  We present the Sequential Aggregation and Rematerialization (SAR) scheme for
distributed full-batch training of Graph Neural Networks (GNNs) on large
graphs. Large-scale training of GNNs has recently been dominated by
sampling-based methods and methods based on non-learnable message passing. SAR
on the other hand is a distributed technique that can train any GNN type
directly on an entire large graph. The key innovation in SAR is the distributed
sequential rematerialization scheme which sequentially re-constructs then frees
pieces of the prohibitively large GNN computational graph during the backward
pass. This results in excellent memory scaling behavior where the memory
consumption per worker goes down linearly with the number of workers, even for
densely connected graphs. Using SAR, we report the largest applications of
full-batch GNN training to-date, and demonstrate large memory savings as the
number of workers increases. We also present a general technique based on
kernel fusion and attention-matrix rematerialization to optimize both the
runtime and memory efficiency of attention-based models. We show that, coupled
with SAR, our optimized attention kernels lead to significant speedups and
memory savings in attention-based GNNs.We made the SAR GNN training library
publicy available: \url{https://github.com/IntelLabs/SAR}.
