Foreword

At its inception some 40 or so years ago, parallel computing was the province of experts who applied it to exotic fields, such as high energy physics, and to engineering applications, such as computational fluid dynamics. We've come a long way since those early days.

This change is being driven by hardware trends. The days of perpetually increasing processor clock speeds are now at an end. Instead, the increased chip densities that Moore's Law predicts are being used to create multicore processors, or single chips with multiple processor cores. Quad-core processors are now common, and this trend will continue, with 10's of cores available on the hardware in the not-too-distant future.

In the last five years, Microsoft has taken advantage of this technological shift to create a variety of parallel implementations. These include the Windows High Performance Cluster (HPC) technology for message-passing interface (MPI) programs, Dryad, which offers a Map-Reduce style of parallel data processing, the Windows Azure platform, which can supply compute cores on demand, the Parallel Patterns Library (PPL) for native code, and the parallel extensions of the .NET Framework 4.

Multicore computation affects the whole spectrum of applications, from complex scientific and design problems to consumer applications and new human/computer interfaces. We used to joke that "parallel computing is the future, and always will be," but the pessimists have been proven wrong. Parallel computing has at last moved from being a niche technology to being center stage for both application developers and the IT industry.

But, there is a catch. To obtain any speed-up of an application, programmers now have to divide the computational work to make efficient use of the power of multicore processors, a skill that still belongs to experts. Parallel programming presents a massive challenge for the majority of developers, many of whom are encountering it for the first time. There is an urgent need to educate them in practical ways so that they can incorporate parallelism into their applications.

Two possible approaches are popular with some of my computer science colleagues: either design a new parallel programming language or develop a "heroic" parallelizing compiler. While both are certainly interesting academically, neither has had much success in popularizing and simplifying the task of parallel programming for non-experts. In contrast, a more pragmatic approach is to provide programmers with a library that hides much of parallel programming's complexity and to teach programmers how to use it.

To that end, the Microsoft .NET Framework parallel extensions present a higher-level programming model than earlier APIs. Programmers can, for example, think in terms of tasks rather than threads and can avoid the complexities of managing threads. Parallel Programming with Microsoft .NET teaches programmers how to use these libraries by putting them in the context of design patterns. As a result, application developers can quickly learn to write parallel programs and gain immediate performance benefits.

I believe that this book, with its emphasis on parallel design patterns and an up-to-date programming model, represents an important first step in moving parallel programming into the mainstream.

Tony Hey

Corporate Vice President, Microsoft Research

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