ML-for-ML
Not on the map. Why?
Abstract (arXiv)
AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important. In shared cloud clusters, training and fine-tuning jobs compete with co-running workloads for network resources, while network mechanisms and ML training choices are typically optimized separately: networking controls how bytes move, whereas ML systems control when and how much communication occurs. We argue that this separation leaves end-to-end performance on the table. We present ML-for-ML, a cross-layer perspective in which network-side and ML-side knobs are selected jointly under a shared time-to-target-loss objective. Our preliminary prototype shows that by co-optimizing the ML and network parameters, we reach the target loss up to 42% faster.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 6 Aug 2026.
Builds on
18 of 39 references.
References · 18
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
- 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,492
- 2015Cyclical Learning Rates for Training Neural Networkscited by 629
- 2018Local SGD Converges Fast and Communicates Littlecited by 605
- 2017Don't Decay the Learning Rate, Increase the Batch Sizecited by 320
- 2018An Empirical Model of Large-Batch Trainingcited by 281
- 2017Train longer, generalize better: closing the generalization gap in large batch training of neural networkscited by 279
- 2017Sparse Communication for Distributed Gradient Descentcited by 249
- 2019Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloadscited by 79
- 2018Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGDcited by 77
- 2020Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learningcited by 72
6 more
- 2023CASSINI: Network-Aware Job Scheduling in Machine Learning Clusterscited by 26
- 2023THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compressioncited by 15
- 2021Design and Operation of Shared Machine Learning Clusters on Campuscited by 12
- 2024Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compressioncited by 6
- 2026DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reducecited by 4
- 2013Low latency via redundancycited by 2
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Citing papers per year: 2026: 1.
1 paper.