Performance models

Performance models (i.e., human-readable mathematical functions) are a crucial feature of ElastiSim, enabling elastic workloads. All tasks (see Task types) implicitly support performance models to specify either the load simulated on the platform or the number of iterations.

The simulation engine evaluates performance models on each (re)configuration to a single number. In combination with variables representing the number of assigned resources, performance models are a powerful feature to describe adaptive workloads. ElastiSim supports the following variables in performance models for tasks:

Variable	Description
num_nodes	The number of assigned compute nodes
num_gpus_per_node	The number of assigned GPUs per compute node
num_gpus	The total number of assigned GPUs (syntactic sugar for num_nodes * num_gpus_per_node)
num_nodes_min	Requested number of nodes (minimum, only available for evolving requests)
num_nodes_max	Requested number of nodes (maximum, only available for evolving requests)
phase_iteration	Iteration number of the phase (starting at 0, only available for evolving requests)

Phases specify evolving requests before they start. The variable phase_iteration starts with the value 0 (e.g., for a phase with five iterations, phase_iteration will evaluate to the values [0,4]). In phases that are not repetitively executed (i.e., iterations is unspecified or 1), phase_iteration will evaluate to 0.

Job arguments

Arguments specified for a job (see Job) are automatically valid variables in all performance models. As multiple jobs can use the same application model, arguments can enable different workloads without modeling a new application. Furthermore, arguments also allow phases to use performance models in their number of iterations.

Phases do not support variables representing the number of assigned resources in performance models, as this would break malleability based on scheduling points between phases (and phase iterations).

Example

The following example shows two jobs specifying different arguments to adjust the simulated load. The computational load per node (note the uniform distribution pattern) in the cpu task depends on the actively configured number of nodes.

Jobs

{
  "jobs": [
    {
      "type": "malleable",
      "submit_time": 120,
      "num_nodes_min": 16,
      "num_nodes_max": 32,
      "application_model": "/path/to/application_model.json",
      "arguments": {
        "phase_i": 10,
        "seq_i": 25,
        "comp": 8e12,
        "checkpoint_size": 7e11
      }
    },
    {
      "type": "malleable",
      "submit_time": 360,
      "num_nodes_min": 12,
      "num_nodes_max": 24,
      "application_model": "/path/to/application_model.json",
      "arguments": {
        "phase_i": 15,
        "seq_i": 40,
        "comp": 6e12,
        "checkpoint_size": 9e11
      }
    }
  ]
}

Application model

{
  "phases": [
    {
      "iterations": "phase_i",
      "tasks": [
        {
          "type": "pfs_read",
          "name": "PFS write",
          "bytes": "model_size",
          "pattern": "uniform"
        },
        {
          "type": "sequence",
          "iterations": "seq_i",
          "tasks": [
            {
              "type": "cpu",
              "flops": "comp/num_nodes^0.8",
              "computation_pattern": "uniform"
            },
            {
              "type": "pfs_write",
              "name": "PFS write",
              "bytes": "checkpoint_size",
              "pattern": "uniform"
            }
          ]
        }
      ]
    }
  ]
}