Program Behavior Characterization Through Advanced Kernel Recognition

Abstract

[Abstract] Understanding program behavior is at the foundation of program optimization. Techniques for automatic recognition of program constructs (from now on, computational kernels) characterize the behavior of program statements, providing compilers with valuable information to to guide code optimization. Our goal is to develop automatic techniques that summarize the behavior of full-scale real applications by building a high-level representation that hides the complexity of implementation details. The first step towards this goal is the description of applications in terms of computational kernels such as induction variables, reductions, and array recurrences. To this end we use XARK, a compiler framework that recognizes a comprehensive collection of frequently used kernels. This paper presents detailed experiments that describe several benchmarks from different application domains in terms of the kernels recognized by XARK. More specifically, the SparsKit-II library for the manipulation of sparse matrices, the Perfect benchmarks, the SPEC CPU2000 collection and the PLTMG package for solving elliptic partial differential equations are characterized in detail.