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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.

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Abstract. Heterogeneous architectures are currently widespread. With the advent of easy-to-program general purpose GPUs, virtually every recent desktop computer is a heterogeneous system. Combining the CPU and the GPU brings great amounts of processing power. However, such architectures are often used in a restricted way for domain-specific applications like scientific applications and games, and they tend to be used by a single application at a time. We envision future heterogeneous computing systems where all their heterogeneous resources are continuously utilized by different applications with versioned critical parts to be able to better adapt their behavior and improve execution time, power consumption, response time and other constraints at runtime. Under such a model, adaptive scheduling becomes a critical component. In this paper, we propose a novel predictive user-level scheduler based on past performance history for heterogeneous systems. We developed several scheduling policies and present the study of their impact on system performance. We demonstrate that such scheduler allows multiple applications to fully utilize all available processing resources in CPU/GPUlike systems and consistently achieve speedups ranging from 30% to 40% compared to just using the GPU in a single application mode.

Abstract. Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material.We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material at c-mind.org/repo to set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community.

Iterative optimization is a popular compiler optimization approach that has been studied extensively over the past decade. In this article, we deconstruct iterative optimization by evaluating whether it works across datasets and by analyzing why it works.Up to now, most iterative optimization studies are based on a premise which was never truly evaluated: that it is possible to learn the best compiler optimizations across datasets. In this article, we evaluate this question for the first time with a very large number of datasets. We therefore compose KDataSets, a dataset suite with 1000 datasets for 32 programs, which we release to the public. We characterize the diversity of KDataSets, and subsequently use it to evaluate iterative optimization. For all 32 programs, we find that there exists at least one combination of compiler optimizations that achieves at least 83% or more of the best possible speedup across all datasets on two widely used compilers (Intel's ICC and GNU's GCC). This optimal combination is program-specific and yields speedups up to 3.75× (averaged across datasets of a program) over the highest optimization level of the compilers (-O3 for GCC and -fast for ICC). This finding suggests that optimizing programs across datasets might be much easier than previously anticipated.In addition, we evaluate the idea of introducing compiler choice as part of iterative optimization. We find that it can further improve the performance of iterative optimization because different programs favor different compilers. We also investigate why iterative optimization works by analyzing the optimal combinations. We find that only a handful optimizations yield most of the speedup. Finally, we show that optimizations interact in a complex and sometimes counterintuitive way through two case studies, which confirms that iterative optimization is an irreplaceable and important compiler strategy.

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