Points-to Analysis Using MDE: A Multi-level Deduplication Engine for Repetitive Data and Operations

2026-04-11

AI Summary

This work addresses the combinatorial explosion in memory and time that plagues scalable flow- and context-sensitive pointer analysis, where existing optimizations often compromise precision. To overcome this challenge, the authors propose a Multi-level Deduplication Engine (MDE) that recursively identifies structured redundancies, assigns unique identifiers to equivalent computation states, and integrates memoization of operations to enable efficient reuse—thereby surpassing the limitations of traditional non-recursive deduplication techniques. Implemented in C++ and integrated into a pointer analysis framework, MDE demonstrates substantial performance gains on the SPEC benchmark suite, achieving up to an 18.1× reduction in peak memory usage and an 8.15× speedup in runtime. Notably, the optimization benefits intensify with increasing program scale, highlighting MDE’s effectiveness for large real-world applications.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Web measurements

Abstract

Precise pointer analysis is a foundational component of many client analyses and optimizations. Scaling flow- and context-sensitive pointer analysis has been a long-standing challenge, suffering from combinatorial growth in both memory usage and runtime. Existing approaches address this primarily by reducing the amount of information tracked often, at the cost of precision and soundness. In our experience a significant proportion of this cost comes from propagation of duplicate data and low-level data structure operations being repeated a large number of times. Our measurements on SPEC benchmarks show that more than 90% of all set-union operations performed can be redundant. We present Multi-level Deduplication Engine (MDE), a mechanism that recursively augments the representation of data through de-duplication and the assignment of unique identifiers to values to eliminate redundancy. This allows MDE to trivialize many operations, and memoize operations enabling their future reuse. MDE's recursive structure allows it to represent de-duplicated values that themselves are constructed from other de-deuplicated values, capturing structural redundancy not easily possible with non-recursive techniques. We provide a full C++ implementation of MDE as a library and integrate it into an existing implementation of a flow- and context-sensitive pointer analysis. Evaluation on selected SPEC benchmarks shows a reduction up to 18.10x in peak memory usage and 8.15x in runtime. More notably, MDE exhibits an upward trend of effectiveness with the increase in benchmark size. Besides performance improvements, this work highlights the importance of representation design and suggests new opportunities for bringing efficiency to future analyses.

Problem

• pointer analysis
• scalability
• redundancy
• memory usage
• runtime

Innovation

• Multi-level Deduplication
• Pointer Analysis
• Memoization
• Structural Redundancy
• Scalable Program Analysis