Software Automatic Tuning
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Software Automatic Tuning
Author: Ken Naono
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-09-09
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.
Rise of Auto-Tune
""Rise of Auto-Tune"" examines the profound influence of Auto-Tune on contemporary music, moving beyond its basic function as a pitch correction tool to explore its impact on vocal aesthetics, musical authenticity, and the music industry. The book charts the technology's journey from a subtle studio aid to a defining element in popular music, investigating how it has reshaped performance and perceptions of musical talent. An intriguing insight is how Auto-Tune has not only corrected pitch but also opened new creative avenues, leading to iconic vocal styles and launching the careers of artists synonymous with the technology. The book begins by demystifying the science behind Auto-Tune, providing a foundation for understanding its capabilities. It then progresses chronologically, tracing its adoption within the music industry and examining case studies of artists who pioneered its use in genres like hip-hop and R&B. The narrative is supported by interviews with producers, engineers, and artists, offering a multifaceted perspective on Auto-Tune's impact. It also delves into the ethical and aesthetic debates surrounding Auto-Tune, considering its role in shaping our understanding of music and identity in the digital age.
Automatic Tuning of Compilers Using Machine Learning
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.