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GPU-aware collective communication has become a major bottleneck for moderncomputing platforms as GPU computing power rapidly rises. To address thisissue, traditional approaches integrate lossy compression directly intoGPU-aware collectives, which still suffer from serious issues such asunderutilized GPU devices and uncontrolled data distortion. In this paper, wepropose gZCCL, a general framework that designs and optimizes GPU-aware,compression-enabled collectives with an accuracy-aware design to control errorpropagation. To validate our framework, we evaluate the performance on up to512 NVIDIA A100 GPUs with real-world applications and datasets. Experimentalresults demonstrate that our gZCCL-accelerated collectives, including bothcollective computation (Allreduce) and collective data movement (Scatter), canoutperform NCCL as well as Cray MPI by up to 4.5X and 28.7X, respectively.Furthermore, our accuracy evaluation with an image-stacking applicationconfirms the high reconstructed data quality of our accuracy-aware framework.

gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters