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cs.DC2026
Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
Yishun Lu, Junhao Zhang, Zeyu Yang +1
Second-order methods offer an attractive path toward more sample-efficient LLM training, but their practical use is often blocked by the systems cost of maintaining and updating la…
cs.DC2025
CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads
Radostin Stoyanov, Viktória Spišaková, Jesus Ramos +5
Deep learning training at scale is resource-intensive and time-consuming, often running across hundreds or thousands of GPUs for weeks or months. Efficient checkpointing is crucial…