Runtime predictability of loops

To obtain the benefits of aggressive, wide-issue, architectures, a large window of valid instructions must be available. While researchers have been successful in obtaining high accuracies with a range of dynamic branch predictors, there still remains the need for more aggressive instruction delivery. Loop bodies possess a large amount of spatial and temporal locality. A large percentage of a program's entire execution can be attributed to code found in loop bodies. If we retain this code in a buffer or the cache, we do not have to refetch this code on subsequent loop iterations., Loops tend to iterate multiple times before exiting, thus providing us with the opportunity to speculatively issue multiple iterations. While some loops can be unrolled by a compiler many contain conditional branches. The number of times a loop iterates may be dependent on a program variable. These issues can hinder our ability to speculatively issue multiple iterations of a loop. If we are able to profile loops during runtime, we can use this information to more accurately issue speculative paths through loop bodies. In this paper we present a characterization of loop execution across the SPECint2000 benchmark suite. We intend for this study to serve as a guide in the selection of design parameters of a loop path predictor We characterize the patterns exhibited during multiple visits to a loop body. We present the design of a table that records path-based loop execution history and allows us to predict multiple loop iterations dynamically.