Transforming flow information during code optimization for timing analysis

Abstract

The steadily growing embedded-systems market comprises many application domains in which real-time constraints must be satisfied. To guarantee that these constraints are met, the analysis of the worst-case execution time (WCET) of software components is mandatory. In general WCET analysis needs additional control-flow information, which may be provided manually by the user or calculated automatically by program analysis. For flexibility and simplicity reasons it is desirable to specify the flow information at the same level at which the program is developed, i.e., at the source level. In contrast, to obtain precise WCET bounds the WCET analysis has to be performed at machine-code level. Mapping and transforming the flow information from the source-level down to the machine code, where flow information is used in the WCET analysis, is challenging, even more so if the compiler generates highly optimized code.In this article we present a method for transforming flow information from source code to machine code. To obtain a mapping that is safe and accurate, flow information is transformed in parallel to code transformations performed by an optimizing compiler. This mapping is not only useful for transforming manual code annotations but also if platform-independent flow information is automatically calculated at the source level. We show that our method can be applied to every type of semantics-preserving code transformation. The precision of this flow-information transformation allows its users to calculate tight WCET bounds.

Tracing Flow Information for Tighter WCET Estimation: Application to Vectorization

“…Compared with their not powerful structure, we can handle most LLVM optimizations, including vectorization. S000 S1111 S1112 S1115 S118 S119 S1119 S132 S152 S173 S176 S1221 S1232 S233 S2233 S251 S1251 S2275 S1281 S1351 S1421 S423 S431 S452 S471 S491 S4112 S4113 S4114 S4117 S4121 vag vas vpv vtv vpvtv vpvts vpvpv vtvtv vbor Average Improvement ra<o S000 S1111 S1112 S1115 S118 S119 S1119 S132 S152 S173 S176 S1221 S1232 S233 S2233 S251 S1251 S2275 S1281 S1351 S1421 S423 S431 S452 S471 S491 S4112 S4113 S4114 S4117 S4121 vag vas vpv vtv vpvtv vpvts vpvpv vtvtv vbor Average Improvement ra<o Later, Kirner et al [16], [17] present a method to transform flow information from source code level to machine code level. The SATIrE [18] system is designed as a source-tosource analysis tool to map source code annotations to the intermediate program representation.…”

Testing Method of Code Redundancy Simplification Based on Program Dependency Graph

“…E. Ozcan proposed a Memetic Algorithm (MA) to arrange the best number of processors and the best data distribution for each stage of a parallel system [11]. R. Kirner studied the Worstcase execution time analysis, particularly emphasizing on execution time [12]. The existing methods of code optimization focus on the compiling process predominantly, possessing low generality.…”

The Real‐Time Systems Compiler: migrating event‐triggered systems to time‐triggered systems

“…Program slicing was employed by Gerber and Gopinath to improve the schedulability of real‐time systems. Kirner , for instance, enhances WCET‐analysis by transforming flow information within optimising compilers. Thereby, loop annotations are also maintained across transformations like loop unrolling.…”