Deep Data Flow Analysis

Chris Cummins, Zacharias Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather, Michael O'Boyle

Keywords: program representations, program analysis, compilers, graph neural networks

Abstract:

Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of program, architecture, and other optimizations, is alluring. However, most machine learning methods cannot replicate even the simplest of the abstract interpretations of data flow analysis that are critical to making good optimization decisions. This must change for machine learning to become the dominant technology in compiler heuristics.

To this end, we propose ProGraML - Program Graphs for Machine Learning - a language-independent, portable representation of whole-program semantics for deep learning. To benchmark current and future learning techniques for compiler analyses we introduce an open dataset of 461k Intermediate Representation (IR) files for LLVM, covering five source programming languages, and 15.4M corresponding data flow results. We formulate data flow analysis as an MPNN and show that, using ProGraML, standard analyses can be learned, yielding improved performance on downstream compiler optimization tasks.

One-sentence Summary: A graph representation for programs that enables more powerful reasoning about program semantics and improved performance on downstream compiler tasks.

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Reviewed Version (pdf): https://openreview.net/references/pdf?id=D0OE7b_GEp