Locality-aware adaptive grain signatures for Transactional Memories

Transactional Memory (TM) has attracted considerable attention because it promises to increase programmer productivity by making it easier to write correct parallel programs. To maintain correctness in the face of concurrency, detecting conflicts among simultaneously running transactions is an essential element. Hardware signatures have been proposed as an area-efficient mechanism for conflict detection. A signature can summarize an unbounded amount of addresses and misses no conflicts, but could falsely declare conflicts even when no true conflict exists (false positives) due to aliasing and occupancy. Previous signature designs assume that false positives are destructive to performance and attempt to reduce the total number of false positives. In this paper, we show that some false positives can be helpful to performance by triggering the early abortion of a transaction which would encounter a true conflict later anyway. Based on this observation, we propose an adaptive grain signature to improve performance by dynamically changing the range of address keys based on the history. With the use of adaptive grain signatures, we can increase the number of performance-friendly false positives as well as decrease the number of performance-destructive false positives.