Online shared memory dependence reduction via bisectional coordination.

Order of shared memory accesses, known as the shared memory dependence, is the cornerstone of dynamic analyses of concurrent programs. In this paper, we study the problem of reducing shared memory dependences. We present the first online software-only algorithm to reduce shared memory dependences without vector clock maintenance, opening a new direction to a broad range of applications (e.g., deterministic replay and data race detection). Our algorithm exploits a simple yet effective observation, that adaptive variable grouping can recognize and match spatial locality in shared memory accesses, to reduce shared memory dependences. We designed and implemented the bisectional coordination protocol, which dynamically maintains a partition of the program's address space without its prior knowledge, such that shared variables in each partitioned interval have consistent thread and spatial locality properties. Evaluation on a set of real-world programs showed that by paying a 0--54.7% (median 21%) slowdown, bisectional coordination reduced 0.95--97% (median 55%) and 16--99.99% (median 99%) shared memory dependences compared with RWTrace and LEAP, respectively.