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Reconstructing Control Flow from Predicated Assembly Code

  • Björn Decker
  • Daniel Kästner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2826)

Abstract

Predicated instructions are a feature more and more common in contemporary instruction set architectures. Machine instructions are only executed if an individual guard register associated with the instruction evaluates to true. This enhances execution efficiency, but comes at a price: the control flow of a program is not explicit any more. Instead instructions from the same basic block may belong to different execution paths if they are subject to disjoint guard predicates. Postpass tools processing machine code with the purpose of program analyses or optimizations require the control flow graph of the input program to be known. The effectiveness of postpass analyses and optimizations strongly depends on the precision of the control flow reconstruction. If traditional reconstruction techniques are applied for processors with predicated instructions, their precision is seriously deteriorated. In this paper a generic algorithm is presented that can precisely reconstruct control flow from predicated assembly code. The algorithm is incorporated in the Propan system that enables high-quality machine-dependent postpass optimizers to be generated from a concise hardware specification. The control flow reconstruction algorithm is machine-independent, and automatically derives the required hardware-specific knowledge from the machine specification. Experimental results obtained for the Philips TriMedia TM1000 processor show that the precision of the reconstructed control flow is significantly higher than with reconstruction algorithms that do not specifically take predicated instructions into account.

Keywords

Basic Block Instruction Variation Input Program Target Architecture Explicit Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Björn Decker
    • 1
  • Daniel Kästner
    • 2
  1. 1.Saarland University 
  2. 2.AbsInt GmbH 

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