hZCCL: Accelerating Collective Communication with Co-Designed Homomorphic Compression

As network bandwidth struggles to keep up with rapidly growing computing capabilities, the efficiency of collective communication has become a critical challenge for exa-scale distributed and parallel applications. Traditional approaches directly utilize error-bounded lossy compression to accelerate collective computation operations, exposing unsatisfying performance due to the expensive decompression-operation-compression (DOC) workflow. To address this issue, we present a first-ever homomorphic compression-communication co-design, hZCCL, which enables operations to be performed directly on compressed data, saving the cost of time-consuming decompression and recompression. In addition to the co-design framework, we build a light-weight compressor, optimized specifically for multi-core CPU platforms. We also present a homomorphic compressor with a run-time heuristic to dynamically select efficient compression pipelines for reducing the cost of DOC handling. We evaluate $h \mathbf{Z C C L}$ with up to 512 nodes and across five application datasets. The experimental results demonstrate that our homomorphic compressor achieves a CPU throughput of up to $379.08 \mathrm{~GB} / \mathrm{s}$, surpassing the conventional DOC workflow by up to $36.53 \times$. Moreover, our $\boldsymbol{h Z C C L}$-accelerated collectives outperform two state-of-the-art baselines, delivering speedups of up to $2.12 \times$ and $6.77 \times$ compared to original MPI collectives in single-thread and multi-thread modes, respectively, while maintaining data accuracy.