Few in the computing industry dispute that achieving ‘exascale’ performance is worthy on its own merits. But fixating only on the magic milestone (10^18 FLOPS) misses an important point – HPC as currently practiced, even at the exascale level, won’t solve the grand challenges in science and society contends Dr. Sadasivan Shankar, Harvard University visiting lecturer and long-time prominent Intel materials scientist.

“I would argue that exascale is more than just exascale. It’s a new way of thinking. I would even argue that [when] petascale came along a lot of us who were using large scale computing didn’t see the benefits. If we are planning to solve some of the large scale grand problems in science and society today, the premise I am putting forth is we should do it a little differently,” Dr. Shankar told the audience at the recent HPC User Forum.

The new evolving paradigm, at least in materials science, is In Silico Inverse Design, proposed Dr. Shankar in his presentation – Exascale for Grand Challenge Problems in Sciences and Engineering. He presented his case for inverse design by examining emerging disruptive technologies in both science and society, identifying commonality in the challenges they represent (e.g. scale and combinatorial complexity), and presented a small sample of related computational challenges.

Many in the computer industry may know Dr. Shankar, who is the first Margaret and Will Hearst Visiting Lecturer in Computational Science and Engineering at Harvard School of Engineering and Applied Sciences. He was formerly an Intel scientist working in semiconductor materials, design and manufacturing.

During his tenure in the semiconductor industry Dr. Shankar worked on initiatives using computational modeling to optimize semiconductor processing and equipment for several technology generations, advanced process control using physics-based models, thermo-mechanical reliability of microprocessors, thermal modeling of 3D die stacking, and using thermodynamic principles to estimate energy efficiency of ideal computing architectures. He is a co-inventor in several patent filings covering areas in chemical reactor designs, semiconductor processes, bulk and nano materials, device structures, and algorithms (fuller bio).

The core of his argument is that in silico inverse design represents an emerging paradigm characterized by distinct computational requirements that are different and challenging as compared to direct design. Not a new concept, inverse design has gained traction in recent years. The Department of Energy’s Center for Inverse Design offers the following description:

“…To address a crucial scientific grand challenge…rather than using the conventional direct approach (Given the structure, find the electronic properties), we are using a “materials by inverse design” approach (Given the desired property, find the structure).

“The target properties of interest include general semiconductor optical and electrical properties; the desired materials functionalities include electron- and hole-conductive transparent conductors, solar absorbers, and nanostructures for energy sustainability. Our predictions of materials are examined iteratively by various synthetic approaches, including high-throughput parallel materials science.”

Dr. Shankar’s succinct definition is “the ability to use predictive capabilities, to design a material, which when synthesized in the manufacturing line will exhibit targeted properties.” He is quick to add, because of the approach “It’s not always guaranteed there is, in fact, a solution.”

A nuanced theory perhaps best heard directly from Dr. Shankar (link to a video of his presentation is at the end of the article); it is nonetheless possible to outline some of the central ideas he presented. Number one, science and technology have advanced to the point, he contends, that the direct design approach no longer works as well. Number two, the disruptive challenges facing both science and society share important technology commonalities best addressed by inverse design.

He cited work (McKinsey) that identified the top technology-based disruptive forces in the worldwide economy and business as: mobile internet, automation, internet of things, advanced robotics, autonomous and non autonomous vehicles, next-gen genomics, energy storage, 3d printing, advanced materials, advanced oil and gas exploration, and renewable energy.

“Traditionally disruptions are caused by emergence of a different kind of material, different kind of process, or a different way of doing something. Those cited here are expected to completely upstage the worldwide economy by 2025,” he said.

DOE offers a list of five expected scientific disruptors: control of material processes at the level of atoms and electrons; design and perfect atom- and energy- efficient synthesis of revolutionary new forms of matter with tailored properties; emergence of remarkable properties of matter from complex correlations of the atomic or electronic constituents and optimal control of these properties; characterization and control matter of non-equilibrium systems; leverage knowledge of energy and information interactions from living beings at the nanoscale to design systems with high energy efficiencies.

“If you look at these challenges (science and business) side by side and look for commonalities you could distill them to five items: 1) design, 2) materials and devices, 3) sensing, 4) automation and 5) miniaturization, fabrication and testing,” he said, all of which are well suited to inverse design.

Leaving science aside for a moment, Dr. Shankar noted the political landscape is shifting in support of funding supercomputing generally and exascale in specific (See Obama’s 2016 Budget Boosts R&D, Exascale Funding, HPCwire. The Partnership for Advanced Computing in Europe (PRACE) is also chasing exascale. China, of course, has the fastest supercomputer on the TOP500 list.)

Amid the rush to reach exascale Dr. Shankar believes the practicality of turning a quintillion FLOPS into real-world applications is perhaps not getting enough attention. The race to exascale (and resulting technologies and architectures) should be well informed by real-world applications and requirements of inverse design methodology – nowhere more so than in material science which has such a direct effect on economies and progress.

In semiconductor materials synthesis generally there are at least six challenges said Shankar: “One is the ability to measure; the second is the ability to synthesize; the third is the intrinsic disparity in scales; the fourth is being able to compute as needed; the fifth is complexity; and sixth is the daunting combinatorics associated with trying to go from a small scale to a large scale.”

Consider the combinatorial challenge associated with semiconductor material synthesis.

“If you look at the number of elements you could use it’s about 60 to 70, basically anything that’s not toxic. If you look at how you can mix them up into alloys or compounds, there are about 100,000. Now if you take two of these materials and try to create a junction now the possibilities are in the billions,” said Dr. Shankar. This number quickly grows to trillion and beyond as the number of interfaces between structures grow.

“In my talk, I was trying to synthesize 4 complex premises [in addressing these challenges],” said Dr. Shankar:

• Computational Design of Materials and Chemistry and why computing can play a key role
• Sub-points for bio and systems biology also benefit
• Eco-system is changing in favor of computing, both in terms of needs and political support
• Disruptions in technology and fundamental sciences could be connected by computing

“Computing will be connecting the dots but not computing the way we do traditional compute,” he said. Here is a link to the full video of Dr. Shanker’s presentation.