Your CPU meter shows a problem. One core is running at 100 percent, but all
the other cores are idle. Your application is CPU-bound, but you are using
only a fraction of the computing power of your multicore system. Is there
a way to get better performance?

The answer, in a nutshell, is parallel programming. Where you once would
have written the kind of sequential code that is familiar to all programmers,
you now find that this no longer meets your performance goals. To use your
system.s CPU resources efficiently, you need to split your application into
pieces that can run at the same time. Of course, this is easier said than
done. Parallel programming has a reputation for being the domain of experts
and a minefield of subtle, hard-to-reproduce software defects. Everyone
seems to have a favorite story about a parallel program that did not behave
as expected because of a mysterious bug.

These stories should inspire a healthy respect for the difficulty of the
problems you will face in writing your own parallel programs. Fortunately,
help has arrived. The Parallel Patterns Library (PPL) and the Asynchronous
Agents Library introduce a new programming model for parallelism that
significantly simplifies the job. Behind the scenes are sophisticated
algorithms that dynamically distribute computations on multicore
architectures. Proven design patterns are another source of help. This
guide introduces you to the most important and frequently used patterns
of parallel programming and provides executable code samples for them,
using PPL. When thinking about where to begin, a good place to start is
to review the patterns in this book. See if your problem has any attributes
that match the six patterns presented in the following chapters. If it
does, delve more deeply into the relevant pattern or patterns and study
the sample code.

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