First step to combining control and data speculation
Toshinori Sato
Abstract
Toshinori Sato
Abstract
Recently there are many studies of data value prediction for increasing instruction level parallelism, and it is found that data speculation affects branch prediction accuracy. Even when data dependences are speculated successfully, processor performance would be degraded if branch prediction accuracy were decreased. On the other hand, branch prediction studies are nearly matured. While it becomes very difficult to increase the accuracy, there is still a vast gap between actual and ideal processor performance. From these considerations, we investigate to combine control and data speculation. Here, we evaluate the correlation between control speculation and data speculation and then propose to predict branch outcomes using data value prediction. Keywords: instruction level parallelism, speculative execution, branch prediction, value prediction, speculative verification.
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Recently there are many studies of data value prediction for increasing instruction level parallelism, and it is found that data speculation affects branch prediction accuracy. Even when data dependences are speculated successfully, processor performance would be degraded if branch prediction accuracy were decreased. On the other hand, branch prediction studies are nearly matured. While it becomes very difficult to increase the accuracy, there is still a vast gap between actual and ideal processor performance. From these considerations, we investigate to combine control and data speculation. Here, we evaluate the correlation between control speculation and data speculation and then propose to predict branch outcomes using data value prediction. Keywords: instruction level parallelism, speculative execution, branch prediction, value prediction, speculative verification.
Key concepts: Speculation, Branch predictor, Speculative execution, Computer science, Parallelism (grammar), Value (mathematics), Speculative multithreading, Instruction-level parallelism