Cole

Abstract

Modern compilers implement a large number of optimizations which all interact in complex ways, and which all have a different impact on code quality, compilation time, code size, energy consumption, etc. For this reason, compilers typically provide a limited number of standard optimization levels, such as -O1, -O2, -O3 and -Os, that combine various optimizations providing a number of trade-offs between multiple objective functions (such as code quality, compilation time and code size). The construction of these optimization levels, i.e., choosing which optimizations to activate at each level, is a manual process typically done using high-level heuristics based on the compiler developer's experience.This paper proposes COLE, Compiler Optimization Level Exploration, a framework for automatically finding Pareto optimal optimization levels through multi-objective evolutionary searching. Our experimental results using GCC and the SPEC CPU benchmarks show that the automatic construction of optimization levels is feasible in practice, and in addition, yields better optimization levels than GCC's manually derived (-Os, -O1, -O2 and -O3) optimization levels, as well as the optimization levels obtained through random sampling. We also demonstrate that COLE can be used to gain insight into the effectiveness of compiler optimizations as well as to better understand a benchmark's sensitivity to compiler optimizations.

The interplay of compile-time and run-time options for performance prediction

“…The problem of choosing which optimizations of a compiler to apply has a long tradition [50]. Since the mid-1990s, machine-learning-based or evolutionary approaches have been investigated to explore the configuration space of compiler [5,20,45,57,58,61,62]. Such works usually consider a limited set of run-time configurations in favor of a broad consideration of inputs (programs).…”

Deep Software Variability: Towards Handling Cross-Layer Configuration

“…Numerous research papers study the effects of variability layers on software's performances or on configuration options: hardware [10,35,43], workloads [19,20,24,30,43], variants [22,37], versions [13,29,37,41], compilation options [15,27] and input data [3,8]. Such studies provide evidence that some layers have a noticeable impact on the software (configuration) layer.…”

“…Various research papers [15,19,20,24,27,43] configure their variability layer to optimize non-functional properties of software, while others, as [8,30], adapt configuration options of software to variability layers. Such works are in line with the vision exposed in Section 3.5 though only one layer at a time is usually considered as part of the specialization process.…”

Tuning compilations by multi-objective optimization: Application to Apache web server

“…MOP in combination with GA have been widely studied before [7,8,9,10]. Authors in [11] also adopted a multi-objective evolutionary search for finding the best compiler options.…”