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LLVM Static Analysis for Program Characterization and Memory Reuse Profile Estimation by Barai, Atanu; Santhi, Nandakishore; Razzak, Abdur; Eidenbenz, Stephan; Badawy, Abdel-Hameed A. is a scholarly article available to read on EtoBox.

What is LLVM Static Analysis for Program Characterization and Memory Reuse Profile Estimation about?

Profiling various application characteristics, including the number of different arithmetic operations performed, memory footprint, etc., dynamically is time- and space-consuming. On the other hand, static analysis methods, although fast, can be less accurate. This paper presents an LLVM-based probabilistic static analysis method that accurately predicts different program characteristics and estimates the reuse distance profile of a program by analyzing the LLVM IR file in constant time, regardless of program input size. We generate the basic-block-level control flow graph of the target application kernel and determine basic-block execution counts by solving the linear balance equation involving the adjacent basic blocks' transition probabilities. Finally, we represent the kernel memory accesses in a bracketed format and employ a recursive algorithm to calculate the reuse distance profile. The results show that our approach can predict application characteristics accurately compared to another LLVM-based dynamic code analysis tool, Byfl.

Author

Barai, Atanu; Santhi, Nandakishore; Razzak, Abdur; Eidenbenz, Stephan; Badawy, Abdel-Hameed A.

Published

2023

Language

EN