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Architects are now taking advantage of the computer in new ways through experimentation with algorithmic and simulation-driven design. Computation Works: The Building of Algorithmic Thought focuses on this emerging theme in design practice, showcasing built and soon-to-be-built projects and providing a state of the art in computational design. Computational design is considered to be first a design tool, and second a series of instruments that can be applied in the creation of architecture. It allows architects to incorporate performance analysis and knowledge about material, tectonics and the parameters of production machinery. Moving towards a new role as hybrid practitioners, architects are taking concepts from other disciplines and customising architectural and other CAD software. In addition to the discussion of built projects, a further series of texts examines new custom software instruments. New digital tools provide new modes of representation, new methods of evaluation, and new techniques for design exploration. The development of new computational tools can create more responsive designs, allowing architects to explore new design options and to analyse architectural decisions during the design process. This issue raises important questions such as: How is computation changing the way architects design? Are the design tools and methods related to the result? What is computational design in the context of architectural practice? and How is computation changing the processes of design and construction?
An introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophy Mathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation. With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges. He explains the ideas and motivations leading to key models, notions, and results. In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences. Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics. He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society. For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields. Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyond High-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific discipline Historical accounts of the evolution and motivations of central concepts and models A broad view of the theory of computation's influence on science, technology, and society Extensive bibliography
A variety of programming models relevant to scientists explained, with an emphasis on how programming constructs map to parts of the computer. What makes computer programs fast or slow? To answer this question, we have to get behind the abstractions of programming languages and look at how a computer really works. This book examines and explains a variety of scientific programming models (programming models relevant to scientists) with an emphasis on how programming constructs map to different parts of the computer's architecture. Two themes emerge: program speed and program modularity. Throughout this book, the premise is to "get under the hood," and the discussion is tied to specific programs. The book digs into linkers, compilers, operating systems, and computer architecture to understand how the different parts of the computer interact with programs. It begins with a review of C/C++ and explanations of how libraries, linkers, and Makefiles work. Programming models covered include Pthreads, OpenMP, MPI, TCP/IP, and CUDA.The emphasis on how computers work leads the reader into computer architecture and occasionally into the operating system kernel. The operating system studied is Linux, the preferred platform for scientific computing. Linux is also open source, which allows users to peer into its inner workings. A brief appendix provides a useful table of machines used to time programs. The book's website (https://github.com/divakarvi/bk-spca) has all the programs described in the book as well as a link to the html text.
Perspectives in Computation covers three broad topics: the computation process & its limitations; the search for computational efficiency; & the role of quantum mechanics in computation.
A clear illustration of how parallel computers can be successfully appliedto large-scale scientific computations. This book demonstrates how avariety of applications in physics, biology, mathematics and other scienceswere implemented on real parallel computers to produce new scientificresults. It investigates issues of fine-grained parallelism relevant forfuture supercomputers with particular emphasis on hypercube architecture. The authors describe how they used an experimental approach to configuredifferent massively parallel machines, design and implement basic systemsoftware, and develop algorithms for frequently used mathematicalcomputations. They also devise performance models, measure the performancecharacteristics of several computers, and create a high-performancecomputing facility based exclusively on parallel computers. By addressingall issues involved in scientific problem solving, Parallel ComputingWorks! provides valuable insight into computational science for large-scaleparallel architectures. For those in the sciences, the findings reveal theusefulness of an important experimental tool. Anyone in supercomputing andrelated computational fields will gain a new perspective on the potentialcontributions of parallelism. Includes over 30 full-color illustrations.
Computer Systems Organization -- general.
Human computation is a new and evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. With the growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of people via the Web to perform complex computation. There are various genres of human computation applications that exist today. Games with a purpose (e.g., the ESP Game) specifically target online gamers who generate useful data (e.g., image tags) while playing an enjoyable game. Crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) are human computation systems that coordinate workers to perform tasks in exchange for monetary rewards. In identity verification tasks, users perform computation in order to gain access to some online content; an example is reCAPTCHA, which leverages millions of users who solve CAPTCHAs every day to correct words in books that optical character recognition (OCR) programs fail to recognize with certainty. This book is aimed at achieving four goals: (1) defining human computation as a research area; (2) providing a comprehensive review of existing work; (3) drawing connections to a wide variety of disciplines, including AI, Machine Learning, HCI, Mechanism/Market Design and Psychology, and capturing their unique perspectives on the core research questions in human computation; and (4) suggesting promising research directions for the future. Table of Contents: Introduction / Human Computation Algorithms / Aggregating Outputs / Task Routing / Understanding Workers and Requesters / The Art of Asking Questions / The Future of Human Computation
The second in the Studies in the Design Laboratory epub series produced by the Harvard University Graduate School of Design and the CCA, this publication traces the development of complex computational geometry in the work of Ron Resch. Resch’s strikingly novel generative methods laid the seeds of computational origami, and during the early 1970’s he collaborated in the pioneering computer science department of the University of Utah, a hotbed of early computer graphics. Featuring interviews with Resch’s collaborators, excerpts from his remarkable films, and a consideration of the role of the architect in cross-disciplinary laboratories, this epub argues for Resch as one of the first true computational designers.
Computation permeates our world, but a satisfactory philosophical theory of what it is has been lacking. Gualtiero Piccinini presents a mechanistic account of what makes a physical system a computing system. He argues that computation does not entail representation or information-processing, although information-processing entails computation.