A survey of inlining heuristics

This blog post explores the inlining heuristics used by various Just-In-Time (JIT) compilers, focusing on the trade-offs between performance optimization and code size. The author discusses challenges such as method call overhead, memory traffic, and the balance between inlining methods for performance and managing the potential size increase that may complicate cache efficiency. The post surveys multiple JIT implementations including Cinder, PyPy, V8, and others, detailing their specific strategies and heuristics for inlining, and concludes by considering emerging research trends including machine learning approaches for optimization heuristics.