SLURM scheduler version has been updated to SLURM 22.05.8 and a new CLI filter has been installed on the GPU nodes in order to provide optimal binding of GPUs.

Slurm use for CPU-only nodes

The request and use of resources for the CPU-only nodes has not changed, so users may keep using their already working Slurm batch scripts for submitting jobs.

The only recommendation that we may raise at this point is that Slurm has announced the "separation" of the request of resources and the srun launcher. This implies that, in future versions of Slurm, srun will not inherit the exact parameters as requested for the allocation. Since previous versions of Slurm, this has already happened to the --cpus-per-task (or -c) option, which needs to be explicitly set in each srun command, independently of its setting during the request for resources. Therefore, we recommend users to be aware of the upcoming changes on Slurm and adopt, as a best practice, the explicit set all the parameters of srun that indicate the resources to be used in the command, rather than assuming that these parameters are inherited correctly by default. (Indeed, this practice has now become a requirement for the use of the GPU nodes in Setonix, as you can read in the following section.)

Slurm use for GPU nodes

Pawsey's way for requesting resources on GPU nodes (different to standard Slurm)

The request of resources for the GPU nodes has changed dramatically. The main reason for this change has to do with Pawsey's efforts to provide a method for optimal binding of the GPUs to the CPU cores in direct physical connection for each task. For this, we decided to completely separate the options used for resource request via salloc or (#SBATCH pragmas) and the options for the use of resources during execution of the code via srun.

Furthermore, in the request of resources, users should not indicate any other Slurm allocation option related to memory or CPU cores. Therefore, users should not use --ntasks, --cpus-per-task, --mem, etc. in the request headers of the script ( #SBATCH directives), or in the request options given to salloc for interactive sessions. If, for some reason, the requirements for a job are indeed determined by the number of CPU cores or the amount of memory, then users should estimate the number of "allocation-packs" that cover their needs. The "allocation-pack" is the minimal unit of resources that can be managed, so that all allocation requests should be indeed multiples of this basic unit.

Pawsey also has some site specific recommendations for the use/management of resources with srun command. Users should explicitly provide a list of several parameters for the use of resources by srun. (The list of these parameters is made clear in the examples below.) Users should not assume that srun will inherit any of these parameters from the allocation request. Therefore, the real management of resources at execution time is performed by the command line options provided to srun. Note that, for the case of srun, options do have the standard Slurm meaning. 

The following table provides some examples that will serve as a guide for requesting resources in the GPU nodes. Most of the examples in the table provide are for typical jobs where multiple GPUs are allocated to the job as a whole but each of the tasks spawned by srun is binded and has direct access to only 1 GPU. For applications that require multiple GPUs per task, there 3 examples (^*4, ^*5 & ^*7) where tasks are binded to multiple GPUs:

Notes for the request of resources:

• Note that this simplified way of resource request is based on requesting a number of "allocation-packs", so that standard use of Slurm parameters for allocation should not be used for GPU resources.
• The --nodes (-N) option indicates the number of nodes requested to be allocated.
• The --gres=gpu:number option indicates the number of allocation-packs requested to be allocated per node. (The "equivalent" option --gpus-per-node=number is not recommended as we have found some bugs with its use.)
• The --exclusive option requests all the resources from the number of requested nodes. When this option is used, there is no need for the use of --gres=gpu:number during allocation and, indeed, its use is not recommended in this case.
• Users should not include any other Slurm allocation option that may indicate some "calculation" of required memory or CPU cores. The management of resources should only be performed after allocation via srun options.
• The same simplified resource request should be used for the request of interactive sessions with salloc.
• IMPORTANT: In addition to the request parameters shown in the table, users should indeed use other Slurm request parameters related to partition, walltime, job naming, output, email, etc. (Check the examples of the full Slurm batch scripts.)

Notes for the use/management of resources with srun:

• Note that, for the case of srun, options do have the standard Slurm meaning.
• The following options need to be explicitly provided to srun and not assumed to be inherited with some default value from the allocation request:
  • The --nodes (-N) option indicates the number of nodes to be used by the srun step.
  • The --ntasks (-n) option indicates the total number of tasks to be spawned by the srun step. By default, tasks are spawned evenly across the number of allocated nodes.
  • The --cpus-per-task (-c) option should be set to multiples of 8 (whole chiplets) to guarantee that srun will distribute the resources in "allocation-packs" and then "reserving" whole chiplets per srun task, even if the real number is 1 thread per task. The real number of threads is controlled with the OMP_NUM_THREADS environment variable.
  • The --gres=gpu:number option indicates the number of GPUs per node to be used by the srun step. (The "equivalent" option --gpus-per-node=number is not recommended as we have found some bugs with its use.)
  • The --gpus-per-task option indicates the number of GPUs to be binded to each task spawned by the srun step via the -n option. Note that this option neglects sharing of the assigned GPUs to a task with other tasks. (See cases ^*4, ^*5 and ^*7 and their notes for non-intuitive cases.)
• And for optimal binding, the following should be used:
  • The --gpu-bind=closest indicates that the chosen GPUs to be binded to each task should be the optimal (physically closest) to the chiplet assigned to each task.
  • IMPORTANT: The use of --gpu-bind=closest will assign optimal binding but may still NOT work and launch execution errors for codes relying OpenMP or OpenACC pragma's for moving data from/to host to/from GPU and attempting to use GPU-to-GPU enabled MPI communication. For those cases, the use of the "manual" optimal binding (method 2) is required. Method 2 is explained later in the main document.

• (^*1) This is the only case where srun may work fine with default inherited option values. Nevertheless, it is a good practice to always use full explicit options of srun to indicate the resources needed for the executable. In this case, the settings explicitly "reserve" a whole chiplet (-c 8) for the srun task and control the real number of threads with the OMP_NUM_THREADS environment variable. Although the use of gres=gpu, gpus-per-task & gpu-bind is reduntant in this case, we keep them for encouraging their use, which is strictly needed in the most of cases (except case ^*5).
• (^*2) The required CPU threads per task is 14 and that is controlled with the OMP_NUM_THREADS environment variable. But still the two full chiplets (-c 16) are indicated for each srun task.
• (^*3) The settings explicitly "reserve" a whole chiplet (-c 8) for each srun task. This provides "one-chiplet-long" separation among each of the CPU cores to be allocated for the tasks spawned by srun (-n 3).  The real number of threads is controlled with the OMP_NUM_THREADS variable. The requirement of optimal binding of GPU to corresponding chiplet is indicated with the option --gpu-bind=closest. And, in order to allow GPU-aware MPI communication, the environment variable MPICH_GPU_SUPPORT_ENABLED is set to 1.
• (^*4) Each task needs to be in direct communication with 2 GCDs. For that, each of the CPU task reserve "two-full-chiplets". IMPORTANT: The use of -c 16 "reserves" a "two-chiplets-long" separation among the two CPU cores that are to be used (one for each of the srun tasks, -n 2 ). In this way, each task will be in direct communication to the two logical GPUs in the MI250X card that has optimal connection to the chiplets reserved for each task. The real number of threads is controlled with the OMP_NUM_THREADS variable. The requirement of optimal binding of GPU to corresponding chiplet is indicated with the option --gpu-bind=closest. And, in order to allow GPU-aware MPI communication, the environment variable MPICH_GPU_SUPPORT_ENABLED is set to 1.
• (^*5) Sometimes, the executable (and not the scheduler) performs all the management of GPUs, like in the case of Tensorflow distributed training, and other Machine Learning Applications. If all the management logic for the GPUs is performed by the executable, then all the available resources should be exposed to it. IMPORTANT: In this case, the --gpu-bind option should not be provided. Neither the --gpus-per-task option should be provided, as all the available GPUs are to be available to all tasks. The real number of threads is controlled with the OMP_NUM_THREADS variable. And, in order to allow GPU-aware MPI communication, the environment variable  MPICH_GPU_SUPPORT_ENABLED  is set to 1. These last two settings may not be necessary for aplications like Tensorflow.
• (^*6) All GPUs in the node are requested, which mean all the resources available in the node via the --exclusive allocation option (there is no need to indicate the number of GPUs per node when using exclusive allocation). The use of -c 8 provides "one-chiplet-long" separation among each of the CPU cores to be allocated for the tasks spawned by srun (-n 8).  The real number of threads is controlled with the OMP_NUM_THREADS variable. The requirement of optimal binding of GPU to corresponding chiplet is indicated with the option --gpu-bind=closest. And, in order to allow GPU-aware MPI communication, the environment variable MPICH_GPU_SUPPORT_ENABLED is set to 1.
• (^*7) All resources in each node are requested via the --exclusive allocation option (there is no need to indicate the number of GPUs per node when using exclusive allocation). Each task needs to be in direct communication with 4 GCDs. For that, each of the CPU task reserve "four-full-chiplets". IMPORTANT: The use of -c 32 "reserves" a "four-chiplets-long" separation among the two CPU cores that are to be used per node (8 srun tasks in total, -n 8 ). The real number of threads is controlled with the OMP_NUM_THREADS variable. The requirement of optimal binding of GPU to corresponding chiplet is indicated with the option --gpu-bind=closest. In this way, each task will be in direct communication to the closest four logical GPUs in the node with respect to the chiplets reserved for each task. And, in order to allow GPU-aware MPI communication, the environment variable  MPICH_GPU_SUPPORT_ENABLED  is set to 1. The --gres=gpu:8 option assigns 8 GPUs per node to the srun step (32 GPUs in total as 4 nodes are being assigned).
• (*8) All GPUs in the node are requested using the --exclusive option, but only 1 CPU chiplet - 1 GPU "unit" (or allocation-pack) is used in the srun step.

General notes:

• The allocation charge is for the total of allocated resources and not for the ones that are explicitly used in the execution, so all idle resources will also be charged

An extensive explanation on the use of the GPU nodes (including these updates and the "manual" binding) is in Example Slurm Batch Scripts for Setonix on GPU Compute Nodes.