RESEARCH ◥ and sporadically and must ultimately face di- REVIEW SUMMARY minishing returns. As such, we see the big- gest benefits coming from algorithms for new COMPUTER SCIENCE problem domains (e.g., machine learning) and from developing new theoretical machine There’s plenty of room at the Top: What will drive models that better reflect emerging hardware. ◥ Hardware architectures computer performance after Moore’s law? ON OUR WEBSITE can be streamlined—for Read the full article instance, through proces- Charles E. Leiserson, Neil C. Thompson*, Joel S. Emer, Bradley C. Kuszmaul, Butler W. Lampson, at https://dx.doi. sor simplification, where Daniel Sanchez, Tao B. Schardl org/10.1126/ a complex processing core science.aam9744 is replaced with a simpler .................................................. core that requires fewer BACKGROUND: Improvements in computing in computing power stalls, practically all in- transistors. The freed-up transistor budget can power can claim a large share of the credit for dustries will face challenges to their produc- then be redeployed in other ways—for example, manyofthethingsthatwetakeforgranted tivity. Nevertheless, opportunities for growth by increasing the number of processor cores in our modern lives: cellphones that are more in computing performance will still be avail- running in parallel, which can lead to large powerful than room-sized computers from able, especially at the “Top” of the computing- efficiency gains for problems that can exploit 25 years ago, internet access for nearly half technology stack: software, algorithms, and parallelism. Another form of streamlining is the world, and drug discoveries enabled by hardware architecture. domain specialization, where hardware is cus- powerful supercomputers. Society has come tomized for a particular application domain. Downloaded from to rely on computers whose performance in- ADVANCES: Software can be made more effi- This type of specialization jettisons processor creases exponentially over time. cient by performance engineering: restructur- functionality that is not needed for the domain. Much of the improvement in computer per- ing software to make it run faster. Performance It can also allow more customization to the formance comes from decades of miniatur- engineering can remove inefficiencies in pro- specific characteristics of the domain, for in- ization of computer components, a trend that grams, known as software bloat, arising from stance, by decreasing floating-point precision was foreseen by the Nobel Prize–winning phys- traditional software-development strategies for machine-learning applications. http://science.sciencemag.org/ icist Richard Feynman in his 1959 address, that aim to minimize an application’s devel- Inthepost-Mooreera,performanceim- “There’s Plenty of Room at the Bottom,” to opment time rather than the time it takes to provements from software, algorithms, and the American Physical Society. In 1975, Intel run. Performance engineering can also tailor hardware architecture will increasingly re- founder Gordon Moore predicted the regu- software to the hardware on which it runs, quire concurrent changes across other levels larity of this miniaturization trend, now called for example, to take advantage of parallel pro- of the stack. These changes will be easier to im- Moore’s law, which, until recently, doubled the cessors and vector units. plement, from engineering-management and number of transistors on computer chips every Algorithms offer more-efficient ways to solve economic points of view, if they occur within 2 years. problems. Indeed, since the late 1970s, the time big system components: reusable software with Unfortunately, semiconductor miniaturiza- to solve the maximum-flow problem improved typically more than a million lines of code or tion is running out of steam as a viable way nearly as much from algorithmic advances hardware of comparable complexity. When a to grow computer performance—there isn’t as from hardware speedups. But progress on single organization or company controls a big on June 5, 2020 much more room at the “Bottom.” If growth a given algorithmic problem occurs unevenly component, modularity can be more easily re- engineered to obtain performance gains. More- over, costs and benefits can be pooled so that The Top important but costly changes in one part of the big component can be justified by benefits Technology elsewhere in the same component. OUTLOOK: As miniaturization wanes, the silicon- fabrication improvements at the Bottom will Software Algorithms Hardware architecture no longer provide the predictable, broad-based Opportunity Software performance New algorithms Hardware streamlining gains in computer performance that society has engineering enjoyed for more than 50 years. Software per- formance engineering, development of algo- Examples Removing software bloat New problem domains Processor simplification rithms, and hardware streamlining at the Tailoring software to New machine models Domain specialization Top can continue to make computer applica- hardware features tions faster in the post-Moore era. Unlike the historical gains at the Bottom, however, gains at the Top will be opportunistic, uneven, and sporadic. Moreover, they will be subject to diminishing returns as specific computations The Bottom become better explored.▪ for example, semiconductor technology SCIENCE Performance gains after Moore’s law ends. In the post-Moore era, improvements in computing power will The list of author affiliations is available in the full article online. “ ” “ ” *Corresponding author. Email: [email protected] increasingly come from technologies at the Top of the computing stack, not from those at the Bottom , Cite this article as C. E. Leiserson et al., Science 368, CREDIT: N. CARY/ reversing the historical trend. eaam9744 (2020). DOI: 10.1126/science.aam9744 Leiserson et al., Science 368, 1079 (2020) 5 June 2020 1of1 RESEARCH ◥ Working at the Top to obtain performance REVIEW also differs from the Bottom in how it affects a computing system overall. The performance COMPUTER SCIENCE provided by miniaturization has not required substantial changes at the upper levels of the There’s plenty of room at the Top: What will drive computing stack, because the logical behavior of the digital hardware, software, and data in computer performance after Moore’s law? a computation is almost entirely independent of the size of the transistors at the Bottom. As Charles E. Leiserson1, Neil C. Thompson1,2*, Joel S. Emer1,3, Bradley C. Kuszmaul1†, a result, the upper levels can take advantage Butler W. Lampson1,4, Daniel Sanchez1, Tao B. Schardl1 of smaller and faster transistors with little or no change. By contrast—and unfortunately— The miniaturization of semiconductor transistors has driven the growth in computer performance for many parts of the Top are dependent on each more than 50 years. As miniaturization approaches its limits, bringing an end to Moore’s law, other, and thus when one part is restructured to performance gains will need to come from software, algorithms, and hardware. We refer to these improve performance, other parts must often technologies as the “Top” of the computing stack to distinguish them from the traditional technologies adapt to exploit, or even tolerate, the changes. at the “Bottom”: semiconductor physics and silicon-fabrication technology. In the post-Moore era, the When these changes percolate through a sys- Top will provide substantial performance gains, but these gains will be opportunistic, uneven, and tem, it can take considerable human effort to sporadic, and they will suffer from the law of diminishing returns. Big system components offer a correctly implement and test them, which in- promising context for tackling the challenges of working at the Top. creases both costs and risks. Historically, the strategies at the Top for improving perform- Downloaded from ance coexisted with Moore’s law and were ver the past 50 years, the miniaturiza- physics of materials changes at atomic levels— used to accelerate particular applications that tion of semiconductor devices has been and because of the economics of chip manu- needed more than the automatic performance at the heart of improvements in com- facturing. Although semiconductor technology gains that Moore’s law could provide. puter performance, as was foreseen by may be able to produce transistors as small Here, we argue that there is plenty of room O http://science.sciencemag.org/ physicist Richard Feynman in his 1959 as 2 nm (20 Å), as a practical matter, min- at the Top, and we outline promising opportu- address (1) to the American Physical Society, iaturization may end around 5 nm because of nities within each of the three domains of soft- “There’s Plenty of Room at the Bottom.” Intel diminishing returns (10). And even if semi- ware, algorithms, and hardware. We explore founder Gordon Moore (2)observedasteady conductor technologists can push things a little the scale of improvements available in these rate of miniaturization and predicted (3)that further, the cost of doing so rises precipitously areas through examples and data analyses. We the number of transistors per computer chip as we approach atomic scales (11, 12). also discuss why “big system components” will would double every 2 years—a cadence, called In this review, we discuss alternative ave- provide a fertile ground for capturing these Moore’s law, that has held up considerably nues for growth in computer performance after gains at the Top.