Abstract
Managed language virtual machines (VM) rely on dynamic or just-in-time (JIT) compilation to generate optimized native code at run-time to deliver high execution performance. Many VMs and JIT compilers collect profile data at run-time to enable profile-guided optimizations (PGO) that customize the generated native code to different program inputs. PGOs are generally considered integral for VMs to produce high-quality and performant native code. In this work, we study and quantify the performance benefits of PGOs, understand the importance of profiling data quantity and quality/accuracy to effectively guide PGOs, and assess the impact of individual PGOs on VM performance. The insights obtained from this work can be used to understand the current state of PGOs, develop strategies to more efficiently balance the cost and exploit the potential of PGOs, and explore the implications of and challenges for the alternative ahead-of-time (AOT) compilation model used by VMs.
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