sp-DBA：一种自适应变换域计算的通用框架
sp-DBA: a general framework for adaptive transform-domain computation
浏览论文内容
中文总结 AI 辅助
本文提出sp-DBA框架，在不重建现有求解器的情况下为变换域工作流引入执行级自适应，经多领域测试实现显著加速，提升了科学模拟与信号处理计算的规模及复杂度。
中文摘要 AI 辅助
变换域方法简化了分析与计算，使其成为科学计算和信号处理的核心。然而，现有自适应策略常引入新的数据结构或需大幅重新设计工作流，限制了在大规模并行硬件上的高效执行。本文提出谱动态块激活（sp-DBA），一种针对变换域工作流的自适应加速框架。sp-DBA会随计算推进动态激活变换域计算块，在保留数值精度与优化变换操作的同时，将计算集中在需要的区域。在材料科学、生物学和光通信的代表性工作流中，已实现的变换域更新加速比最高达28.1倍，整体加速比最高达8.4倍；在最多8个GPU上进行的强缩放和弱缩放测试表明，该局部自适应更新在分布式FFT工作流中仍保持有效。通过在成熟变换域工作流中引入执行级自适应，无需重建现有求解器，sp-DBA扩展了现代并行系统上科学模拟和信号处理计算的规模、时长与复杂度。
英文摘要
Transform-domain methods simplify analysis and computation, making them central to scientific computing and signal processing. However, existing adaptive strategies often introduce new data structures or require substantial workflow redesign, limiting efficient execution on massively parallel hardware. Here we present spectral dynamic block activation (sp-DBA), an adaptive acceleration framework for transform-domain workflows. sp-DBA dynamically activates transform-domain computation blocks as a calculation evolves, concentrating computation where it is needed while retaining numerical accuracy and optimized transform operations. Across representative workflows in materials science, biology, and optical communications, the demonstrated implementations achieve speedups of up to 28.1-fold for transform-domain updates and up to 8.4-fold overall acceleration; strong- and weak-scaling tests on up to eight GPUs show that the local adaptive update remains effective within distributed FFT workflows. By introducing execution-level adaptivity into mature transform-domain workflows without rebuilding existing solvers, sp-DBA extends the scale, duration, and complexity of scientific simulations and signal-processing calculations on modern parallel systems.
发表机构
- College of Materials Science and Engineering, Hunan University（湖南大学材料科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
