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ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
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Proposes ATA, a lower-confidence-bound bandit method that adaptively allocates a fixed per-iteration task budget across heterogeneous stochastic workers, avoiding the wasteful greedy allocation used by Rennala/Ringmaster SGD, with ATA-Empirical as a data-dependent variant.
Problem
Greedy task allocation in distributed/asynchronous ML wastes computation when worker times are random and heterogeneous, since it keeps all workers busy and discards surplus tasks; the goal is to minimize completion time under a strict budget of B tasks per round without knowing time distributions.
Approach
Casts task allocation as a combinatorial bandit with a non-linear max-type proxy loss, uses lower confidence bounds on worker mean times (sub-exponential concentration) and solves the resulting allocation exactly with a recursive routine (RAS); a variant replaces the uniform Orlicz bound with per-arm empirical scale via a new concentration lemma.
Key results
- Total worker time ratio of GTA to ATA grows from 1.3 at n=17 to 12.45 at n=459 (ATA-Empirical 14.1 at n=459), showing GTA wastes increasing resources.
- ATA's theoretical guarantee: expected total computation time at most (1+4 eta ln B) times the optimal plus O(ln K), with eta = max alpha_i/mu_i.
- Regret of ATA grows logarithmically in K, and ATA/ATA-Empirical converge to the oracle OFTA allocation performance up to a constant.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ATA instead of greedy GTA allocation | SGD task allocation · SGD worker-time simulation, n=17 to 459 | total worker time ratio (GTA / method): 1.3; 2.91; 7.22; 12.45 | Helped |
| Use ATA-E instead of ATA | ATA task allocation · Linear worker-time simulation, n=17, 51, 153 | total worker time ratio (GTA / method): 2.32 -> 1.91; 6.71 -> 5.02; 3.41 -> 8.68 | Mixed |
| Use ATA-E instead of ATA | ATA task allocation · SGD worker-time simulation, n=17, 51, 153, 459 | total worker time ratio (GTA / method): 1.3 -> 1.26; 2.91 -> 2.69; 7.22 -> 7.02; 12.45 -> 14.1 | Mixed |
| Use ATA instead of GTA greedy allocation | SGD task allocation · SGD worker-time simulation | runtime: runtime ratios 1.73; 2.43; 3.44; 6.36 | Hurt |
| Use ATA-E instead of GTA greedy allocation | SGD task allocation · SGD worker-time simulation | total worker time: total worker time ratios 1.26; 2.69; 7.02; 14.1 | Helped |
| Increase the number of prior timing runs | ATA and ATA-Empirical · SGD optimization setup, n=51 | training speed | Helped |
Surprising findings
- Greedy GTA is fastest in wall-clock runtime but its total worker time can be arbitrarily worse as the number of workers grows.
Limitations
- Bounds include a multiplicative factor 1+4 eta ln B that is not 1, so optimality is only up to a constant.
- ATA-Empirical requires an upper bound on eta = max alpha_i/mu_i, and ATA's bound depends on the maximum Orlicz norm, which can be loose when arm variances differ.
Experimental setup
Datasets: CIFAR-100
Benchmarks: simulated quadratic optimization with SGD CIFAR-100 CNN training with Adam
Metrics: runtime to reach suboptimality 1e-5 total worker time average iteration time cumulative regret
Baselines: GTA-SGD (Rennala SGD) OFTA (oracle optimal fixed allocation) UTA (uniform allocation)
Builds on: Rennala SGD Ringmaster ASGD UCB/LCB bandit methods Minibatch SGD
Closest papers by meaning
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