Profile Guided Load Marking for Memory Renaming

Memory operations remain a significant bottleneck in dynamically scheduled pipelined processors, due in part to the inability to statically determine the existence of memory address dependencies. Hardware memory renaming techniques have been proposed which predict which stores a load might be dependent upon. These prediction techniques can be used to speculatively forward a value from a predicted store dependency to a load through a value prediction table; however, these techniques require large and time-consuming hardware tables. In this paper we propose a software-guided approach for identifying dependencies between store and load instructions and the Load Marking (LM) architecture to communicate these dependencies to the hardware. Compiler analysis and profiles are used to find important store/load relationships, and these relationships are identified during execution via hints or an n-bit tag. For those loads that are not marked for renaming, we then use additional profiling inform..