Scaling Study

Overview

Questions

• How can I decide the amount of resources I should request for my job?
• How do I know how my application behaves at different scales?

Objectives

After completing this episode, participants should be able to …

• Perform a simple scaling study for a given application.
• Identify good working points for the job configuration.

What do we look at?

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• Amdahl’s vs. Gustavsons’s law / strong and weak scaling
• Walltime, Speedup, efficiency

Discussion: What dimensions can we look at?

Show me the solution

• CPUs
• Nodes
• Workload/problem size

• Define example payload
  • Long enough to be significant
  • Short enough to be feasible for a quick study
• Identify dimension for scaling study, e.g.
  • number of processes (on a single node)
  • number of processes (across nodes)
  • number of nodes involved (network-communication boundary)
  • size of workload
  • Decide on number of processes across node, fixed workload size
• Choose limits (e.g. 1, 2, 4, … cores), within reasonable size for given Cluster
• Beyond nodes? Set to one node?

Parameter Scan

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• Take measurements
  • Use time and repeating measurements (something like 3 or 10)
  • Vary scaling parameter

Exercise: Run the Example with different -n

• 1, 2, 4, 8, 16, 32, … cores and same workload
• Take time measurements (ideally multiple and with --exclusive)

Analyzing results

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Exercise: Plot the scaling

• Plot it against time
• Calculate speedup with respect to baseline with 1 core

• What’s a good working point? How
• Overhead
• Efficiency: not wasting cores if adding them doesn’t do much

Summary

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What’s a good working point for our example (at a given workload)?

Key Points

• Jobs behave differently with varying resources and workloads
• Scaling study is necessary to proof a certain behavior of the application
• Good working points defined by sections where more cores still provide sufficient speedup, but no costs due to overhead etc. occurs