For maximum performance, an out-of-order processor must issue load instructions as early as possible, while avoiding memory-order violations with prior store instructions that write to the same memory location. One approach is to use memory dependence prediction to identify the stores upon which a load depends, and communicate that information to the instruction scheduler. We designate the set of stores upon which each load has depended as the load's "store set". The processor can discover and use a load's store set to accurately predict the earliest time the load can safely execute. We show that store sets accurately predict memory dependencies in the context of large instruction window, superscalar machines, and allow for near-optimal performance compared to an instruction scheduler with perfect knowledge of memory dependencies. In addition, we explore the implementation aspects of store sets, and describe a low cost implementation that achieves nearly optimal performance.

Profile data is valuable for identifying performance bottlenecks and guiding optimizations. Periodic sampling of a processor's performance monitoring hardware is an effective, unobtrusive way to obtain detailed profiles. Unfortunately, existing hardware simply counts events, such as cache misses and branch mispredictions, and cannot accurately attribute these events to instructions, especially on out-of-order machines. We propose an alternative approach, called ProfileMe, that samples instructions. As a sampled instruction moves through the processor pipeline, a detailed record of all interesting events and pipeline stage latencies is collected. ProfileMe also supports paired sampling, which captures information about the interactions between concurrent instructions, revealing information about useful concurrency and the utilization of various pipeline stages while an instruction is in flight. We describe an inexpensive hardware implementation of ProfileMe, outline a variety of software techniques to extract useful profile information from the hardware, and explain several ways in which this information can provide valuable feedback for programmers and optimizers.