No abstract

Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools.Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describe Milepost GCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the Milepost technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation 1 INRIA Saclay, France (HiPEAC member) · 2 University of Versailles Saint Quentin en Yvelines, France · 3 IBM Haifa, Israel (HiPEAC member) · 4 CAPS Entreprise, France (HiPEAC member) · 5 ARC International, UK · 6 University of Edinburgh, UK (HiPEAC member) · 2 time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor. We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilation time and code size by 12% and 7% respectively on Intel Xeon processor.

Applying the right compiler optimizations to a particular program can have a significant impact on program performance. Due to the non-linear interaction of compiler optimizations, however, determining the best setting is nontrivial. There have been several proposed techniques that search the space of compiler options to find good solutions; however such approaches can be expensive. This paper proposes a different approach using performance counters as a means of determining good compiler optimization settings. This is achieved by learning a model off-line which can then be used to determine good settings for any new program. We show that such an approach outperforms the state-ofthe-art and is two orders of magnitude faster on average. Furthermore, we show that our performance counter-based approach outperforms techniques based on static code features. Using our technique we achieve a 17% improvement over the highest optimization setting of the commercial PathScale EKOPath 2.3.1 optimizing compiler on the SPEC benchmark suite on a recent AMD Athlon 64 3700+ platform.