The title of this issue, "The Promise and Perils of the Coming Multicore Revolution and Its Impact", can be seen as a request for an interpretation of what multicore processors mean now, or as an invitation to try to understand the role of multicore processors from a variety of different directions. We choose the latter approach, arguing that, over time, multicore processors will play several key roles in an ongoing, but ultimately fundamental, transformation of the computing industry.
This article is organized as an extended discussion of the meaning of the terms "balance" and "optimization" in the context of microprocessor design. After an initial review of terminology (Section 1), the remainder of the article is arranged chronologically, showing the increasing complexity of "balance" and "optimization" over time. Section 2 is a retrospective, "why did we start making multiprocessors?" Section 3 discusses ongoing developments in system and software design related to multicore processors. Section 4 extrapolates current trends to forecast future complexity, and the article concludes with some speculations on the future potential of a maturing multicore processor technology.1
Before attempting to describe the evolution of multicore computing, it is important to develop a shared understanding of nomenclature and goals. Surprisingly, many commonly used words in this area are used with quite different meanings across the spectrum of producers, purchasers, and users of computing products. Investigation of these different meanings sheds a great deal of light on the complexities and subtleties of the important trade-offs that are so fundamental to the engineering design process.
Webster's online dictionary lists twelve definitions for "balance."2 The two that are most relevant here are:
- 5 a : stability produced by even distribution of weight on each side of the vertical axis b : equipoise between contrasting, opposing, or interacting elements c : equality between the totals of the two sides of an account
- 6 a : an aesthetically pleasing integration of elements
Both of these conceptualizations appear to play a role in people's thinking about "balance" in computer systems. The former is quantitative and analytical, but attempts to apply this definition quickly run into complications. What attributes of a computer system should display an "even distribution"? Depending on the context, the attributes of importance might include cost, price, power consumption, physical size, reliability, or any of a host of (relatively) independent performance attributes. There is no obvious "right answer" in choosing which of these attributes to "balance" or how to combine multiple comparisons into a single "balanced" metric.
The aesthetic definition of "balance" strikes an emotional chord when considering the multidimensional design/configuration space of computer systems, but dropping the quantitative element eliminates the utility of the entire "balance" concept when applied to the engineering and business space.
The failure of these standard definitions points to a degree of subtlety that cannot be ignored. We will happily continue to use the word "balance" in this article, but with the reminder that the word only makes quantitative sense in the context of a clearly defined quantitative definition of the relevant metrics, including how they are composed into a single optimization problem.
Webster's online dictionary lists only a single definition for "optimization":3
- : an act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible; specifically : the mathematical procedures (as finding the maximum of a function) involved in this
For this term, the most common usage error among computer users draws from the misleadingly named "optimizers" associated with compilers. As a consequence of this association, the word "optimization" is often used as a synonym for "improvement." While the terms are similar, optimization (in its mathematical sense) refers to minimizing or maximizing a particular objective function in the presence of constraints (perhaps only those provided by the objective function itself). In contrast, "improvement" means to "make something better" and does not convey the sense of trade-off that is fundamental to the concept of an "optimization" problem. Optimization of a computer system design means choosing parameters that minimize or maximize a particular objective function, while typically delivering a "sub-optimal" solution for other objective functions. Many parameters act in mutually opposing directions for commonly used objective functions, e.g., low cost vs high performance, low power vs high performance, etc. Whenever there are design parameters that act in opposing directions, the "optimum" design point will depend on the specific quantitative formulation of the objective function – just exactly how much is that extra 10% in performance (or 10% reduction in power consumption, etc.) worth in terms of the other design goals?
