu.cuda.0.procs=1,gpu.cuda.0.clock=2025, \ gpu.cuda.0.util=57,gpu.cuda.1.mem_free=758054912, \ gpu.cuda.1.procs=1,gpu.cuda.1.clock=2025, \ gpu.cuda.1.util=54,gpu.names=GeForce GTX 1080;GeForce \ GTX 1080; processors 32 user_lists NONE xuser_lists NONE projects NONE xprojects NONE usage_scaling NONE report_variables NONE
The above shows that host
msg-iogpu3 has 2 GeForce GTX 1080 GPUs. Each GPU is running one process, each is just over 50% utilized, and each has approximately 722 MiB (758,054,912 bytes) of free memory.