Applying predication to efficiently handle runtime class testing

Abstract

Runtime class testing is a technique whereby virtual function calls are transformed into statically-bound function calls through a series of conditional branches. Through this transformation, the overhead of virtual function calls can be significantly reduced. However, the drawback of these tests is that by relying on conditional branches, the amount of instruction-level parallelism (ILP) is limited and the mispredict penalties can be relatively high. We show that by using predication during class testing, these drawbacks can be eliminated, and the benefits of class testing can be improved upon. Predication is a supported feature in Explicitly Parallel Instruction Computing (EPIC) architecture, which converts control dependencies into data dependencies, and thus eliminates the mispredict penalties. With analytical cost models and experimental results, we show that predicated class testing can reduce the direct cost of virtual function calls by 43% on an average, which in contrast to other class testing techniques, is a performance improvement of up to 31% per function call. These results are based on architectural specifications that most modern architectures will exceed, and are considered conservative. The actual speedups resulting from predicated class testing are expected to be higher