aiqbench analysis
ML-for-ML addresses the critical bottleneck in large-scale AI training by jointly optimizing network and machine learning parameters to accelerate convergence. This cross-layer approach demonstrates that co-designing infrastructure controls with model dynamics can reduce time-to-target-loss significantly compared to traditional separate optimizations. As distributed computing clusters grow, such integrated strategies become essential for managing rising energy costs and improving overall system efficiency without relying solely on hardware upgrades.
ML-for-ML
arXiv:2608.06046v1 Announce Type: cross Abstract: 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.
Abstract: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.
Networking and Internet Architecture (cs.NI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
arXiv:2608.06046 [cs.NI]
(or arXiv:2608.06046v1 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2608.06046 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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