Research map / Optimization / Distributed and compressed
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Asynchronous distributed averaging with repeated local proximal-gradient steps (DAve-RPG) versus synchronous proximal gradient and PIAG | distributed regularized logistic regression · RCV1 and URL (wall clock) | suboptimality vs wall clock time | Helped |
| Increasing local inner proximal-gradient iterations p from 1 to 7 (ablation) | DAve-RPG · URL, 100 workers | time to reach minimum | Helped |
| Increasing local inner iterations to p=10 (ablation) | DAve-RPG · URL, 100 workers | convergence speed | Hurt |
| Adding large simulated random machine delays and crashes (delays up to about 300 iterations) (ablation) | DAve-RPG · RCV1, 10 machines | suboptimality | No clear effect |
| Scaling number of workers (speedup measured as inverse time to suboptimality 1e-2) (ablation) | DAve-RPG with p=50 · RCV1, up to 100 machines | speedup | Mixed |
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