HIGH PERFORMANCE COMPUTING
Faster code: get more ‘bang for your buck’
ROBERT ROE INTERVIEWS JOHN SHALF ON THE DEVELOPMENT OF DIGITAL COMPUTING IN THE POST MOORE’S LAW ERA
As not only the HPC industry but the larger computing ecosystem tries to overcome the slowdown
and eventual end of transistor scaling described by Moore’s Law, scientists and researchers are implementing programmes that will define the technologies and new materials needed to supplement, or replace, traditional transistor technologies. In his keynote speech at the upcoming
ISC conference in Frankfurt, John Shalf Department Head for Computer Science at Lawrence Berkeley National Laboratory, will discuss the need to increase the pace of development for new technologies which can help to deliver the next generation of computing performance improvements. Shalf will describe the decline in Moore’s Law as we approach the physical limits of transistor fabrication, which is estimated to be in the 3 to 5nm range. Shalf will also describe the lab-wide project at Berkeley and the DOE’s efforts to overcome these challenges through the development acceleration of the design of new computing technologies. Finally, he will provide a view into what a system might look like in 2021 to 2023, and the challenges ahead, based on our most recent understanding of technology roadmaps. The keynote will highlight the tapering of
historical improvements in lithography, and how it affects options available to continue scaling of successors to the first exascale machine.
What are the options available to the HPC industry in a post-Moore’s Law environment? There are really three paths forward. The first being the one that is pursued most immediately is architecture specialisation. This is creating architectures that are
4 Scientific Computing World June/July 2019
tailored for the problem that you are solving. An example of that would be Google’s tensor processing unit (TPU). It’s a purpose-built architecture for their inferencing workload and it is much more efficient than using a general-purpose chip to accomplish the same goal. This isn’t a new thing. GPU’s where specialised for graphics and we also have many video codecs that have been specialised, that are specialised processors for video encoding/decoding. It’s a known quantity and it is known to work for a lot of target applications. But the question is, what is this going
crystal ball is not very clear on which way to go. For the direction of these carbon nanotubes, or negative capacitance FETS, or some other CMOS replacement technology, we need to accelerate the pace of discovery. We need to have more capable simulation frameworks to find better materials, that have properties that can outperform silicon CMOS. New device concepts, such as these tiny relays called NEMS (A nanoelectromechanical (NEM) relay), carbon nanotubes or magentoelectronics – there is a lot of opportunities there. We don’t know what is going to win in that space but we definitely need to accelerate the pace of discovery. But that is ten years out probably. The last direction is new modes of
‘If you look at the mega datacentre market, it is already happening, so it is a question of when is HPC going to catch up?’
to do for science? How do we create customisations that are effective for scientific computing? The second direction that we can go is CMOS replacements. This would be the new transistor that could replace the silicon-based transistors that we have today. There is a lot of activity in that space, but we also know that it takes about 10 years to get from a laboratory demonstration to fabrication, and the lab demonstrations do not demonstrate that there is a clear alternative to CMOS yet. There are a lot of promising candidates but that demonstration of something new that could replace silicon is not there yet. The
computation which is the neuro-inspired computing and quantum computing. These are all very interesting ways to go, but I should point out that they are not a direct replacement for digital logic as we know it. They expand computing into areas where digital logic is not very effective, such as solving combinatorial Np-hard problems with quantum, digital computers are not so good at that. Or neuromorphic or AI image recognition, this is another area where traditional computers are not as efficient, but AI could expand computing into those areas. But we still need to pay attention to the pace of capability of digital computing, because it directly solves important mathematical equations, and it has a role that is very important for us.
Architecture specialisation The more you specialise, the more benefit you could get. Examples such as the ANTON and ANTON 2 computers (which were extremely specialised for molecular dynamics computations), they had some flexibility but it really doesn’t do anything other than molecular dynamics. But there is a wide spectrum of specialisation and there isn’t any one path. There are some codes, like the climate code, that are so broad in terms of the algorithms that they have in it, that you have to go in the general purpose direction, but you would still want to
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