Profile guided optimization for dataflow predication

Dataflow predication provides a lightweight full support for predicated execution in dataflow-like architectures. One of its major overhead is the large amounts of fanout trees for distributing predicates to all dependant instructions. Conventional optimizations are predicating only the heads or tails of dataflow chains. Predicating tails offers more speculation but leads to resource contentions and power consumption increasing. Predicating heads is power efficient but reduces speculation and instruction level parallelism. This paper introduces a profile guided technique to combine these optimizations. It uses profiling feedback to guide the compiler in deciding to predicate at the head or tail. By predicating tails on hot paths and predicating heads on infrequent paths, this technique can get performance, power and resource efficiency. Performance evaluation result shows that profile guided optimization performs better in removing fanout trees. It has 10.6% speedup over always predicating heads and 2.5% speedup over always predicating tails in performance.