OJXPerf: Featherlight Object Replica Detection for Java Programs

TL;DR

OJXPerf is a lightweight profiling tool that detects identical objects in Java programs, enabling developers to optimize memory usage and performance by reducing object duplication.

Contribution

It introduces a novel, sampling-based method using hardware performance monitoring to identify duplicate objects in Java, with minimal overhead.

Findings

Up to 11% reduction in memory usage

Up to 25% speedup in program execution

9% runtime and 6% memory overheads on average

Abstract

Memory bloat is an important source of inefficiency in complex production software, especially in software written in managed languages such as Java. Prior approaches to this problem have focused on identifying objects that outlive their life span. Few studies have, however, looked into whether and to what extent myriad objects of the same type are identical. A quantitative assessment of identical objects with code-level attribution can assist developers in refactoring code to eliminate object bloat, and favor reuse of existing object(s). The result is reduced memory pressure, reduced allocation and garbage collection, enhanced data locality, and reduced re-computation, all of which result in superior performance. We develop OJXPerf, a lightweight sampling-based profiler, which probabilistically identifies identical objects. OJXPerf employs hardware performance monitoring units (PMU) in conjunction with hardware debug registers to sample and compare field values of different objects of the same type allocated at the same calling context but potentially accessed at different program points. The result is a lightweight measurement, a combination of object allocation contexts and usage contexts ordered by duplication frequency. This class of duplicated objects is relatively easier to optimize. OJXPerf incurs 9% runtime and 6% memory overheads on average. We empirically show the benefit of OJXPerf by using its profiles to instruct us to optimize a number of Java programs, including well-known benchmarks and real-world applications. The results show a noticeable reduction in memory usage (up to 11%) and a significant speedup (up to 25%).