Over the past decade there has been a surge of academic and industrial interest in optimistic concurrency, i.e. the speculative parallel execution of code regions that have the semantics of isolation. This work analyzes scalability bottlenecks of workloads that use optimistic concurrency. We find that one common bottleneck is updates to auxiliary program data in otherwise non-conflicting operations, e.g. reference count updates and hashtable occupancy field increments. To eliminate the performance impact of conflicts on such auxiliary data, this work proposes RetCon, a hardware mechanism that tracks the relationship between input and output values symbolically and uses this symbolic information to transparently repair the output state of a transaction at commit. RetCon is inspired by instruction replay-based mechanisms but exploits simplifying properties of the nature of computations on auxiliary data to perform repair without replay. Our experiments show that RetCon provides significant speedups for workloads that exhibit conflicts on auxiliary data, including transforming a transactionalized version of the Python interpreter from a workload that exhibits no scaling to one that exhibits near-linear scaling on 32 cores.
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