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Note
titleImportant: GCD vs GPU
Anchor
gcdgpu
gcdgpu

A MI250x GPU card has two GCDs. Previous generations of GPUs only had 1 GCD per GPU card, so these terms could be used interchangeably. The interchangeable usage continues even though now GPUs have more than one GCD. Slurm for instance only use the GPU terminology when referring to accelerator resources, so requests such as gpu-per-nodegres=gpu:number is equivalent to a request for a certain number of GCDs per node. On Setonix, the max number is 8. 


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  • 1 whole CPU chiplet (8 CPU cores)
  • ~32 GB memory (1/8 of the total available RAM)
  • 1 GCD (slurm GPU) directly connected to that chiplet
Note
titleIMPORTANT: Shared jobs may not receive optimal binding

For jobs that only use a partial set of resources of the node (non-exclusive jobs that share the rest of the node with other jobs), the current Setonix GPU configuration may not provide perfect allocation and binding, which may impact performance depending on the amount of CPU-GPU communication. This is under active investigation, and the recommendations included in this document will serve to achieve optimal allocations in most of the cases, but is not 100% guaranteed. Therefore, if you detect that imperfect binding or the use of shared nodes (even with optimal binding) is impacting the performance of your jobs, it is recommended to use exclusive nodes where possible, noticing that the project will still be charged for the whole node even if part of the resources remain idle. Also Please also report the observed issues to Pawsey's helpdesk.

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Excerpt

New way of request (#SBATCH) and use (srun) of resources for GPU nodes

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.

With a new CLI filter that Pawsey staff had put in place for the GPU nodes, the request of resources in GPU nodes should be thought as requesting a number of "allocation-packs". Each "allocation-pack" consists of:

  • 1 whole CPU chiplet (8 CPU cores)
  • a bit less of 32 GB memory (29.44 GB of memory, to be exact, allowing some memory for the system to operate the node) = 1/8 of the total available RAM
  • 1 GCD directly connected to that chiplet

For that, the request of resources only needs the number of nodes (–-nodes, -N) and the number of GPUs per node (--gpus-per-nodegres=gpu:number). The total number of requested GCDs (equivalent to slurm GPUs), resulting from the multiplication of these two parameters, will be interpreted as an indication of the total number of requeted "allocation-packs".

In the request of resources, users should not indicate any other Slurm allocation option related to memory or CPU cores, so don't use --ntasks, --cpus-per-task, --mem, etc. in the request headers of the script ( #SBATCH directives), or in the request options of given to salloc for interactive sessions. If, for some reason, the requirements for a job requirements are dictated indeed determined by the number of CPU cores or the amount of memory, then users should estimate the number of "allocation-packs" that meet 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.

The use/management of resources with srun is another story. After No default parameters should be assumed to be inherited from the allocation towards the srun. Therefore, after the requested resources are allocated, the srun command should be explicitly provided with enough parameters indicating how resources are to be used by the srun step and the spawned tasks. So the real management of resources at execution time is performed by the command line options of of srun. No default parameters should be considered for srun.

The following table provides some examples that will serve as a guide for requesting resources in the GPU nodes:

Warning
title--gpu-bind=closest may NOT work for all applications

Within the full explicit srun options for "managing resources", there are some that help to achieve optimal binding of GPUs to their directly connected chiplet on the CPU. There are now two methods to achieve this optimal binding of GPUs:

The use srun parameters for optimal binding

. So, together with the full explicit srun options, the following two methods can be used:

  1. Include these two Slurm parameters: --gpus-per-task=<number> together with --gpu-bind=closest
  2. "Manual" optimal binding with the use of "two auxiliary techniques".

The first method is simpler, but may not work for all codes. "Manual" binding may be the only useful method 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. An example of such a code is Slate. 


gpus-per-node=gpus-per-node=gpus-per-node=gpus-per-node=gpus-per-node=closest512 export MPICH_GPU_SUPPORT_ENABLED = 1 1c 8 --gpus-per-node=closest
Required Resources per JobNew "simplified" way of requesting resourcesTotal Allocated resourcesCharge per hour

The use of full explicit srun options is now required
(only the 1st method for optimal binding is listed here)

1 CPU task (single CPU thread) controlling 1 GCD (Slurm GPU)#SBATCH --nodes=1
#SBATCH --gpus-per-node=gres=gpu:1		1 allocation-pack =
1 GPU, 8 CPU cores (1 chiplet), 29.44 GB RAM		64 SU

*1

export OMP_NUM_THREADS=1
srun -N 1 -n 1 -c 8 --

gres=gpu:1 --gpus-per-task=1 <executable>

1 CPU task (with 14 CPU threads each) all threads controlling the same 1 GCD

#SBATCH --nodes=1
#SBATCH --gpus-per-node=gres=gpu:2

2 allocation-packs=
2 GPUs, 16 CPU cores (2 chiplets), 58.88 GB RAM		128 SU

*2

export OMP_NUM_THREADS=14
srun -N 1 -n 1 -c 16 --

gres=gpu:1 --gpus-per-task=1 <executable>

3 CPU tasks (single thread each), each controlling 1 GCD with GPU-aware MPI communication#SBATCH --nodes=1
#SBATCH --		gres=gpu:33 allocation-packs=
3 GPUs, 24 CPU cores (3 chiplets), 88.32 GB RAM		192 SU

*3

export MPICH_GPU_SUPPORT_ENABLED=1
export OMP_NUM_THREADS=1
srun -N 1 -n 3 -c 8 --

gres=gpu:3 --gpus-per-task=1 --gpu-bind=closest <executable>

2 CPU tasks (single thread each), each task controlling 2 GCDs with GPU-aware MPI communication

#SBATCH --nodes=1
#SBATCH --gpus-per-node=gres=gpu:4

4 allocation-packs=
4 GPU, 32 CPU cores (4 chiplets), 117.76 GB RAM		256 SU

*4

export MPICH_GPU_SUPPORT_ENABLED=1
export OMP_NUM_THREADS=1
srun -N 1 -n 2 -c 16 --

gres=gpu:4 --gpus-per-task=2 --gpu-bind=

closest <executable>

8

5 CPU tasks (

single thread each), each controlling 1 GCD with GPU-aware MPI communication

with 2 CPU threads each) all threads/tasks able to see all 5 GPUs

#SBATCH --nodes=1
#SBATCH --

exclusive8

gres=gpu:5

5 allocation-packs=
8 GPU5 GPUs, 64 40 CPU cores (8 5 chiplets), 235 147.2 GB RAM		320 SU

*5

export OMP_NUM_THREADS=

2
srun -N 1 -n 5 -c 8 -

-gres=gpu:5 <executable>

8 CPU tasks (single thread each), each controlling 1 GCD with GPU-aware MPI communication#SBATCH --nodes=1
#SBATCH --exclusive		8 allocation-packs=
8 GPU, 64 CPU cores (8 chiplets), 235 GB RAM		512 SU

*6

export MPICH_GPU_SUPPORT_ENABLED=1
export OMP_NUM_THREADS=1
srun -N 1 -n 8 -c 8 --gres=gpu:8 --gpus-per-task=1 --gpu-bind=

Notes for the request of resources:

Note that this simplified way of resource request

closest <executable>

8 CPU tasks (single thread each), each controlling 4 GCD with GPU-aware MPI communication#SBATCH --nodes=4
#SBATCH --exclusive		32 allocation-packs=
4 nodes, each with: 8 GPU, 64 CPU cores (8 chiplets), 235 GB RAM		2048 SU

*7

export MPICH_GPU_SUPPORT_ENABLED=1
export OMP_NUM_THREADS=1
srun -N 4 -n 8 -c 32 --gres=gpu:8 --gpus-per-task=4 --gpu-bind=closest <executable>






Notes for the request of resources:

  • Note that this simplified way of resource request is based on requesting a number of "allocation-packs".
  • 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:

  • IMPORTANT: The use of --gpu-bind=closest may NOT work 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.
  • 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 the OMP_NUM_THREADS environment variable.
  • (*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.
  • (*2) The required CPU threads per task is 14 but two full chiplets (-c 16) are indicated for each srun task and the number of threads is controlled with the OMP_NUM_THREADS variable.(*3) The settings explicitly "reserve" a whole chiplet (-c 8) for each srun task. This 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". 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.
  • (*5) Sometimes, the executable performs all the management of GPUs requested. If all the management logic is performed by the executable, then all the available resources should be exposed to it. In this case no options for optimal binding are given and only the number of gpus per node to be exposed to the job (--gres=gpu:number) is given.
  • (*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 by srun (-n 38).  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) Note the use of -c 16 to "reserve" 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 each chiplets.

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

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