Download or read book Machine Learning Algorithms for Choosing Compiler Heuristics written by Gennady G. Pekhimenko and published by . This book was released on 2008 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Automatic Tuning of Compilers Using Machine Learning written by Amir H. Ashouri and published by Springer. This book was released on 2017-12-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Compiler Construction written by Alan Mycroft and published by Springer Science & Business Media. This book was released on 2006-03-21 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Software Automatic Tuning written by Ken Naono and published by Springer Science & Business Media. This book was released on 2010-09-09 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Genetic Algorithms and Machine Learning for Programmers written by Frances Buontempo and published by Pragmatic Bookshelf. This book was released on 2019-01-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Languages and Compilers for Parallel Computing written by Eduard Ayguadé and published by Springer. This book was released on 2007-05-16 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligence Methodology Systems and Applications written by Doris R. Scott and published by Springer. This book was released on 2003-08-02 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Compiler Construction written by Koen De Bosschere and published by Springer. This book was released on 2013-02-17 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Worst Case Execution Time Aware Compilation Techniques for Real Time Systems written by Paul Lokuciejewski and published by Springer Science & Business Media. This book was released on 2010-09-24 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book High Performance Embedded Architectures and Compilers written by Koen De Bosschere and published by Springer. This book was released on 2007-07-20 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Conference on High Performance Embedded Architectures and Compilers, HiPEAC 2007, held in Ghent, Belgium, in January 2007. The 19 revised full papers presented together with one invited keynote paper were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections.

Download or read book Metaheuristic and Machine Learning Optimization Strategies for Complex Systems written by R., Thanigaivelan and published by IGI Global. This book was released on 2024-07-17 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Languages and Compilers for Parallel Computing written by James Brodman and published by Springer. This book was released on 2015-04-30 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2014, held in Hillsboro, OR, USA, in September 2014. The 25 revised full papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on accelerator programming; algorithms for parallelism; compilers; debugging; vectorization.

Download or read book Instruction Selection written by Gabriel Hjort Blindell and published by Springer. This book was released on 2016-06-03 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive, structured, up-to-date survey on instruction selection. The survey is structured according to two dimensions: approaches to instruction selection from the past 45 years are organized and discussed according to their fundamental principles, and according to the characteristics of the supported machine instructions. The fundamental principles are macro expansion, tree covering, DAG covering, and graph covering. The machine instruction characteristics introduced are single-output, multi-output, disjoint-output, inter-block, and interdependent machine instructions. The survey also examines problems that have yet to be addressed by existing approaches. The book is suitable for advanced undergraduate students in computer science, graduate students, practitioners, and researchers.

Download or read book Languages and Compilers for Parallel Computing written by Hironori Kasahara and published by Springer. This book was released on 2013-04-05 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2012, held in Tokyo, Japan, in September 2012. The 16 revised full papers, 5 poster papers presented with 1 invited talk were carefully reviewed and selected from 39 submissions. The focus of the papers is on following topics: compiling for parallelism, automatic parallelization, optimization of parallel programs, formal analysis and verification of parallel programs, parallel runtime systems, task-parallel libraries, parallel application frameworks, performance analysis tools, debugging tools for parallel programs, parallel algorithms and applications.

Download or read book Metaheuristics for Machine Learning written by Kanak Kalita and published by John Wiley & Sons. This book was released on 2024-03-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.

Download or read book The Compiler Design Handbook written by Y.N. Srikant and published by CRC Press. This book was released on 2018-10-03 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today’s embedded devices and sensor networks are becoming more and more sophisticated, requiring more efficient and highly flexible compilers. Engineers are discovering that many of the compilers in use today are ill-suited to meet the demands of more advanced computer architectures. Updated to include the latest techniques, The Compiler Design Handbook, Second Edition offers a unique opportunity for designers and researchers to update their knowledge, refine their skills, and prepare for emerging innovations. The completely revised handbook includes 14 new chapters addressing topics such as worst case execution time estimation, garbage collection, and energy aware compilation. The editors take special care to consider the growing proliferation of embedded devices, as well as the need for efficient techniques to debug faulty code. New contributors provide additional insight to chapters on register allocation, software pipelining, instruction scheduling, and type systems. Written by top researchers and designers from around the world, The Compiler Design Handbook, Second Edition gives designers the opportunity to incorporate and develop innovative techniques for optimization and code generation.

Download or read book Information Processing and Management of Uncertainty in Knowledge Based Systems written by Joao Paulo Carvalho and published by Springer. This book was released on 2016-06-10 with total page 754 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set (CCIS 610 and 611) constitute the proceedings of the 16th International Conference on Information processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016, held in Eindhoven, The Netherlands, in June 2016. The 127 revised full papers presented together with four invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy measures and integrals; uncertainty quantification with imprecise probability; textual data processing; belief functions theory and its applications; graphical models; fuzzy implications functions; applications in medicine and bioinformatics; real-world applications; soft computing for image processing; clustering; fuzzy logic, formal concept analysis and rough sets; graded and many-valued modal logics; imperfect databases; multiple criteria decision methods; argumentation and belief revision; databases and information systems; conceptual aspects of data aggregation and complex data fusion; fuzzy sets and fuzzy logic; decision support; comparison measures; machine learning; social data processing; temporal data processing; aggregation.