Papers › Repositioning Tiered HotSpot Execution Performance Relative to the Interpreter
Repositioning Tiered HotSpot Execution Performance Relative to the Interpreter
Jonathan Lambert, Kevin Casey, Rosemary Monahan
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Although the advantages of just-in-time compilation over traditional interpretive execution are widely recognised, there needs to be more current research investigating and repositioning the performance differences between these two execution models relative to contemporary workloads. Specifically, there is a need to examine the performance differences between Java Runtime Environment (JRE) Java Virtual Machine (JVM) tiered execution and JRE JVM interpretive execution relative to modern multicore architectures and modern concurrent and parallel benchmark workloads. This article aims to fill this research gap by presenting the results of a study that compares the performance of these two execution models under load from the Renaissance Benchmark Suite. This research is relevant to anyone interested in understanding the performance differences between just-in-time compiled code and interpretive execution. It provides a contemporary assessment of the interpretive JVM core, the entry and starting point for bytecode execution, relative to just-in-time tiered execution. The study considers factors such as the JRE version, the GNU GCC version used in the JRE build toolchain, and the garbage collector algorithm specified at runtime, and their impact on the performance difference envelope between interpretive and tiered execution. Our findings indicate that tiered execution is considerably more efficient than interpretive execution, and the performance gap has increased, ranging from 4 to 37 times more efficient. On average, tiered execution is approximately 15 times more efficient than interpretive execution. Additionally, the performance differences between interpretive and tiered execution are influenced by workload category, with narrower performance differences observed for web-based workloads and more significant differences for Functional and Scala-type workloads.
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