A Pattern Language for Parallel Programming in Java

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1 A Pattern Language for Parallel Programming
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Computers are used to model physical systems in many fields of science, medicine, and engineering Modelers, whether trying to predict the weather or render a scene in the next blockbuster movie, can usually use whatever computing power is available to make ever more detailed simulations Vast amounts of data, whether customer shopping patterns, telemetry data from space, or DNA sequences, require analysis To deliver the required power, computer designers combine multiple processing elements into a single larger system These so called parallel computers run multiple tasks simultaneously and solve bigger problems in less time Traditionally, parallel computers were rare and available for only the most critical problems Since the mid 1990s, however, the availability of parallel computers has changed dramatically With multithreading support built into the latest microprocessors and the emergence of multiple processor cores on a single silicon die, parallel computers are becoming ubiquitous Now, almost every university computer science department has at least one parallel computer Virtually all oil companies, automobile manufacturers, drug development companies, and special effects studios use parallel computing For example, in computer animation, rendering is the step where information from the animation files, such as lighting, textures, and shading, is applied to 3D models to generate the 2D image that makes up a frame of the film Parallel computing is essential to generate the needed number of frames (24 per second) for a feature length film Toy Story, the first completely computer generated feature length film, released by Pixar in 1995, was processed on a "renderfarm" consisting of 100 dual
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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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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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