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Trace-based compilation is a promising technique for language compilers and binary translators. It offers the potential to expand the compilation scopes that have traditionally been limited by method boundaries. Detecting repeating cyclic execution paths and capturing the detected repetitions into traces is a key requirement for trace selection algorithms to achieve good optimization and performance with small amounts of code. One important class of repetition detection is cyclic-path-based repetition detection, where a cyclic execution path (a path that starts and ends at the same instruction address) is detected as a repeating cyclic execution path. However, we found many cyclic paths that are not repeating cyclic execution paths, which we call false loops. A common class of false loops occurs when a method is invoked from multiple call-sites. A cycle is formed between two invocations of the method from different call-sites, but which does not represent loops or recursion. False loops can result in shorter traces and smaller compilation scopes, and degrade the performance. We propose false loop filtering, an approach to reject false loops in the repetition detection step of trace selection, and a technique called false loop filtering by call-stack-comparison, which rejects a cyclic path as a false loop if the call stacks at the beginning and the end of the cycle are different. We applied false loop filtering to our trace-based Java™ JIT compiler that is based on IBM's J9 JVM. We found that false loop filtering achieved an average improvement of 16% and 10% for the DaCapo benchmark when applied to two baseline trace selection algorithms, respectively, with up to 37% improvement for individual benchmarks. In the end, with false loop filtering, our trace-based JIT achieves a performance comparable to that of the method-based J9 JVM/JIT using the corresponding optimization level.

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This paper describes our multi-level compilation techniques implemented in a trace-based Java JIT compiler (trace-JIT). Like existing multi-level compilation for method-based compilers, we start JIT compilation with a small compilation scope and a low optimization level so the program can start running quickly. Then we identify hot paths with a timer-based sampling profiler, generate long traces that capture the hot paths, and recompile them with a high optimization level to improve the peak performance. A key to high performance is selecting long traces that effectively capture the entire hot paths for upgrade recompilations. To do this, we introduce a new technique to generate a directed graph representing the control flow, a TTgraph, and use the TTgraph in the trace selection engine to efficiently select long traces. We show that our multilevel compilation improves the peak performance of programs by up to 58.5% and 22.2% on average compared to compiling all of the traces only at a low optimization level. Comparing the performance with our multi-level compilation to the performance when compiling all of the traces at a high optimization level, our technique can reduce the startup times of programs by up to 61.1% and 31.3% on average without significant reduction in the peak performance. Our results show that our adaptive multilevel compilation can balance the peak performance and startup time by taking advantage of different optimization levels.

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