Mixed speculative multithreaded execution models
ACM Transactions on Architecture and Code Optimization (TACO)
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While a canonical out-of-order engine can effectively exploit implicit parallelism in sequential programs, its effectiveness is often hindered by instruction and data supply imperfections manifested as branch mispredictions and cache misses. Accurate and deep look-ahead guided by a slice of the executed program is a simple yet effective approach to mitigate the performance impact of branch mispredictions and cache misses. Unfortunately, program slice-guided look ahead is often limited by the speed of the look-ahead code slice, especially for irregular programs. In this paper, we attempt to speed up the look-ahead agent using speculative parallelization, which is especially suited for the task. First, slicing for look-ahead tends to reduce important data dependences that prohibit successful speculative parallelization. Second, the task for look-ahead is not correctness critical and thus naturally tolerates dependence violations. This enables an implementation to forgo violation detection altogether, simplifying architectural support tremendously. In a straightforward implementation, incorporating speculative parallelization to the look-ahead agent further improves system performance by up to 1.39x with an average of 1.13x.