  GPU-aware collective communication has become a major bottleneck for modern
computing platforms as GPU computing power rapidly rises. A traditional
approach is to directly integrate lossy compression into GPU-aware collectives,
which can lead to serious performance issues such as underutilized GPU devices
and uncontrolled data distortion. In order to address these issues, in this
paper, we propose gZCCL, a first-ever general framework that designs and
optimizes GPU-aware, compression-enabled collectives with an accuracy-aware
design to control error propagation. To validate our framework, we evaluate the
performance on up to 512 NVIDIA A100 GPUs with real-world applications and
datasets. Experimental results demonstrate that our gZCCL-accelerated
collectives, including both collective computation (Allreduce) and collective
data movement (Scatter), can outperform NCCL as well as Cray MPI by up to 4.5X
and 28.7X, respectively. Furthermore, our accuracy evaluation with an
image-stacking application confirms the high reconstructed data quality of our
accuracy-aware framework.
