arXiv subjects

Lannie Dalton Hough

Publications and source records attributed to Lannie Dalton Hough.

2 recordsLinked to original sources

arXiv2026

SpCCL: A Sparsity-Aware Collective Communication Library for GPU Platforms

Collective communication is essential to high performance computing and machine learning workloads, yet libraries such as NCCL do not exploit sparsity in message payloads. Sending only nonzero values can reduce network traffic, but explicitly handling sparsity introduces challenges such as compression and decompression overheads. We address these challenges with sparsity-exploiting versions of all-gather, reduce-scatter, and all-reduce collectives. Our implementations use a new bitvector-based format, Pici, designed for low space overhead and fast GPU-based compression and decompression. Further, our collective algorithms adapt to the degree of sparsity in data, modifying data representations during the course of the collective. At 99% input sparsity, our collectives achieve up to 5.25$\times$, 2.5$\times$, and 2.66$\times$ speedups over NCCL for all-gather, reduce-scatter, and all-reduce, respectively. Integrating our collectives into a representative deep learning application, we achieve a 26% end-to-end speedup.

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arXiv2026

Understanding and Improving Communication Performance in Multi-node LLM Inference

As large language models (LLMs) continue to grow in size, distributed inference has become increasingly important. Model-parallel strategies must now efficiently scale not only across multiple GPUs but also across multiple nodes. In this work, we present a detailed performance study of multi-node distributed inference using LLMs on GPU-based supercomputers. We conduct experiments with several state-of-the-art inference engines alongside YALIS, a research-oriented prototype engine designed for controlled experimentation. We analyze the strong-scaling behavior of different model-parallel schemes and identify key bottlenecks. Because all-reduce operations are a common performance bottleneck, we develop NVRAR, a hierarchical all-reduce algorithm based on recursive doubling with NVSHMEM. NVRAR achieves up to 1.9$\times$-3.6$\times$ lower latency than NCCL for message sizes between 128 KB and 2 MB on HPE Slingshot and InfiniBand interconnects. Integrated into YALIS, NVRAR achieves up to a 1.72$\times$ reduction in end-to-end batch latency for the Llama 3.1 405B model in multi-node decode-heavy workloads using tensor parallelism.

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