SparkleDock让分子对接在超算上实现秒级完成，突破了精度与速度的瓶颈。
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
- 重设计萤火虫优化算法，实现每个分子代理的细粒度并行计算
- 用张量核心兼容的矩阵运算加速能量评分，提升GPU利用率
- 支持跨多GPU负载均衡和外存扩展，适合大规模虚拟筛选
柔性分子对接能高保真预测生物分子相互作用，但传统方法成本高昂。现有方法LightDock虽准确，却因并行性差、计算不规则和负载不均，难以在GPU超算上高效运行。本文提出SparkleDock，一种基于GSO的可扩展对接框架，重新设计GSO以在萤火虫代理层面暴露大规模细粒度并行性，并将主要能量评分计算重构为适配张量核心的矩阵形式，通过结构化运算高效处理不规则成对交互。进一步引入基于性能模型的调度策略，实现跨GPU负载均衡与外存扩展。SparkleDock在单块A100和H100 GPU上分别比LightDock快9.7倍和18.9倍，在512块GPU上实现超过两个数量级的加速，使原本耗时数小时的对接任务缩短至秒级，首次实现大规模高保真虚拟筛选。
原文摘要 · Abstract (English)
Flexible macromolecular docking offers high-fidelity predictions of biomolecular interactions, but remains prohibitively expensive at scale. Among existing approaches, LightDock leverages Glowworm Swarm Optimization (GSO) for accuracy, yet suffers from limited parallelism, irregular computation, and severe load imbalance, preventing efficient execution on GPU supercomputers. We present SparkleDock, a scalable GSO-based docking framework enabling near-real-time flexible docking. We redesign GSO to expose massive fine-grained parallelism at the glowworm-agent level, and restructure the dominant energy scoring computation into a Tensor Core-compatible formulation, enabling efficient execution of irregular pairwise interactions through structured matrix operations. We further introduce a performance-model-driven scheduling for load balancing and out-of-core scaling across GPUs. SparkleDock achieves 9.7 $\times$ and 18.9 $\times$ speedups over LightDock on single A100 and H100 GPU, and delivers over two orders of magnitude acceleration at scale. On 512 GPUs, it reduces docking time from hours to seconds, enabling large-scale, high-fidelity virtual screening previously impractical with flexible docking.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。