Supporting Highly-Speculative Execution via Adaptive Branch Trees

Most of the prediction mechanisms predict a single path to continue the execution on a branch. Alternatively, we may exploit parallelism from either possible paths of a branch, discarding wrong paths once the branch is resolved. This paper proposes a concept of Adaptive Branch Trees (ABT) to support highly speculative execution for processors with deeper pipelines and wide issue widths. The basic idea of the adaptive branch tree is to dynamically keep track of alternative branch paths and to speculatively execute the code on the most likely path. Hence, unlike branch prediction mechanisms, the ABT scheme would not miss out misprediction paths since the scheme can eventually go back to other alternative paths when the machine has explored more pending branches. The branch tree is realized by an adaptive branch tree table. A token is associated with each basic block and operations in the entire basic blocks are tagged with the token. With a novel token assignment strategy, we can reconfigure the ABT by a shift operation once one branch is resolved. Our experiment results on the SPEC95 benchmarks show that the proposed approach can achieve significant branch penalty reduction for wide-issue processors.