multiple processors, sometimes of differing power and/or performance characteristics and functions, on a single chip. This section describes those intertwined technical drivers and the resulting challenges to continued growth in computing performance. This shift away from an emphasis on ever-increasing speed has disrupted what has historically been a continuing progression of dramatic sequential performance improvements and associated software innovation and evolution atop a predictable hardware base followed by increased demand for ever more software innovations that in turn motivated hardware improvements. This disruption has profound implications not just for the information technology industry, but for society as a whole. This section first describes the benefits of this virtuous cycle—now ending—that we have depended on for so long. The technical challenges related to scaling nanometer devices, what the shift to multicore architectures means for architectural innovation, programming explicitly parallel hardware, increased heterogeneity in hardware, and the need for correct, secure, and evolvable software are then discussed.

1.1.1 Hardware-Software Virtuous Cycle

The hardware and performance improvements described above came with a stable programming interface between hardware and software. This interface persisted over multiple hardware generations and in turn contributed to the creation of a virtuous hardware-software cycle (see Figure 1-1). Hardware and software capabilities and sophistication each grew dramatically in part because hardware and software designers could innovate in isolation from each other, while still leveraging each other’s advances in a predictable and sustained fashion. For example, hardware designers added sophisticated out-of-order instruction issue logic, branch prediction, data prefetching, and instruction prefetching to the capabilities. Yet, even as the hardware became more complex, application software did not have to change to take advantage of the greater performance in the underlying hardware and, consequently, achieve greater performance on the software side as well.

Software designers were able to make grounded and generally accurate assumptions about future capabilities of the hardware and could—and did—create software that needed faster, next-generation processors with larger memories even before chip and system architects actually were able to deliver them. Moreover, rising hardware performance allowed software tool developers to raise the level of abstraction for software development via advanced libraries and programming models, further accelerating application development. New, more demanding applications that only executed on the latest, highest performance hardware drove the market for the newest, fastest, and largest memory machines as they appeared.

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FIGURE 1-1 Cracks in the hardware-software virtuous cycle. SOURCE: Adapted from a 2011 briefing presentation on the Computer Science and Telecommunications Board report The Future of Computing Performance: Game Over or Next Level?

Another manifestation of the virtuous cycle in software was the adoption of high-level programming language abstractions, such as object orientation, managed runtimes, automatic memory management, libraries, and domain-specific languages. Programmers embraced these abstractions (1) to manage software size, sophistication, and complexity and (2) to leverage existing components developed by others. However, these abstractions are not without cost and rely on system software (i.e., compilers, runtimes, virtual machines, and operating systems) to manage software complexity and to map abstractions to efficient hardware implementations. In the past, as long as the software used a sequential programming interface, the cost of abstraction was hidden by ongoing, significant improvements in hardware performance. Programmers embraced abstraction and consequently produced working software faster.

Looking ahead, it seems likely that the right choice of new abstractions will expand the pool of programmers further. For example, a domain specialist can become a programmer if the language is intuitive and the



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