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.

中文翻译：

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探索配置文件数据对 HotSpot JVM 中代码质量的影响

托管语言虚拟机 (VM) 依靠动态或即时 (JIT) 编译在运行时生成优化的本机代码，以提供高执行性能。许多 VM 和 JIT 编译器收集轮廓数据以启用配置文件引导优化 (PGO)，将生成的本机代码自定义为不同的程序输入。PGO 通常被认为是 VM 生成高质量和高性能本机代码的组成部分。在这项工作中，我们研究和量化 PGO 的性能优势，了解分析数据数量和质量/准确性以有效指导 PGO 的重要性，并评估单个 PGO 对 VM 性能的影响。从这项工作中获得的见解可用于了解 PGO 的当前状态，制定战略以更有效地平衡成本和利用 PGO 的潜力，并探索替代提前 (AOT) 的影响和挑战VM 使用的编译模型。