• Unit 9: Look Back and Look Ahead

    This unit looks back at important concepts of computer architecture that were covered in this course and looks ahead at some additional topics of interest. Computer architecture is both a depth and breadth subject. It is an in depth subject that is of particular interest if you are interested in computer architecture for a professional researcher, designer, developer, tester, manager, manufacturer, etc. and you want to continue with additional study in advanced computer architecture. On the other hand, computer architecture is a rich source of ideas and understanding for other areas of computer science, giving you a broad and stronger foundation for the study of programming, computer languages, compilers, software architecture, domain specific computing (like scientific computing), and more.

    In this unit, you will look back at some of the theoretical laws and analysis techniques that were introduced during the course. Looking ahead, you will be introduced to special purpose processors, application specific processing, high volume data storage, and network computing.

    Completing this unit should take you approximately 1 hour.

    • Upon successful completion of this unit, you will be able to:

      • discuss basic laws applicable to computer performance;
      • identify examples of computer architecture for special purpose computing architectures; and
      • identify examples of special purpose applications of parallel computing.

    • 9.1: Theory and Laws

      • Topologies Page

        Read section 2.6 to learn about network topologies. If a task cannot be performed by a computer with one processor, we decompose the task into subtasks, which will be allocated to multiple hardware devices, say processors or arithmetic units or memory. These multiple hardware devices need to communicate so that the original task can be done with acceptable cost and performance. The hardware devices and their interconnections form a network.

        Consider another situation: suppose we have a large software system made up of a large number of subsystems, in turn composed of many software components, subcomponents, etc. Suppose we list all the lowest level subcomponent's names across the top of a sheet of paper. These will be our column headings. Also, let's list down the side of the same sheet the same subcomponent names. These will be our row headings. This forms a table or two by two matrix. Finally, suppose we put a 1 in the table whenever or wherever there is a connection between the subcomponent named in the column heading and the subcomponent named in the row heading. Let's put a 0 everywhere else. This table now represents a topology for our network of software components; it could also be done for hardware components. These components and their interconnections are part of the software architecture. Realize that the matrix could be huge: 100 by 100, 1000 by 1000, and so on. The complexity of the interconnections is a factor in the reliability and performance of the architecture.

        Read section 2.7, which reviews Amdahl's law, an extension of Amdahl's law that includes communication overhead, and Gustafson's law. These laws express expected performance as the number of processors increases, or as both the size of the problem and number of processors increases.

        Then, read section 2.9 to learn about load balancing. This section looks at the situation where a processor is idle and another is busy, which is referred to as a load imbalance. If the work were to be distributed differently among the processors, then the idle time might be able to be eliminated. In this case, the load balance problem is explained as a graph problem.

    • 9.2: Special Purpose Computing Architectures

      • GPU, Distributed, Grid, and Cloud Computing Page

        Read section 2.8 to learn about GPU computing. GPU stands for Graphics Processing Unit. These are of interest because of the increase in the amount of graphics data handled by popular laptops and desktop computers. Furthermore, it turns out that since a GPU does primarily arithmetic computations, the architecture of a GPU is applicable to other types of applications that involve arithmetic on large amounts of data.

        Read section 2.10 to learn about distributed, grid, and cloud computing. These are configurations of multiple computers for increasing performance and/or decreasing cost for high volume database access, sharing of resources, or accessing remote computer resources, respectively.

      • TOP500 Page

        Read this article, paying particular attention to the rankings of supercomputers, based on performance in running a LINPACK benchmark for computing a dense set of linear equations. Towards the end of the article, note the large number of cores in these supercomputers.

    • 9.3: Case Study: Special Purpose Applications of Parallel Computing

    • Unit 9 Assessment

      • Unit 9 Assessment Quiz
        Receive a grade

        Take this assessment to see how well you understood this unit.

        • This assessment does not count towards your grade. It is just for practice!
        • You will see the correct answers when you submit your answers. Use this to help you study for the final exam!
        • You can take this assessment as many times as you want, whenever you want.