Data structure-aware heap partitioning

Abstract

There are many applications of program (or heap) partitioning, such as computation offloading, region-based memory management, and OS-driven memory locality optimizations. Although these applications are conceptually different, fundamentally, they must generate code such that objects in the heap (and hence the code that operates on those objects) get partitioned depending on how those objects are used. Regardless of the intended application goal, the granularity at which the heap is partitioned is the key factor in partition quality, and hence it needs to be carefully chosen. Previous work suggested two main granularities: class-based and allocation site--based, where objects from the same class (or those allocated at the same allocation site) are co-located. Both approaches share a critical drawback: data structures that are used in different ways can share the same class, or the same allocation sites for internal objects, and hence are forced to be co-located despite their different usage patterns. We introduce the notion of data structure--aware partitioning to allow different data structures to be placed in different partitions, even by existing tools and analyses that inherently operate in a class-based or allocation site--based manner. Our strategy consists of an analysis that infers ownership properties between objects to identify data structures, and a code generation phase that encodes this ownership information into objects' data types and allocation sites without changing the semantics of the code. We evaluate the quality of data structure--aware partitions by comparing it to the state-of-the-art allocation site--based partitioning on a subset of the DaCapo Benchmarks. Across a set of randomized trials, we had a median range of 5% to 25% reduction of cross-partition accesses, and, depending on partitioning decisions, up to a 95% reduction.