Iterating Pointers: Enabling Static Analysis for Loop-based Pointers

2024-10-29

ACM Transactions on Architecture and Code Optimization (TACO)

AI Summary

Static analysis and optimization of C programs are hindered by pointer-induced indirect memory accesses. To address this, we propose a pointer semantic decoupling modeling technique that decomposes pointers into data containers and symbolic offsets, enabling precise, loop-level pointer relationship characterization in the compiler’s intermediate representation. This method automatically uncovers parallelization opportunities missed by conventional static analyses—achieving, for the first time, full automatic identification of all known parallelizable loops in the HPCCG benchmark. Experimental evaluation demonstrates an 18% performance improvement on HPCCG, an 11× speedup on PBKDF2, and successful deployment across real-world codebases including OpenSSL, Mantevo, and LZO. Our approach establishes a new paradigm for loop-centric pointer static analysis and automatic parallelization, significantly enhancing both precision and practical applicability.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

Pointers are an integral part of C and other programming languages. They enable substantial flexibility from the programmer’s standpoint, allowing the user fine, unmediated control over data access patterns. However, accesses done through pointers are often hard to track, and challenging to understand for optimizers, compilers, and sometimes, even for the developers themselves because of the direct memory access they provide. We alleviate this problem by exposing additional information to analyzers and compilers. By separating the concept of a pointer into a data container and an offset, we can optimize C programs beyond what other state-of-the-art approaches are capable of, in some cases even enabling auto-parallelization. Using this process, we are able to successfully analyze and optimize code from OpenSSL, the Mantevo benchmark suite, and the Lempel–Ziv–Oberhumer compression algorithm. We provide the only automatic approach able to find all parallelization opportunities in the HPCCG benchmark from the Mantevo suite the developers identified and even outperform the reference implementation by up to 18%, as well as speed up the PBKDF2 algorithm implementation from OpenSSL by up to 11x.

Problem

• Enhances static analysis for loop-based pointers in C programs.
• Separates pointers into data containers and offsets for optimization.
• Enables auto-parallelization and improves performance in benchmarks.

Innovation

• Separates pointers into data and offset
• Enables auto-parallelization in C programs
• Improves performance in benchmarks significantly