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Computer Architecture: A Quantitative Approach 5 E
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Computer Architecture - A Quantitative Approach [5e, 2012].pdf
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In Praise of Computer Architecture: A Quantitative Approach
Fifth Edition
“The 5th edition of Computer Architecture: A Quantitative Approach continues
the legacy, providing students of computer architecture with the most up-to-date
information on current computing platforms, and architectural insights to help
them design future systems. A highlight of the new edition is the significantly
revised chapter on data-level parallelism, which demystifies GPU architectures
with clear explanations using traditional computer architecture terminology.”
—Krste Asanovic´, University of California, Berkeley
“Computer Architecture: A Quantitative Approach is a classic that, like fine
wine, just keeps getting better. I bought my first copy as I finished up my under-
graduate degree and it remains one of my most frequently referenced texts today.
When the fourth edition came out, there was so much new material that I needed
to get it to stay current in the field. And, as I review the fifth edition, I realize that
Hennessy and Patterson have done it again. The entire text is heavily updated and
Chapter 6 alone makes this new edition required reading for those wanting to
really understand cloud and warehouse scale-computing. Only Hennessy and
Patterson have access to the insiders at Google, Amazon, Microsoft, and other
cloud computing and internet-scale application providers and there is no better
coverage of this important area anywhere in the industry.”
—James Hamilton, Amazon Web Services
“Hennessy and Patterson wrote the first edition of this book when graduate stu-
dents built computers with 50,000 transistors. Today, warehouse-size computers
contain that many servers, each consisting of dozens of independent processors
and billions of transistors. The evolution of computer architecture has been rapid
and relentless, but Computer Architecture: A Quantitative Approach has kept
pace, with each edition accurately explaining and analyzing the important emerg-
ing ideas that make this field so exciting.”
—James Larus, Microsoft Research
“This new edition adds a superb new chapter on data-level parallelism in vector,
SIMD, and GPU architectures. It explains key architecture concepts inside mass-
market GPUs, maps them to traditional terms, and compares them with vector
and SIMD architectures. It’s timely and relevant with the widespread shift to
GPU parallel computing. Computer Architecture: A Quantitative Approach fur-
thers its string of firsts in presenting comprehensive architecture coverage of sig-
nificant new developments!”
—John Nickolls, NVIDIA
“The new edition of this now classic textbook highlights the ascendance of
explicit parallelism (data, thread, request) by devoting a whole chapter to each
type. The chapter on data parallelism is particularly illuminating: the comparison
and contrast between Vector SIMD, instruction level SIMD, and GPU cuts
through the jargon associated with each architecture and exposes the similarities
and differences between these architectures.”
—Kunle Olukotun, Stanford University
“The fifth edition of Computer Architecture: A Quantitative Approach explores
the various parallel concepts and their respective tradeoffs. As with the previous
editions, this new edition covers the latest technology trends. Two highlighted are
the explosive growth of Personal Mobile Devices (PMD) and Warehouse Scale
Computing (WSC)—where the focus has shifted towards a more sophisticated
balance of performance and energy efficiency as compared with raw perfor-
mance. These trends are fueling our demand for ever more processing capability
which in turn is moving us further down the parallel path.”
—Andrew N. Sloss, Consultant Engineer, ARM
Author of ARM System Developer’s Guide
Computer Architecture
A Quantitative Approach
Fifth Edition
John L. Hennessy is the tenth president of Stanford University, where he has been a member
of the faculty since 1977 in the departments of electrical engineering and computer science.
Hennessy is a Fellow of the IEEE and ACM; a member of the National Academy of Engineering,
the National Academy of Science, and the American Philosophical Society; and a Fellow of
the American Academy of Arts and Sciences. Among his many awards are the 2001 Eckert-
Mauchly Award for his contributions to RISC technology, the 2001 Seymour Cray Computer
Engineering Award, and the 2000 John von Neumann Award, which he shared with David
Patterson. He has also received seven honorary doctorates.
In 1981, he started the MIPS project at Stanford with a handful of graduate students. After
completing the project in 1984, he took a leave from the university to cofound MIPS Computer
Systems (now MIPS Technologies), which developed one of the first commercial RISC
microprocessors. As of 2006, over 2 billion MIPS microprocessors have been shipped in devices
ranging from video games and palmtop computers to laser printers and network switches.
Hennessy subsequently led the DASH (Director Architecture for Shared Memory) project, which
prototyped the first scalable cache coherent multiprocessor; many of the key ideas have been
adopted in modern multiprocessors. In addition to his technical activities and university
responsibilities, he has continued to work with numerous start-ups both as an early-stage
advisor and an investor.
David A. Patterson has been teaching computer architecture at the University of California,
Berkeley, since joining the faculty in 1977, where he holds the Pardee Chair of Computer
Science. His teaching has been honored by the Distinguished Teaching Award from the
University of California, the Karlstrom Award from ACM, and the Mulligan Education Medal and
Undergraduate Teaching Award from IEEE. Patterson received the IEEE Technical Achievement
Award and the ACM Eckert-Mauchly Award for contributions to RISC, and he shared the IEEE
Johnson Information Storage Award for contributions to RAID. He also shared the IEEE John von
Neumann Medal and the C & C Prize with John Hennessy. Like his co-author, Patterson is a
Fellow of the American Academy of Arts and Sciences, the Computer History Museum, ACM,
and IEEE, and he was elected to the National Academy of Engineering, the National Academy
of Sciences, and the Silicon Valley Engineering Hall of Fame. He served on the Information
Technology Advisory Committee to the U.S. President, as chair of the CS division in the Berkeley
EECS department, as chair of the Computing Research Association, and as President of ACM.
This record led to Distinguished Service Awards from ACM and CRA.
At Berkeley, Patterson led the design and implementation of RISC I, likely the first VLSI reduced
instruction set computer, and the foundation of the commercial SPARC architecture. He was a
leader of the Redundant Arrays of Inexpensive Disks (RAID) project, which led to dependable
storage systems from many companies. He was also involved in the Network of Workstations
(NOW) project, which led to cluster technology used by Internet companies and later to cloud
computing. These projects earned three dissertation awards from ACM. His current research
projects are Algorithm-Machine-People Laboratory and the Parallel Computing Laboratory,
where he is director. The goal of the AMP Lab is develop scalable machine learning algorithms,
warehouse-scale-computer-friendly programming models, and crowd-sourcing tools to gain
valueable insights quickly from big data in the cloud. The goal of the Par Lab is to develop tech-
nologies to deliver scalable, portable, efficient, and productive software for parallel personal
mobile devices.
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