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arXiv · 2610.25033

PlaneZip: Lossless Bit-Plane Compression of Training Activations Offloaded over PCIe

Zhufan Gu·Qizhe Tang·Yuxin Wang·Xun Zhou·Hui Feng
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Abstract

Training under a device-memory budget relies on tensor rematerialization: an evicted activation is either swapped to host memory or dropped and recomputed. Because GPU compute has outgrown PCIe bandwidth, cost-model planners such as MegTaiChi, which charge a swap its full transfer time, almost never swap, and swaps that fill idle link time carry only a small fraction of the saved activations. Saved activations, however, are homogeneously typed arrays with bit-level redundancy beyond zeros, which lightweight bit-plane compressors can remove at line rate. We present PlaneZip, which compresses swapped tensors on the copy path and plans around the compression. Its codec picks, for every 128-byte block, the smallest of zero-value, delta bit-plane, bit-plane, and a new zero-masked bit-plane encoding. A per-tensor predictor reuses the ratio of the previous iteration, and the planner budgets link time in compressed transfer times. On activations captured from five models trained on real data, the codec compresses BF16 activations by 1.71x and FP32 activations by 1.39x (geometric means), against 1.18x and 1.20x for zero-value compression. In trace-driven simulation of a PCIe 3.0 consumer GPU and a PCIe 4.0 datacenter GPU at half the activation memory, PlaneZip cuts the rematerialization overhead by 47% and 31% relative to a bandwidth-aware planner without compression, and by 73% and 54% relative to MegTaiChi's cost model, with an estimated engine area of 0.036 square millimeters.

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Zhufan Gu, Qizhe Tang, Yuxin Wang, Xun Zhou, Hui Feng. 2026-10-18. PlaneZip: Lossless Bit-Plane Compression of Training Activations Offloaded over PCIe. https://arxiv.org/abs/2610.25033

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