Zejian Wang, Yifan Zhao, Kaihui Ren, Xiaoling Zhou, Yubin Wang, Chenhao Guan, Lin Zhang, Yufeng Li 等 14 位作者
Fully Sharded Data Parallelism (FSDP2) maintains parameters and gradients as per-parameter tensors, whereas its collective path operates on flat, rank-major buffers. Converting between these layouts copies all-gather outputs into parameter storage and packs gradients into full-group fp32 buffers. Layout conversion alone accounts for 6.4% of step time in Llama-3.1 70B training at 8K tokens per GPU. Communication kernels impose a second cost: a default NCCL reduce-scatter can slow a concurrent GEMM that would otherwise complete in a single wave by 69%. We present TIDE, a set of zero-copy collectives built on public NCCL 2.31+ APIs. TIDE preserves FSDP2's per-parameter storage and compute kernels while folding layout placement into collective addressing. TIDE-AG delivers shards directly to parameter storage using NVSwitch multicast and paced recursive-doubling RDMA, with no SMs used for transport or placement. TIDE-RS consumes per-parameter bf16 gradients directly and reduces them into fp32 shards (a mixed-precision mode that NCCL does not provide) using copy engines and short-lived thread blocks (CTAs) for node-local accumulation and NVSwitch for cross-node reduction. A two-phase pipeline separates gradient reads from network progress; Slot-Reuse optionally stages gradients in vacated all-gather slots. On 64 GPUs, TIDE reduces step time by up to 27.1% relative to Stock FSDP2 and 6.0% relative to FSDP2+SymmMem across Llama-3.1 70B, Wan2.2, and Qwen3-30B-A3B. It reduces reduce-scatter SM time by factors of 12.5-14.5 and cuts interference with concurrent compute by up to an order of magnitude. Its reduce-scatter buffer remains nearly constant from 16 to 64 GPUs at a fixed per-GPU output size, and its peak memory is up to 4.7 GiB below FSDP2+SymmMem's and, with Slot-Reuse on Wan2.2, below Stock FSDP2's.
Artificial intelligence doctor assistants (AIDAs) help streamline clinical decision-making and reduce physician workload. While existing systems primarily utilize Large Language Models (LLMs) or retrieval-augmented generation (RAG), these methods typically retrieve static facts—whether as text passages or structured graphs—lacking the explicit logical pathways essential for multi-step reasoning. In this paper, we propose the AIDA-SEAT framework to provide reliable clinical decision-making support. First, we design the state-evaluation-action tree (SEAT), which covers diagnosis, treatment, and examination. To develop this tree, we refine and transform SEAT collected from medical documents and doctors. Then, we propose an adaptive method to select optimal trees tailored to the current patients’ state. Finally, we leverage LLMs to perform state assessment, evaluation, and action execution based on the tree, thereby generating reliable responses. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built dataset. Our method achieves 1.01% higher than current state-of-the-art (SOTA) baselines across five departments, including common RAG-based methods. Furthermore, analysis of 200 consultation records during deployment on an online hospital revealed that system-assisted responses are 24.16 seconds faster on average than manual ones, improving efficiency by 26.85%.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表