关于线程收敛
On Thread Convergence
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中文总结 AI 辅助
研究控制流图节点和边的收敛概念，通过形式化相关定义、给出推理规则和算法在流图中传播收敛信息，并利用多种信息改进精度，帮助编译器确定屏障位置，避免代码转换。
中文摘要 AI 辅助
我们引入了控制流图节点和边的收敛概念，用于捕捉在该位置放置的屏障是否能保证在每次执行中同步线程块的所有线程。收敛分析使编译器能确定屏障是否处于统一执行区域，从而避免在 warp 同步硬件上正确实现线程块屏障所需的代码转换。我们形式化了收敛节点、收敛边和同步良好的程序，给出两个推理规则及线性时间迭代工作列表算法在流图中双向传播收敛信息，还描述了利用单入口单出口区域信息、路径信息和线程方差信息提高精度的改进方法。
英文摘要
We introduce a notion of convergence for the nodes and edges of a control-flow graph that captures whether a barrier placed at that location is guaranteed to synchronize all threads of a thread block in every execution. Convergence analysis lets a compiler determine when a barrier lies in a uniformly executed region and therefore avoid the code transformations otherwise required to implement thread-block barriers correctly on warp-synchronous hardware. We formalize convergent nodes, convergent edges, and well-synchronized programs; give two inference rules (a branch rule and a merge rule); and present a linear-time iterative work-list algorithm that propagates convergence information bidirectionally through the flow graph. We then describe refinements that improve precision using single-entry single-exit region information, path information, and thread-variance information.
