VoltGrid: Microsecond Collective Interposition for Transient dI/dt Mitigation in Multi-Accelerator Training Clusters

Bulk Synchronous Parallelism (BSP) in distributed deep learning clusters induces severe rate-of-change current transients (dI/dt) across data center power delivery networks. When thousands of accelerators synchronously complete matrix multiplications and enter collective communication barriers (e.g., NCCL AllReduce), cluster current collapses in under 15 microseconds. By Lenz's Law (V_droop = L * dI/dt), this extreme slew rate induces massive reverse-EMF voltage drops across substation transformers and server voltage regulator modules (VRMs), tripping protective circuit breakers and restricting datacenter power utilization. We present VoltGrid, a zero-overhead C++/CUDA interposition engine (libnccl-voltflow.so) that eliminates synchronized inductive cliffs via deterministic, microsecond-scale rank phase cascading without modifying application code or container environments. Empirical validation on a physical multi-GPU cluster (4x NVIDIA GeForce RTX 4090, 1,677.7 W sustained load) demonstrates a 97.52% reduction in instantaneous sub-millisecond dI/dt power step shock, while preserving 100% of compute throughput with less than 0.05% step latency impact.