有前途的2.0:放松内存并发性的全局优化

Sung-Hwan Lee, Minki Cho, A. Podkopaev, S. Chakraborty, C. Hur, O. Lahav, Viktor Vafeiadis
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引用次数: 41

摘要

在超过15年的时间里,研究人员一直试图用一种可用的语义来支持并发编程语言的全局优化,然而这项任务已经被证明是非常困难的,因为:(1)臭名昭著的“无中生有”问题,(2)全局优化和线程局部优化之间的微妙交互。在本文中,我们通过重新设计Kang等人的有希望语义(PS)的关键组件来解决这个问题。我们更新的PS 2.0模型支持原始PS模型的所有已知结果(即,线程本地优化,硬件映射,DRF定理),但还支持基于全局值范围分析和寄存器提升的转换(即,如果只有一个线程访问共享位置,则访问本地位置)。PS 2.0还解决了将宽松的rmw编译为ARMv8的问题,这需要一个意想不到的额外篱笆。
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Promising 2.0: global optimizations in relaxed memory concurrency
For more than fifteen years, researchers have tried to support global optimizations in a usable semantics for a concurrent programming language, yet this task has been proven to be very difficult because of (1) the infamous “out of thin air” problem, and (2) the subtle interaction between global and thread-local optimizations. In this paper, we present a solution to this problem by redesigning a key component of the promising semantics (PS) of Kang et al. Our updated PS 2.0 model supports all the results known about the original PS model (i.e., thread-local optimizations, hardware mappings, DRF theorems), but additionally enables transformations based on global value-range analysis as well as register promotion (i.e., making accesses to a shared location local if the location is accessed by only one thread). PS 2.0 also resolves a problem with the compilation of relaxed RMWs to ARMv8, which required an unintended extra fence.
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