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processor machines [PS00] By 1999, for Toy Story 2, Pixar was using a 1,400 processor system with the improvement in processing power fully reflected in the improved details in textures, clothing, and atmospheric effects Monsters, Inc (2001) used a system of 250 enterprise servers each containing 14 processors for a total of 3,500 processors It is interesting that the amount of time required to generate a frame has remained relatively constant as computing power (both the number of processors and the speed of each processor) has increased, it has been exploited to improve the quality of the animation The biological sciences have taken dramatic leaps forward with the availability of DNA sequence information from a variety of organisms, including humans One approach to sequencing, championed and used with success by Celera Corp, is called the whole genome shotgun algorithm The idea is to break the genome into small segments, experimentally determine the DNA sequences of the segments, and then use a computer to construct the entire sequence from the segments by finding overlapping areas The computing facilities used by Celera to sequence the human genome included 150 four way servers plus a server with 16 processors and 64GB of memory The calculation involved 500 million trillion base to base comparisons [Ein00] The SETI@home project [SET, ACK 02 + ] provides a fascinating example of the power of parallel computing The project seeks evidence of extraterrestrial intelligence by scanning the sky with the world's largest radio telescope, the Arecibo Telescope in Puerto Rico The collected data is then analyzed for candidate signals that might indicate an intelligent source The computational task is beyond even the largest supercomputer, and certainly beyond the capabilities of the facilities available to the SETI@home project The problem is solved with public resource computing, which turns PCs around the world into a huge parallel computer connected by the Internet Data is broken up into work units and distributed over the Internet to client computers whose owners donate spare com puting time to support the project Each client periodically connects with the SETI@home server, downloads the data to analyze, and then sends the results back to the server The client program is typically implemented as a screen saver so that it will devote CPU cycles to the SETI problem only when the computer is otherwise idle A work unit currently requires an average of between seven and eight hours of CPU time on a client More than 205,000,000 work units have been processed since the start of the project More recently, similar technology to that demonstrated by SETI@home has been used for a variety of public resource computing projects as well as internal projects within large companies utilizing their idle PCs to solve problems ranging from drug screening to chip design validation Although computing in less time is beneficial, and may enable problems to be solved that couldn't be otherwise, it comes at a cost Writing software to run on parallel computers can be difficult Only a small minority of programmers have experience with parallel programming If all these computers designed to exploit parallelism are going to achieve their potential, more programmers need to learn how to write parallel programs This book addresses this need by showing competent programmers of sequential machines how to design programs that can run on parallel computers Although many excellent books show how to use particular parallel programming environments, this book is unique in that it focuses on how to think about and design parallel algor ithms To accomplish this goal, we will be using the concept of a pattern language This highly structured representation of expert design experience has been heavily used in the object oriented design community.

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Summary Exercises 14 Introduction to Parsing BNF Syntax and Parsing Terminology Writing Handcrafted Parsers Simple Key Value Data Parsing Playlist Data Parsing Parsing the Blocks Domain-Speci c Language Pythonic Parsing with PyParsing A Quick Introduction to PyParsing Simple Key Value Data Parsing Playlist Data Parsing Parsing the Blocks Domain-Speci c Language Parsing First-Order Logic Lex/Yacc-Style Parsing with PLY Simple Key Value Data Parsing Playlist Data Parsing Parsing the Blocks Domain-Speci c Language Parsing First-Order Logic Summary Exercise 15 Introduction to GUI Programming Dialog-Style Programs Main-Window-Style Programs Creating a Main Window Creating a Custom Dialog Summary Exercises Epilogue Selected Bibliography Index

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The book opens with two introductory chapters The first gives an overview of the parallel computing landscape and background needed to understand and use the pattern language This is followed by a more detailed chapter in which we lay out the basic concepts and jargon used by parallel programmers The book then moves into the pattern language itself

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Entities 23 Attributes 24 Data Types 25 Primary and Foreign Keys 30 Domains 31 Single-Valued and Multivalued Attributes 32 Referential Integrity 32

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Once we have some data types, the next thing we need are variables in which to store them Python doesn t have variables as such, but instead has object references When it comes to immutable objects like ints and strs, there is no discernable difference between a variable and an object reference As for mutable objects, there is a difference, but it rarely matters in practice We will use the terms variable and object reference interchangeably Let s look at a few tiny examples, and then discuss some of the details

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This chapter has given a brief overview of some of the concepts and vocabulary used in parallel computing Additional terms are defined in the glossary We also discussed the major programming environments in use for parallel computing: OpenMP, MPI, and Java Throughout the book, we will use these three programming environments for our examples More details about OpenMP, MPI, and Java and how to use them to write parallel programs are provided in the appendixes

The syntax is simply objectReference = value There is no need for predeclaration and no need to specify the value s type When Python executes the rst statement it creates a str object with the text blue , and creates an object reference called x that refers to the str object For all practical purposes we can say that variable x has been assigned the blue string The second statement is similar The third statement creates a new object reference called z and sets it to refer to the same object that the x object reference refers to (in this case the str containing the text blue ) The = operator is not the same as the variable assignment operator in some other languages The = operator binds an object reference to an object in memory If the object reference already exists, it is simply re-bound to refer to the object on the right of the = operator; if the object reference does not exist it is created by the = operator

Let s continue with the x, y, z example, and do some rebinding as noted earlier, comments begin with a # and continue until the end of the line:

Relationships 35 Relationship Types 35 Relationship Options 40 Cardinality 41 Using Subtypes and Supertypes 42 Supertypes and Subtypes Defined 42 When to Use Subtype Clusters 44 Summary 44

33 THE DATA DECOMPOSITION PATTERN 34 THE GROUP TASKS PATTERN 35 THE ORDER TASKS PATTERN 36 THE DATA SHARING PATTERN 37 THE DESIGN EVALUATION PATTERN 38 SUMMARY

print(x, y, z) # prints: blue green blue z = y print(x, y, z) # prints: blue green green x = z print(x, y, z) # prints: green green green

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