The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerators that have become increasingly important for mainstream applications.

A variety of programming models relevant to scientists explained, with an emphasis on how programming constructs map to parts of the computer. What makes computer programs fast or slow? To answer this question, we have to get behind the abstractions of programming languages and look at how a computer really works. This book examines and explains a variety of scientific programming models (programming models relevant to scientists) with an emphasis on how programming constructs map to different parts of the computer's architecture. Two themes emerge: program speed and program modularity. Throughout this book, the premise is to "get under the hood," and the discussion is tied to specific programs. The book digs into linkers, compilers, operating systems, and computer architecture to understand how the different parts of the computer interact with programs. It begins with a review of C/C++ and explanations of how libraries, linkers, and Makefiles work. Programming models covered include Pthreads, OpenMP, MPI, TCP/IP, and CUDA.The emphasis on how computers work leads the reader into computer architecture and occasionally into the operating system kernel. The operating system studied is Linux, the preferred platform for scientific computing. Linux is also open source, which allows users to peer into its inner workings. A brief appendix provides a useful table of machines used to time programs. The book's website (https://github.com/divakarvi/bk-spca) has all the programs described in the book as well as a link to the html text.

A principled, high-level view of computer performance and how to exploit it. Ideal for software architects and data scientists.

Hardware correctness is becoming ever more important in the design of computer systems. The authors introduce a powerful new approach to the design and analysis of modern computer architectures, based on mathematically well-founded formal methods which allows for rigorous correctness proofs, accurate hardware costs determination, and performance evaluation. This book develops, at the gate level, the complete design of a pipelined RISC processor with a fully IEEE-compliant floating-point unit. In contrast to other design approaches, the design presented here is modular, clean and complete.

What makes computer programs fast or slow? To answer this question, we have to get behind the abstractions of programming languages and look at how a computer really works. This book examines and explains a variety of scientific programming models (programming models relevant to scientists) with an emphasis on how programming constructs map to different parts of the computer's architecture. Two themes emerge: program speed and program modularity. Throughout this book, the premise is to "get under the hood," and the discussion is tied to specific programs. The book digs into linkers, compilers, operating systems, and computer architecture to understand how the different parts of the computer interact with programs. It begins with a review of C/C++ and explanations of how libraries, linkers, and Makefiles work. Programming models covered include Pthreads, OpenMP, MPI, TCP/IP, and CUDA. The emphasis on how computers work leads the reader into computer architecture and occasionally into the operating system kernel. The operating system studied is Linux, the preferred platform for scientific computing. Linux is also open source, which allows users to peer into its inner workings. A brief appendix provides a useful table of machines used to time programs. -- Provided by publisher

With the new developments in computer architecture, fairly recent publications can quickly become outdated. Computer Architecture: Software Aspects, Coding, and Hardware takes a modern approach. This comprehensive, practical text provides that critical understanding of a central processor by clearly detailing fundamentals, and cutting edge design features. With its balanced software/hardware perspective and its description of Pentium processors, the book allows readers to acquire practical PC software experience. The text presents a foundation-level set of ideas, design concepts, and applications that fully meet the requirements of computer organization and architecture courses. The book features a "bottom up" computer design approach, based upon the author's thirty years experience in both academe and industry. By combining computer engineering with electrical engineering, the author describes how logic circuits are designed in a CPU. The extensive coverage of a micprogrammed CPU and new processor design features gives the insight of current computer development. Computer Architecture: Software Aspects, Coding, and Hardware presents a comprehensive review of the subject, from beginner to advanced levels. Topics include: o Two's complement numbers o Integer overflow o Exponent overflow and underflow o Looping o Addressing modes o Indexing o Subroutine linking o I/O structures o Memory mapped I/O o Cycle stealing o Interrupts o Multitasking o Microprogrammed CPU o Multiplication tree o Instruction queue o Multimedia instructions o Instruction cache o Virtual memory o Data cache o Alpha chip o Interprocessor communications o Branch prediction o Speculative loading o Register stack o JAVA virtual machine o Stack machine principles

Teaching fundamental design concepts and the challenges of emerging technology, this textbook prepares students for a career designing the computer systems of the future. In-depth coverage of complexity, power, reliability and performance, coupled with treatment of parallelism at all levels, including ILP and TLP, provides the state-of-the-art training that students need. The whole gamut of parallel architecture design options is explained, from core microarchitecture to chip multiprocessors to large-scale multiprocessor systems. All the chapters are self-contained, yet concise enough that the material can be taught in a single semester, making it perfect for use in senior undergraduate and graduate computer architecture courses. The book is also teeming with practical examples to aid the learning process, showing concrete applications of definitions. With simple models and codes used throughout, all material is made open to a broad range of computer engineering/science students with only a basic knowledge of hardware and software.

Today, computer-system optimization, at both the hardware and software levels, must consider the details of the memory system in its analysis; failing to do so yields systems that are increasingly inefficient as those systems become more complex. This lecture seeks to introduce the reader to the most important details of the memory system; it targets both computer scientists and computer engineers in industry and in academia. Roughly speaking, computer scientists are the users of the memory system and computer engineers are the designers of the memory system. Both can benefit tremendously from a basic understanding of how the memory system really works: the computer scientist will be better equipped to create algorithms that perform well and the computer engineer will be better equipped to design systems that approach the optimal, given the resource limitations. Currently, there is consensus among architecture researchers that the memory system is "the bottleneck," and this consensus has held for over a decade. Somewhat inexplicably, most of the research in the field is still directed toward improving the CPU to better tolerate a slow memory system, as opposed to addressing the weaknesses of the memory system directly. This lecture should get the bulk of the computer science and computer engineering population up the steep part of the learning curve. Not every CS/CE researcher/developer needs to do work in the memory system, but, just as a carpenter can do his job more efficiently if he knows a little of architecture, and an architect can do his job more efficiently if he knows a little of carpentry, giving the CS/CE worlds better intuition about the memory system should help them build better systems, both software and hardware. Table of Contents: Primers / It Must Be Modeled Accurately / ...\ and It Will Change Soon

It is a great pleasure to write a preface to this book. In my view, the content is unique in that it blends traditional teaching approaches with the use of mathematics and a mainstream Hardware Design Language (HDL) as formalisms to describe key concepts. The book keeps the “machine” separate from the “application” by strictly following a bottom-up approach: it starts with transistors and logic gates and only introduces assembly language programs once their execution by a processor is clearly de ned. Using a HDL, Verilog in this case, rather than static circuit diagrams is a big deviation from traditional books on computer architecture. Static circuit diagrams cannot be explored in a hands-on way like the corresponding Verilog model can. In order to understand why I consider this shift so important, one must consider how computer architecture, a subject that has been studied for more than 50 years, has evolved. In the pioneering days computers were constructed by hand. An entire computer could (just about) be described by drawing a circuit diagram. Initially, such d- grams consisted mostly of analogue components before later moving toward d- ital logic gates. The advent of digital electronics led to more complex cells, such as half-adders, ip- ops, and decoders being recognised as useful building blocks.