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Combinatorial Scientific Computing explores the latest research on
creating algorithms and software tools to solve key combinatorial
problems on large-scale high-performance computing architectures.
It includes contributions from international researchers who are
pioneers in designing software and applications for
high-performance computing systems. The book offers a
state-of-the-art overview of the latest research, tool development,
and applications. It focuses on load balancing and parallelization
on high-performance computers, large-scale optimization,
algorithmic differentiation of numerical simulation code, sparse
matrix software tools, and combinatorial challenges and
applications in large-scale social networks. The authors unify
these seemingly disparate areas through a common set of
abstractions and algorithms based on combinatorics, graphs, and
hypergraphs. Combinatorial algorithms have long played a crucial
enabling role in scientific and engineering computations and their
importance continues to grow with the demands of new applications
and advanced architectures. By addressing current challenges in the
field, this volume sets the stage for the accelerated development
and deployment of fundamental enabling technologies in
high-performance scientific computing.
A survey book focusing on the key relationships and synergies
between automatic differentiation (AD) tools and other software
tools, such as compilers and parallelizers, as well as their
applications. The key objective is to survey the field and present
the recent developments. In doing so the topics covered shed light
on a variety of perspectives. They reflect the mathematical
aspects, such as the differentiation of iterative processes, and
the analysis of nonsmooth code. They cover the scientific
programming aspects, such as the use of adjoints in optimization
and the propagation of rounding errors. They also cover
"implementation" problems.
The Fifth International Conference on Automatic Differentiation
held from August 11 to 15, 2008 in Bonn, Germany, is the most
recent one in a series that began in Breckenridge, USA, in 1991 and
continued in Santa Fe, USA, in 1996, Nice, France, in 2000 and
Chicago, USA, in 2004. The 31 papers included in these proceedings
re?ect the state of the art in automatic differentiation (AD) with
respect to theory, applications, and tool development. Overall, 53
authors from institutions in 9 countries contributed, demonstrating
the worldwide acceptance of AD technology in computational science.
Recently it was shown that the problem underlying AD is indeed
NP-hard, f- mally proving the inherently challenging nature of this
technology. So, most likely, no deterministic "silver bullet"
polynomial algorithm can be devised that delivers optimum
performance for general codes. In this context, the exploitation of
doma- speci?c structural information is a driving issue in
advancing practical AD tool and algorithm development. This trend
is prominently re?ected in many of the pub- cations in this volume,
not only in a better understanding of the interplay of AD and
certain mathematical paradigms, but in particular in the use of
hierarchical AD approaches that judiciously employ general AD
techniques in application-speci?c - gorithmic harnesses. In this
context, the understanding of structures such as sparsity of
derivatives, or generalizations of this concept like scarcity,
plays a critical role, in particular for higher derivative
computations.
The Fourth International Conference on Automatic Di?erentiation was
held
July20-23inChicago,Illinois.Theconferenceincludedaonedayshortcourse,
42 presentations, and a workshop for tool developers. This
gathering of au- matic di?erentiation researchers extended a
sequence that began in Breck- ridge, Colorado, in 1991 and
continued in Santa Fe, New Mexico, in 1996 and Nice, France, in
2000. We invited conference participants and the general - tomatic
di?erentiation community to submit papers to this special
collection.
The28acceptedpapersre?ectthestateoftheartinautomaticdi?erentiation.
The number of automatic di?erentiation tools based on compiler
techn- ogy continues to expand. The papers in this volume discuss
the implem- tation and application of several compiler-based tools
for Fortran, including the venerable ADIFOR, an extended NAGWare
compiler, TAF, and TAPE- NADE. While great progress has been made
toward robust, compiler-based tools for C/C++, most notably in the
form of the ADIC and TAC++ tools, for now operator-overloading
tools such as ADOL-C remain the undisputed champions for
reverse-mode automatic di?erentiation of C++. Tools for - tomatic
di?erentiation of high level languages, including COSY and ADiMat,
continue to grow in importance as the productivity gains o? ered by
high-level programming are recognized.
Automatic Differentiation (AD) is a maturing computational technology and has become a mainstream tool used by practicing scientists and computer engineers. The rapid advance of hardware computing power and AD tools has enabled practitioners to quickly generate derivative-enhanced versions of their code for a broad range of applications in applied research and development. "Automatic Differentiation of Algorithms" provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. The book covers all aspects of the subject: mathematics, scientific programming ( i.e., use of adjoints in optimization) and implementation (i.e., memory management problems). A strong theme of the book is the relationships between AD tools and other software tools, such as compilers and parallelizers. A rich variety of significant applications are presented as well, including optimum-shape design problems, for which AD offers more efficient tools and techniques. Topics and features: * helpful introductory AD survey chapter for brief overview of the field *extensive applications chapters, i.e., for circuit simulation, optimization and optimal-control shape design, structural mechanics, and multibody dynamical systems modeling *comprehensive bibliography for all current literature and results for the field *performance issues *optimal control sensitivity analysis *AD use with object oriented software tool kits The book is an ideal and accessible survey of recent developments and applications of AD tools and techniques for a broad scientific computing and computer engineering readership. Practitioners, professionals, and advanced graduates working in AD development will find the book a useful reference and essential resource for their work.
Combinatorial Scientific Computing explores the latest research on
creating algorithms and software tools to solve key combinatorial
problems on large-scale high-performance computing architectures.
It includes contributions from international researchers who are
pioneers in designing software and applications for
high-performance computing systems. The book offers a
state-of-the-art overview of the latest research, tool development,
and applications. It focuses on load balancing and parallelization
on high-performance computers, large-scale optimization,
algorithmic differentiation of numerical simulation code, sparse
matrix software tools, and combinatorial challenges and
applications in large-scale social networks. The authors unify
these seemingly disparate areas through a common set of
abstractions and algorithms based on combinatorics, graphs, and
hypergraphs. Combinatorial algorithms have long played a crucial
enabling role in scientific and engineering computations and their
importance continues to grow with the demands of new applications
and advanced architectures. By addressing current challenges in the
field, this volume sets the stage for the accelerated development
and deployment of fundamental enabling technologies in
high-performance scientific computing.
This is the first entry-level book on algorithmic (also known as
automatic) differentiation (AD), providing fundamental rules for
the generation of first- and higher-order tangent-linear and
adjoint code. The author covers the mathematical underpinnings as
well as applications to real-world numerical simulation programs.
Readers will find: * Many examples and exercises, including hints
to solutions * The prototype AD tools dco and dcc for use with the
examples and exercises * First- and higher-order tangent-linear and
adjoint modes for a limited subset of C/C++, provided by the
derivative code compiler dcc * A supplementary website containing
sources of all software discussed in the book, additional exercises
and comments on their solutions (growing over the coming years),
links to other sites on AD, and errata. Ideal for undergraduate and
graduate students, the book is also suitable for researchers and
developers at all levels who need an introduction to AD.
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