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During the last several decades compiler developers have invented a set of powerful heuristics to deal with the complexity of the algorithms they have to use. However, this led to a new problem of finding the best values for every heuristic. This paper describes how machine learning techniques, such as logistic regression, can be used to build a framework for the automatic tuning of compiler heuristics. In this paper we were focused on decreasing the compile time for the static commercial compiler called TPO (Toronto Portable Optimizer) while preserving the execution time. Nevertheless, our techniques can also be used for decreasing the execution time and in dynamic behavior. Our experiments showed that we can speedup the compile process by at least a factor of two with almost the same generated code quality on the SPEC2000 benchmark suite, and that our logistic classifier achieves the same prediction quality for non-SPEC benchmarks.
This book explores break-through approaches to tackling and mitigating the well-known problems of compiler optimization using design space exploration and machine learning techniques. It demonstrates that not all the optimization passes are suitable for use within an optimization sequence and that, in fact, many of the available passes tend to counteract one another. After providing a comprehensive survey of currently available methodologies, including many experimental comparisons with state-of-the-art compiler frameworks, the book describes new approaches to solving the problem of selecting the best compiler optimizations and the phase-ordering problem, allowing readers to overcome the enormous complexity of choosing the right order of optimizations for each code segment in an application. As such, the book offers a valuable resource for a broad readership, including researchers interested in Computer Architecture, Electronic Design Automation and Machine Learning, as well as computer architects and compiler developers.
This book constitutes the refereed proceedings of the 15th International Conference on Compiler Construction, CC 2006, held in March 2006 as part of ETAPS. The 17 revised full papers presented together with three tool demonstration papers and one invited paper were carefully reviewed and selected from 71 submissions. The papers are organized in topical sections.
Automatic Performance Tuning is a new software paradigm which enables software to be high performance in any computing environment. Its methodologies have been developed over the past decade, and it is now rapidly growing in terms of its scope and applicability, as well as in its scientific knowledge and technological methods. Software developers and researchers in the area of scientific and technical computing, high performance database systems, optimized compilers, high performance systems software, and low-power computing will find this book to be an invaluable reference to this powerful new paradigm.
This book constitutes the refereed proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Appliations, AIMSA 2002, held in Varna, Bulgaria in September 2002. The 26 revised full papers presented together with 2 invited papers were carefully reviewed and selected for inclusion in this book. The papers address a broad spectrum of topics in AI, including natural language processing, computational learning, Machine learning, AI planning, heuristics, neural information processing, adaptive systems, computational linguistics, multi-agent systems, AI logic, knowledge management, and information retrieval.
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
This book constitutes the thoroughly refereed post-proceedings of the 18th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2005, held in Hawthorne, NY, USA in October 2005. The 26 revised full papers and eight short papers presented were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections.
This book constitutes the proceedings of the 22nd International Conference on Compiler Construction, CC 2013, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, which took place in Rome, Italy, in March 2013. The 13 papers presented in this book were carefully reviewed and selected from 53 submissions. They have been organized into five topical sections on register allocation, pointer analysis, data and information flow, machine learning, and refactoring.
For real-time systems, the worst-case execution time (WCET) is the key objective to be considered. Traditionally, code for real-time systems is generated without taking this objective into account and the WCET is computed only after code generation. Worst-Case Execution Time Aware Compilation Techniques for Real-Time Systems presents the first comprehensive approach integrating WCET considerations into the code generation process. Based on the proposed reconciliation between a compiler and a timing analyzer, a wide range of novel optimization techniques is provided. Among others, the techniques cover source code and assembly level optimizations, exploit machine learning techniques and address the design of modern systems that have to meet multiple objectives. Using these optimizations, the WCET of real-time applications can be reduced by about 30% to 45% on the average. This opens opportunities for decreasing clock speeds, costs and energy consumption of embedded processors. The proposed techniques can be used for all types real-time systems, including automotive and avionics IT systems.
In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.