Robustness is a key issue on any runtime system that aims to speed up the execution of a program. However, robustness considerations are commonly overlooked when new software-based, thread-level speculation (STLS) systems are proposed. This paper highlights the relevance of the problem, showing different situations when the use of incorrect data can irreversibly alter the speculative execution of an algorithm, despite the efforts of a given STLS system to maintain sequential consistency. We show that the management of speculative exceptions is a common factor to these problems. Based on this fact, we propose a novel solution to handle speculative exceptions. Our solution eagerly tries to solve the issue before the non-speculative thread arrives to the instruction that rose the exception. We compare our solution to a more conservative approach found in the bibliography. The comparison is done both qualitatively, through a detailed analysis of the tradeoffs involved, and quantitatively, evaluating the effects of both solutions in the execution of three different benchmarks on a real system. Both studies conclude that our solution handles the occurrence of speculative exceptions more efficiently. Under heavy loads intended to push to its limits a STLS system, our solution leads to execution times reduced by up to 52.02% with respect to earlier proposals. Our solution does not affect the performance when speculative exceptions do not appear. We believe that our proposal makes STLS systems robust enough to be used in production environments.

No abstract