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Graph Drawing - 10th International Symposium, GD 2002, Irvine, CA, USA, August 26-28, 2002, Revised Papers (Paperback, 2002 ed.)
Stephen G. Kobourov, Michael T Goodrich
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R1,730
Discovery Miles 17 300
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed post-proceedings of the 10th International Symposium on Graph Drawing, GD 2002, held in Irvine, CA, USA, in August 2002.The 24 revised full papers, 9 short papers, and 7 software demonstrations presented together with a report on the GD 2002 graph drawing contest were carefully reviewed and selected from a total of 48 regular paper submissions. All current aspects of graph drawing are addressed.
Symmetric multiprocessors (SMPs) dominate the high-end server
market and are currently the primary candidate for constructing
large scale multiprocessor systems. Yet, the design of e cient
parallel algorithms for this platform c- rently poses several
challenges. The reason for this is that the rapid progress in
microprocessor speed has left main memory access as the primary
limitation to SMP performance. Since memory is the bottleneck,
simply increasing the n- ber of processors will not necessarily
yield better performance. Indeed, memory bus limitations typically
limit the size of SMPs to 16 processors. This has at least
twoimplicationsfor the algorithmdesigner. First, since there are
relatively few processors availableon an SMP, any parallel
algorithm must be competitive with its sequential counterpart with
as little as one processor in order to be r- evant. Second, for the
parallel algorithm to scale with the number of processors, it must
be designed with careful attention to minimizing the number and
type of main memory accesses. In this paper, we present a
computational model for designing e cient al- rithms for symmetric
multiprocessors. We then use this model to create e cient solutions
to two widely di erent types of problems - linked list pre x com-
tations and generalized sorting. Both problems are memory
intensive, but in die rent ways. Whereas generalized sorting
algorithms typically require a large numberofmemoryaccesses, they
areusuallytocontiguousmemorylocations. By contrast, prex
computation algorithms typically require a more modest qu- tity of
memory accesses, but they are are usually to non-contiguous memory
locations.
Based on the authors market leading data structures books in Java
and C++, this book offers a comprehensive, definitive introduction
to data structures in Python by authoritative authors. Data
Structures and Algorithms in Python is the first authoritative
object-oriented book available for Python data structures. Designed
to provide a comprehensive introduction to data structures and
algorithms, including their design, analysis, and implementation,
the text will maintain the same general structure as Data
Structures and Algorithms in Java and Data Structures and
Algorithms in C++. * Begins by discussing Python s conceptually
simple syntax, which allows for a greater focus on concepts. *
Employs a consistent object-oriented viewpoint throughout the text.
* Presents each data structure using ADTs and their respective
implementations and introduces important design patterns as a means
to organize those implementations into classes, methods, and
objects. * Provides a thorough discussion on the analysis and
design of fundamental data structures. * Includes many helpful
Python code examples, with source code provided on the website. *
Uses illustrations to present data structures and algorithms, as
well as their analysis, in a clear, visual manner. * Provides
hundreds of exercises that promote creativity, help readers learn
how to think like programmers, and reinforce important concepts. *
Contains many Python-code and pseudo-code fragments, and hundreds
of exercises, which are divided into roughly 40% reinforcement
exercises, 40% creativity exercises, and 20% programming projects.
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