Object flattening is a non-trivial optimization that inlines the fields of an object inside its containers. Owing to its direct applicability for immutable objects, Java would soon allow programmers to mark compatible classes as “value types”, and Java Virtual Machines (JVMs) to transparently flatten their instances (value objects). Expectations include reduced memory footprint, faster field access, and overall improvement in performance. This paper describes the surprises and challenges we faced while experimenting with value types and object flattening on a real-world JVM, and presents the design of an efficient strategy that selectively flattens profitable value objects, using a novel combination of static and dynamic analyses.

Our value-object flattening strategy is based on insights that span the source program, the just-in-time (JIT) compiler employed by the JVM, as well as the underlying hardware. The first insight identifies source-level patterns that favour and oppose value-object flattening. The second insight finds an interesting dependence of object flattening on object scalarization, and estimates the capability of the JIT in avoiding overheads using escape analysis. Finally, the third insight correlates container objects with cache-line size, based on the load semantics of object fields. In order to develop an efficient strategy to flatten potentially profitable objects, we capture these insights in a tool called VFLATTEN that uses a novel combination of static and dynamic analyses and flattens value objects selectively in a production Java runtime.