Enable timing minibatch#66
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PatrickRMiles
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I think this can go in as-is, but I would suggest one of two things:
- Measure the perf impact of the cuda synchronize in a few scenarios, or
- Make the minibatch timing toggleable in the config file, so if we find down the line that this synchronize is noticeably hampering performance, we can disable it temporarily while we work on a better solution.
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| if time_minibatch: | ||
| # This sync has some potential performance impact | ||
| # TODO: Would be better to measure this with Caliper, which uses CUDA events. | ||
| torch.cuda.synchronize(self.device) | ||
| minibatch_time_s = ( | ||
| time.perf_counter() - minibatch_start_time | ||
| ) |
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Have you measured the perf impacts of this sync?
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For the FOM, I'm changing the timer to CUDA events in #71, which do not need the synchronize, and I am aggregating over all ranks there. Aggregation happens after the epoch such that no perf impact.
So #71 will be superseding these exact issues.
FWIW the performance impact in this PR is negligible even with the sync.
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Time minibatch to evaluate weak scaling, as the number of samples processed per rank is constant when increasing ranks. The current timers for epoch time and total time don't encapsulate this.