Kai hwang computer architecture and parallel processing pdf
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- ADVANCED COMPUTER ARCHITECTURE PARALLELISM SCALABILITY PROGRAMMABILITY Baas® ' iiteCitft
- Computer architecture and parallel processing
- Computer Architecture and Parallel Processing
Kai Hwang. William J. Rajaraman, and C. June 26, PM. GAO - University of Delaware Advanced computer architecture : parallelism, scalability, programmability.
ADVANCED COMPUTER ARCHITECTURE PARALLELISM SCALABILITY PROGRAMMABILITY Baas® ' iiteCitft
These new machines, their operating environments including the operating system and languages, and the programs to effectively utilize them are introducing more rapid changes for researchers, builders, and users than at any time in the history of computer structures. For the first time since the introduction of Cray 1 vector processor in , it may again be necessary to change and evolve the programming paradigm -provided that massively parallel computers can be shown to be xiseful outside of research on massive parallelism.
Vector processors required modest data parallelism and these operations have been reflected either explicitly in Fortran programs or implicitly with the need to evolve Fortran e.
Fortran 90 to build in vector operations. So far, the main line of supercomputing as measured by the usage hours, jobs, number of programs, program portability has been the shared memory, vector multiprocessor as pioneered by Cray Research. In the Cray C90 supercomputer delivers a peak of 16 billion floating-point operations per second a Gigaflops with 16 processors and costs about S30 million, providing roughly floating-point operations per second per dollar.
In contrast, massively parallel computers introduced in the early s are nearly all based on utilizing the same powerful, RISC-based. CMOS microprocessors that are used in workstations. Unfortunately, to obtain peak power requires large-scale problems that can require 0 n 3 operations over supers, and this significantly increases the running time when peak power is the goal.
Not for commercial use xxii Preface The first four chapters should be taught to all disciplines. The three technology chapters are necessary for EE and CE students. The three architecture chapters can be selectively taught to CE and CS students, depending on the instructor's interest and the computing facilities available to teach the course. The three software chapters are written for CS students and are optional to EE students.
Five course outlines are suggested below for different audiences. The first three outlines are for hour, one-semester courses. The last two outlines are for two-quarter courses in a sequence. Instructors may wish to include some advanced research topics treated in Sections 1. The architecture chapters present four different families of commercially available computers. Instructors may choose to teach a subset of these machine families based on the accessibility of corresponding machines on campus or via a public network.
Students are encouraged to learn through hands-on programming experience on parallel computers. Answers to a few selected exercise problems are given at the end of the book. The PrerequisitesThis is an advanced text on computer architecture and parallel programming.
The reader should have been exposed to some basic computer organization and programming courses at the undergraduate level. Students should have some knowledge and experience in logic design, computer hardware, system operations, assembly languages, and Fortran or C programming. Because of the emphasis on scalable architectures and the exploitation of parallelism in practical applications, readers will find it useful to have some background in probability, discrete mathematics, matrix algebra, and optimization theory.
HLL used with compilers, subroutine libraries, batch processing monitor. Multiprogramming and timesharing OS, multiuser applications. Multiprocessor OS, languages, compilers, and environments for parallel processing. Massively parallel processing, grand challenge applications, heterogeneous processing. The First Generation From the architectural and software points of view, firstgeneration computers were built with a single central processing unit CPU which performed serial fixed-point arithmetic using a program counter, branch instructions, and an accumulator.
Machine or assembly languages were used. Subroutine linkage was not implemented in early computers. Highlevel languages HLLs , such as Fortran, Algol, and Cobol, were introduced along with compilers, subroutine libraries, and batch processing monitors.
Register transfer language was developed by Irving Reed for systematic design of digital computers. Microprogrammed control became popular with this generation. Pipelining and cache memory were introduced to close up the speed gap between the CPU and main memory. This led to the development of time-sharing operating systems OS using virtual memory with greater sharing or multiplexing of resources. The Fourth GenerationParallel computers in various architectures appeared in the fourth generation of computers using shared or distributed memory or optional vector hardware.
Multiprocessing OS, special languages, and compilers were developed for parallelism. Software tools and environments were created for parallel processing or distributed computing. During these 15 years , the technology of parallel processing gradually became mature and entered the production mainstream.
The Fifth GenerationFilth-generation computers have just begun to appear. These machines emphasize massively parallel processing MPP. Fifth-generation computers are targeted to achieve Teraflops 10 12 floating-point operations per second performance by the mids.
Heterogeneous processing is emerging to solve large scale problems using a network of heterogeneous computers with shared virtual memories. For numerical problems in science and technology, the solutions demand complex mathematical formulations and tedious integer or floating-point computations.
For alphanumerical problems in business and government, the solutions demand accurate transactions, large database management, and information retrieval operations.
For artificial intelligence AI problems, the solutions demand logic inferences and symbolic manipulations. These computing problems have been labeled numerical compuling, transaction processing, and logical reasoning. Some complex problems may demand a combination of these processing modes. Algorithms and Data Structures Special algorithms and data structures are needed to specify the computations and communications involved in computing problems.
Most numerical algorithms axe deterministic, using regularly structured data. Symbolic processing may use heuristics or nondeterministic searches over large knowledge bases. Problem formulation and the development of parallel algorithms often require interdisciplinary interactions among theoreticians, experimentalists, and computer programmers.
There are many bdoks dealing with the design and mapping of algorithms or heuristics onto parallel computers. In this book, we are more concerned about the computer. Today, programming parallelism is still very difficult for most programmers due to the fact that existing languages were originally developed for sequential computers.
Programmers are often forced to program hardware-dependent features instead of programming parallelism in a generic and portable way. Ideally, we need to develop a parallel programming environment with architecture-independent languages, compilers, and software tools. To develop a parallel language, we aim for efficiency in its implementation, portability across different machines, compatibility with existing sequential languages, expressiveness of parallelism, and ease of programming.
One can attempt a new language approach or try to extend existing sequential languages gradually. A new language approach has the advantage of using explicit high-level constructs for specifying parallelism.
However, new languages are often incompatible with existing languages and require new compilers or new passes to existing compilers. Most systems choose the language extension approach. Compiler SupportThere are three compiler upgrade approaches: preprocessor, precompiler, and parallelizing compiler. A preprocessor uses a sequential compiler and a low-level library of the target computer to implement high-level parallel constructs.
The precompiler approach requires some program flow analysis, dependence checking, and limited optimizations toward parallelism detection. The third approach demands a fully developed parallelizing or vectorizing compiler which can automatically detect parallelism in source code and transform sequential codes into parallel constructs. These approaches will be studied in Chapter The efficiency of the binding process depends on the effectiveness of the preprocessor, the precompiler, the parallelizing compiler, the loader, and the OS support.
Due to unpredictable program behavior, none of the existing compilers can be considered fully automatic or fully intelligent in detecting all types of parallelism. Very often compiler directives are inserted into the source code to help the compiler do a better job.
Users may interact with the compiler to restructure the programs. This has been proven useful in enhancing the performance of parallel computers. As seen by an assembly language programmer, computer architecture is abstracted by its instruction set, which includes opcode operation codes , addressing modes, registers, virtual memory, etc.
From the hardware implementation point of view, the abstract machine is organized with CPUs, caches, buses, microcode, pipelines, physical memory, etc. Therefore, the study of architecture covers both instruction-set architectures and machine implementation organizations. Over the past four decades, computer architecture has gone through evolutional rather than revolutional changes.
Sustaining features are those that were proven performance deliverers. As depicted in Fig. The sequential computer was Paraiiei Computer Models improved from bit-serial to word-parallel operations, and from fixed-point to floatingpoint operations. The von Neumann architecture is slow due to sequential execution of instructions in programs. Functional parallelism was supported by two approaches: One is to use multiple functional units simultaneously, and the other is to practice pipelining at various processing levels.
LegendsThe latter includes pipelined instruction execution, pipelined arithmetic computations, and memory-access operations. Pipelining has proven especially attractive in Copyrighted materialLimited preview! Not for commercial use 1. Vector operations were originally carried out implicitly by software-controlled looping using scalar pipeline processors.
Flynn 's Classification Michael Flynn introduced a classification of various computer architectures based on notions of instruction and data streams. As illustrated in Fig.
Computer architecture and parallel processing
June 26, PM. Advanced Computer Architecture Parallelism Scalability. Concept based notes Advanced Computer Architecture. The new edition offers a balanced treatment of theory, technology architecture and software used by advanced computer systems. It presents state-of-the-art principles and techniques for designing and programming parallel, vector, and scalable computer systems.
Contact us to negotiate about price. If you have any questions, contact us here. Related posts: Solutions Manual for Advanced. The main text deals with. Kai Hwang, Naresh Jotwani The new edition offers a balanced treatment of theory, technology architecture and software used by advanced computer systems.
The authors have divided the use of computers into the following four levels of sophistication: data processing, information Computer architecture and parallel processing K. Hwang, F. Briggs; Published in McGraw-Hill Series in.
Computer Architecture and Parallel Processing
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