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One way to solve a problem fast is to break the problem into pieces, and arrange for all the pieces to be solved simultaneously. The more pieces, the faster the job goes-up to a point where the pieces become too small to make the effort of breaking-up and distributing worth the bother. These simple, obvious observations underlie all of parallel programming. A "parallel program" is a program that uses the breaking-up and handing-out approach to solve large or difficult problems. This book's master plan is to transform "ordinary" programmers into parallel programmers.

Parallelism, we claimed, is one way to solve problems fast. Actually, it is the way. Throughout human problem-solving history, complex engineering and organizational problems have been attacked and mastered by using parallelism. Complex organizations are managed, large buildings built and formidable enemies defeated by bunches of workers, not by isolated actors. A watch, a steam engine or a factory is built up out of many simultaneously-active components.

Parallelism is the norm; purely sequential problem solving is the anomalous restriction. In the early history of computing, the anomaly developed for good reasons. Processors were expensive, and throwing many of them at one problem rarely made sense. More important, people mistook programming for the mere transcription of algorithms into machine-executable form. We understand now that building software machinery is an organizational and engineering challenge of the first order.

All of which is not to say that sequential programming is no longer important. It will remain the technique of choice for solving small problems; it is the indispensable basis for the parallel techniques we discuss in this book. It remains fundamental. When you learn how to paint, you don't forget how to draw. But no serious programmer will want to restrict his technical capabilities to black and white now that color has arrived.

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