48 research outputs found
Machine learning in compilers
Tuning a compiler so that it produces optimised code is a difficult task because modern processors
are complicated; they have a large number of components operating in parallel and each
is sensitive to the behaviour of the others. Building analytical models on which optimisation
heuristics can be based has become harder as processor complexity increased and this trend is
bound to continue as the world moves towards further heterogeneous parallelism. Compiler
writers need to spend months to get a heuristic right for any particular architecture and these
days compilers often support a wide range of disparate devices. Whenever a new processor
comes out, even if derived from a previous one, the compilerâs heuristics will need to be retuned
for it. This is, typically, too much effort and so, in fact, most compilers are out of date.
Machine learning has been shown to help; by running example programs, compiled in
different ways, and observing how those ways effect program run-time, automatic machine
learning tools can predict good settings with which to compile new, as yet unseen programs.
The field is nascent, but has demonstrated significant results already and promises a day when
compilers will be tuned for new hardware without the need for months of compiler expertsâ
time. Many hurdles still remain, however, and while experts no longer have to worry about
the details of heuristic parameters, they must spend their time on the details of the machine
learning process instead to get the full benefits of the approach.
This thesis aims to remove some of the aspects of machine learning based compilers for
which human experts are still required, paving the way for a completely automatic, retuning
compiler.
First, we tackle the most conspicuous area of human involvement; feature generation. In all
previous machine learning works for compilers, the features, which describe the important aspects
of each example to the machine learning tools, must be constructed by an expert. Should
that expert choose features poorly, they will miss crucial information without which the machine
learning algorithm can never excel. We show that not only can we automatically derive
good features, but that these features out perform those of human experts. We demonstrate our
approach on loop unrolling, and find we do better than previous work, obtaining XXX% of the
available performance, more than the XXX% of previous state of the art.
Next, we demonstrate a new method to efficiently capture the raw data needed for machine
learning tasks. The iterative compilation on which machine learning in compilers depends is
typically time consuming, often requiring months of compute time. The underlying processes
are also noisy, so that most prior works fall into two categories; those which attempt to gather
clean data by executing a large number of times and those which ignore the statistical validity
of their data to keep experiment times feasible. Our approach, on the other hand guarantees
clean data while adapting to the experiment at hand, needing an order of magnitude less work
that prior techniques
Music in Evolution and Evolution in Music
Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the âevolutionary algorithmâ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwinâs famous theory, and vice-versa.
Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon.
A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives
Challenges and perspectives of hate speech research
This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research
UMSL Bulletin 2020-2021
The 2020-2021 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1084/thumbnail.jp
UMSL Bulletin 2019-2020
The University Bulletin/Course Catalog 2019-2020 Edition.https://irl.umsl.edu/bulletin/1083/thumbnail.jp
Play Among Books
How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an âinfinite flowâ of real books