The Art of Multiprocessor Programming Mar 2008
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Morgan Kaufmann Publishers | ISBN: 978-0-12-370591-4 | Pages:529 | English | PDF | Size: 8.0 MB | RAR-Commpressed : 4.1 MB | No Password
Introduction
The computer industry is undergoing, if not another revolution, certainly a vigorous shaking-up. The major chip manufacturers have, for the time being at least, given up trying to make processors run faster. Moore’s Law has not been repealed: each year, more and more transistors fit into the same space, but their clock speed cannot be increased without overheating. Instead, manufacturers are turning to “multicore” architectures, in which multiple processors (cores) communicate directly through shared hardware caches. Multiprocessor chips make computing more effective by exploiting parallelism: harnessing multiple processors to work on a single task. The spread of multiprocessor architectures will have a pervasive effect on how we develop software. Until recently, advances in technology meant advances in clock speed, so software would effectively “speed up” by itself over time. Now, however, this free ride is over. Advances in technology will mean increased parallelism and not increased clock speed, and exploiting such parallelism is one of the outstanding challenges of modern Computer Science. This book focuses on how to program multiprocessors that communicate via a shared memory. Such systems are often called shared-memory multiprocessors or, more recently, multicores. Programming challenges arise at all scales of multiprocessor systems—at a very small scale, processors within a single chip need to coordinate access to a shared memory location, and on a large scale, processors in a supercomputer need to coordinate the routing of data. Multiprocessor programming is challenging because modern computer systems are inherently asynchronous: activities can be halted or delayed without warning by interrupts, preemption, cache misses, failures, and other events. These delays are inherently unpredictable, and can vary enormously in scale: a cache miss might delay a processor for fewer than ten instructions, a page fault for a few million instructions, and operating system preemption for hundreds of millions of
instructions. We approach multiprocessor programming from two complementary directions: principles and practice. In the principles part of this book, we focus on computability: figuring out what can be computed in an asynchronous concurrent environment. We use an idealized model of computation in which multiple concurrent threads manipulate a set of shared objects. The sequence of the thread operations on the objects is called the concurrent program or concurrent algorithm. This model is essentially the model presented by the JavaTM, C#, or C++ thread packages.
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