Using the Data Machine

This page acts as a reference for using some of the features of the Data Machine. For more general information on what the Data Machine can offer, please see the Data Machine overview.

Table of Data Machine resources

Node	CPUs	Memory	Local NVME storage	GPU	GPU memory
acm-048 acm-049 acm-070 acm-071	128	2 TB	32 TB
nal-004 nal-005	128	512 GB	32 TB	4 NVIDIA A100 GPUs each	80 GB (per GPU)
nal-006 nal-007	128	512 GB	32 TB	4 NVIDIA A100 GPUs each split into 7 allocatable units	10 GB (per unit)

Two GPU nodes have four full A100 GPUs and two GPU nodes have four GPUs each split into seven units that can be requested. Each of these split units has 10 GB of memory. These units are requested similarly to normal GPUs. See the examples below.

Running code on the Data Machine

Though the Data Machine is not a buy-in node, the same procedures are used behind the scenes to run on Data Machine nodes. Therefore, users must be added to the data-machine buy-in account to run jobs on the Data Machine. To be added to this account please submit a request.

Note

The data-machine account is limited to Data Machine nodes only.

OnDemand Data Machine access

Each OnDemand app has an "Advanced Options" checkbox. This opens additional form entries. To use the Data Machine nodes, enter data-machine in the SLURM Account text box. Your job will queue onto a Data Machine node. Other resources (time, CPU and memory) can be requested as usual by filling out the "Number of hours", "Number of cores per task" and "Amount of memory" boxes.

GPU access

To use a single GPU unit with your OnDemand session, in the "Advanced Options" section and enter a100_slice under "Number of GPUs". If you would like multiple units, use a100_slice:n where n is the number of units you would like on a single node.

To request full GPUs, use a100 instead of a100_slice.

SLURM scripting Data Machine access

Below are some examples of SLURM resource requests for the Data Machine.

Partial Data Machine node

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --mem=256GB  # Set to your desired amount of memory
#SBATCH --cpus-per-task=32  # Set to your desired number of CPUs

Full Data Machine node with no GPU with large memory

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --mem=2TB  # Uses all memory on a large memory node
#SBATCH --cpus-per-task=128  # Uses all CPUs on a node

One GPU unit on a single node

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --mem=256GB  # Set to your desired amount of memory
#SBATCH --cpus-per-task=32  # Set to your desired number of CPUs
#SBATCH --gpus=a100_slice  # Request one GPU unit on the reserved node

Two GPU units on a single node

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --mem=256GB  # Set to your desired amount of memory
#SBATCH --cpus-per-task=32  # Set to your desired number of CPUs
#SBATCH --gpus=a100_slice:2  # Request two GPU units on the reserved node

One full GPU on a single node

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --mem=256GB  # Set to your desired amount of memory
#SBATCH --cpus-per-task=32  # Set to your desired number of CPUs
#SBATCH --gpus=a100  # Request one GPU on the reserved node

Two full GPUs on a single node

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --mem=256GB  # Set to your desired amount of memory
#SBATCH --cpus-per-task=32  # Set to your desired number of CPUs
#SBATCH --gpus=a100:2  # Request two GPUs on the reserved node

Using the fast NVME storage

You can preload your data into local NVME storage using "burst buffers". SLURM will move the data you want to use into NVME storage before your job starts.

Requesting a node

At the moment, burst buffers work best when requesting one specific node in the data machine. This ensures that the time SLURM takes to move your data does not count against the time you reserve the node for.

However, be careful which node you pick. If this node is busy, SLURM will wait until it is available to assign it to you. You can use the

1	`buyin_status --account data-machine`

to see the current usage of the Data Machine nodes.

Example burst buffer resource specification

In this example, we'll assume that we don't need a GPU and choose acm-048.

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodelist=acm-048  # Restrict to a specific data machine node
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --memory=256GB  # Set to your desired amount of memory
#SBATCH --cpus-per-task=128  # Set to your desired number of CPUs
#BB source=/mnt/home/<username>/important/data/here

Using the local data

SLURM sets an environment variable BB_DATA with the location of your data on the local NVME storage. Use this directory to access your data with less latency than the home, research, or scratch space where it originally came from.

Saving data written to local storage

Usually, if you edit data on local storage, your changes will be lost after the job ends. However, if you add the specification resync=true to the #BB line in your submission script, that data will be copied to the location it was originally taken from after the job ends.

Example:

#!/bin/bash
#SBATCH --account=data-machine  # Run under the data machine buy-in
#SBATCH --nodelist=acm-048  # Restrict to a specific data machine node
#SBATCH --nodes=1  # Reserve only one node
#SBATCH --time=4:00:00  # Reserve for four hours (or your desired amount of time)
#SBATCH --memory=256GB  # Set to your desired amount of memory
#SBATCH --cpus-per-task=128  # Set to your desired number of CPUs
#BB source=/mnt/home/<username>/important/data/here resync=true

Debugging burst buffer issues

To check on the status of your submitted jobs, use the command

1	`squeue --me`

The NODELIST(REASON) column of the output may give information relevant to burst buffer steps, e.g.,

   JOBID PARTITION     NAME     USER    STATE       TIME TIME_LIMI  NODES NODELIST(REASON)
24411390 data-mach data_mac grosscra  PENDING       0:00   1:00:00      1 (BurstBufferResources)
24411197 data-mach data_mac grosscra  PENDING       0:00   1:00:00      1 (burst_buffer/lua: slurm_bb_data_in: )

A job with the BurstBufferResources reason is waiting for a node to run on and begin transferring resources. In the example above, a job is running the slurm_bb_data_in, i.e., it is transferring the data to the node.

For more information or if there are problems with the burst buffer specification, use the command

scontrol show job <jobid>

For example, running scontrol show job 24411197 while the job above was transferring data, the output ends with

...
BurstBuffer=#BB source=/mnt/home/grosscra/scripts
BurstBufferState=staging-in
...

Often the Comment field in the scontrol show job <jobid> output can give helpful burst buffer information.