Branchtap: Improving performance with very few checkpoints through adaptive speculation control

Checkpoint prediction and intelligent management have been recently proposed for reducing the number of coarse-grain checkpoints needed to achieve high performance through speculative execution. In this work, we take a closer look at various checkpoint prediction and management alternatives, comparing their performance and requirements as the scheduler window size increases. We also study a few additional design choices. The key contribution of this work is BranchTap, a novel checkpoint-aware speculation strategy that temporarily throttles speculation to reduce recovery cost while allowing speculation to proceed when it is likely to boost performance. BranchTap dynamically adapts to application behavior. We demonstrate that for a 1K-entry window processor with a FIFO of just four checkpoints, our adaptive speculation control mechanism leads to an average performance degradation of just 1.49 % compared to a processor that has an infinite number of checkpoints. This represents an improvement of 28.3 % over using just predictionbased checkpoint allocation. Average performance degradation without BranchTap is 2.08%. For the same configuration, BranchTap decreases the worst case deterioration from 8.99 % t