Writing efficient multithreaded code that can leverage the full parallelism of underlying hardware is difficult. A key impediment is insidious cache contention issues, such as false sharing. False sharing occurs when multiple threads from different cores access disjoint portions of the same cache line, causing it to go back and forth between the caches of different cores and leading to substantial slowdown.

Alas, existing techniques for detecting and repairing false sharing have limitations. On the one hand, in-house (i.e., offline) techniques are limited to situations where falsely-shared data can be determined statically, and are otherwise inaccurate. On the other hand, in-production (i.e., run-time) techniques incur considerable overhead, as they constantly monitor a program to detect false sharing. In-production repair techniques are also limited by the types of modifications they can perform on the fly, and are therefore less effective.

We present Huron, a hybrid in-house/in-production false sharing detection and repair system. Huron detects and repairs as much false sharing as it can in-house, and relies on its lightweight in-production mechanism for remaining cases. The key idea behind Huron's in-house false sharing repair is to group together data that is accessed by the same set of threads, to shift falsely-shared data to different cache lines. Huron's in-house repair technique can generalize to previously-unobserved inputs. Our evaluation shows that Huron can detect more false sharing bugs than all state-of-the-art techniques, and with a lower overhead. Huron improves runtime performance by 3.82× on average (up to 11×), which is 2.11-2.27× better than the state of the art.