Backwards Data-Flow Analysis using Prophecy Variables in the BuildIt System

2026-01-06

arXiv.org

AI Summary

This work proposes a novel approach within the BuildIt system to circumvent the engineering complexity and high cost of traditional program optimization, which relies on backward data-flow analysis to obtain future execution information. Instead of performing backward analysis, the method introduces oracle variables that enable multiple forward executions to predict future program behavior. This is the first application of oracle variables to support optimizations requiring future information, eliminating the need for intermediate representations or custom parsers. Consequently, it significantly reduces implementation overhead for embedded domain-specific languages (DSLs) built atop C++. Integrated with staged compilation and code generation targeting C, C++, and CUDA, experimental results demonstrate that the approach not only enhances performance but also substantially decreases the engineering effort required for DSL development.

Technology Category

• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Many program transformations and optimizations require information about the future behavior of the program. A standard way to obtain this information is to build an intermediate program representation, then use a backwards program analysis to propagate relevant information against the flow of control back to the transformation/optimization site. We instead propose to use prophecy variables, which predict information about the future execution of the program, to enable such transformations and optimizations. We implement prophecy variables in BuildIt, a lightweight domain specific language implementation system. BuildIt uses staged compilation to implement high performance domain specific languages embedded within a standard general purpose programming language (C++). The BuildIt first phase uses standard C++ program execution to generate optimized C, C++, and CUDA second phase code. This approach enables BuildIt to eliminate programming language implementation components such as parsers and intermediate representations, delivering a dramatic decrease in the engineering effort required to implement domain specific languages. The combination of prophecy variables and repeated forward program execution enables BuildIt to extend this approach to include transformations and optimizations that require information about the future execution of the program without backwards analyses and without the engineering overhead associated with implementing these analyses. We formalize the use of prophecy variables for this purpose, discuss the implementation of prophecy variables and repeated execution in BuildIt, and present experimental results for BuildIt computations that benefit from optimizations enabled by the information that prophecy variables provide.

Problem

• backwards data-flow analysis
• prophecy variable
• program optimization
• domain-specific languages
• future execution information

Innovation

• prophecy variables
• backwards data-flow analysis
• staged compilation
• domain-specific languages
• program optimization