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Data races can significantly affect the executions of multi-threadedprograms. Hence, one has to recur the results of data races todeterministically replay a multi-threaded program. However, data races areconcealed in enormous number of memory operations in a program. Due to thedifficulty of accurately identifying data races, previous multi-threadeddeterministic record/replay schemes for commodity multi-processor system giveup to record data races directly. Consequently, they either record all sharedmemory operations, which brings remarkable slowdown to the production run, orrecord the synchronization only, which introduces significant efforts toreplay.  Inspired by the advances in data race detection, we propose an efficientsoftware-only deterministic replay scheme for commodity multi-processorsystems, which is named RacX. The key insight of RacX is as follows: althoughit is NP-hard to accurately identify the existence of data races between a pairof memory operations, we can find out all potential data races in amulti-threaded program, in which the false positives can be reduced to a smallamount with our automatic false positive reduction techniques. As a result,RacX can efficiently monitor all potential data races to deterministicallyreplay a multi-threaded program.  To evaluate RacX, we have carried out experiments over a number of well-knownmulti-threaded programs from SPLASH-2 benchmark suite and large-scalecommercial programs. RacX can precisely recur production runs of these programswith value determinism. Averagely, RacX causes only about 1.21%, 1.89%, 2.20%,and 8.41% slowdown to the original run during recording (for 2-, 4-, 8- and16-thread programs, respectively). The soundness, efficiency, scalability, andportability of RacX well demonstrate its superiority.

Efficient Deterministic Replay Using Complete Race Detection