ANL Special Colloquium on The Future of Computing

By John Russell

May 19, 2022

There are, of course, a myriad of ideas regarding computing’s future. At yesterday’s Argonne National Laboratory’s Director’s Special Colloquium, The Future of Computing, guest speaker Sadasivan Shankar, did his best to convince the audience that the high-energy cost of the current computing paradigm – not (just) economic cost; we’re talking entropy here – is fundamentally undermining computing’s progress such that it will never be able to solve today’s biggest challenges.

The broad idea is that the steady abstracting away of informational content from each piece of modern computing’s complicated assemblage (chips, architecture, programming) inexorably increases the cumulative energy cost, leading toward a hard ceiling. Leaving aside, for a moment, the decline in Moore’s law (just a symptom really), it is the separation (abstraction) of information from direct computation that’s the culprit argues Shankar. Every added step adds energy cost.

Nature, on the other hand, bakes information into things. Consider, said Shankar, how a string of amino acids folds into its intended 3-D conformation on a tiny energy budget and in a very short time just by interacting with its environment, and contrast that with the amount of compute required – i.e. energy expended – to accurately predict protein folding from a sequence of amino acids. Shankar, research technology manager at the SLAC National Laboratory and adjunct Stanford professor, argues computing must take a lesson from nature and strive to pack information more tightly into applications and compute infrastructure.

Information theory is a rich field with a history of rich debate. Turning theory into practice has often proven more difficult and messy. Shankar (and his colleagues) have been developing a formal framework for classifying the levels of information content in human-made computation schemes and natural systems in a way that permits direct comparison between the two. The resulting scale has eight classification levels (0-7).

There’s a lot to digest in Shankar’s talk. Rather than going off the rails here with a garbled explanation it’s worth noting that Argonne has archived the video and Shankar has a far-along paper that’s expected in a couple of months. No doubt some of his ideas will stir conversation. Given that Argonne will be home to Aurora, the exascale supercomputer now being built at the lab, it was an appropriate site for a talk on the future of computing.

Before jumping into what the future may hold, here’s a quick summary of Shankar’s two driving points – 1) Moore’s law, or more properly the architecture and semiconductor technology on which it rests, is limited and 2) the growing absolute energy cost of information processing using traditional methods (von Neumann) are limiting:

  • Moore’s Law. “Let us assume we are looking at 10 nanometers today. How many more generations (doubling) are there if you go down all the way to a hydrogen atomic diameter, [which] we agree you cannot get there. You have 14 doublings beyond the 10-nanometer technology. However, more realistic would be silicon’s Van der Waals diameter, because that is when the properties of silicon material properties are changed, you essentially have nine doublings. But [even] more realistically, you may have only five doublings. Because if you look at the number of atoms on the surface of silicon, as opposed to the bulk, the surface atoms increase [more] than the number of atoms and the bulk, which means it’s no longer behaving like a silicon. If you put that as the endpoint, you essentially have only five doublings left. No matter how you look at it, you have about five to 14 doublings,” said Shankar.
  • Energy Cost. “Why is [this] a major problem for computing? The 10-year estimate of amount of information processed is going to be 1024 bits per year. The energy to process bits could end up being 100 exajoules per year. The total energy consumption that humans use this year is about 580 exajoules, so 20 percent of the energy could go into computing. And this is going to be a big problem. The three most important items for future of computing would be energy for information processed, energy for manufacturing – I didn’t even tell you the EUV [extreme ultraviolet lithography] for example, takes 100 kilowatts just to process pattern layers. Energy appears to be the single most important problem.”

A big part of the answer to the question of how computing must progress, suggested Shankar, is to take a page from Feynman’s reverberating idea – not just for quantum computing – and emulate the way nature computes, pack[ing] all of the information needed for the computing into the things themselves or at least by reducing abstraction as much as possible.

Argonne assembled an expert panel to bat Shankar’s ideas around. The panel included moderator Rick Stevens (associate laboratory director and Argonne distinguished fellow), Salman Habib (director, Argonne computational science division and Argonne distinguished fellow), Yanjing Li (assistant professor, department of computer science, University of Chicago), and Fangfang Xia (computer scientist, data science and learning division, ANL).

Few quibbled with the high-energy cost of computing as described by Shankar but they had a variety of perspectives on moving forward. One of the more intriguing comments came from Xia, an expert in neuromorphic computing. He suggested using neuromorphic systems to discover new algorithms is a potentially productive approach.

“My answer goes back to the earlier point Sadas and Rick made which is, if we’re throwing away efficiency in the information power conversion process, why don’t we stay with biological system for a bit longer. There’s this interesting field called synthetic biological intelligence. They are trying to do these brain-computer interfaces, not in a Neurolink way, because that’s still shrouded in uncertainty. But there is a company and they grow these brain cells in a petri dish. Then they connect this to an Atari Pong game. And you can see that after just 10 minutes, these brain cells self-organize into neural networks, and they can learn to play the game,” said Xia.

“Keep in mind, this is 10 minutes in real life, it’s not a simulation time. It’s only dozens of games, just like how we pick up games. So this data efficiency is enormous. What I find particularly fascinating about this is that in this experiment there was no optimization goal. There is no loss function you have to tweak. The system, when connected in this closed loop fashion, will just learn in an embodied way. That opens so many possibilities, you think about all these dishes, just consuming glucose, you can have them to learn latent representations, maybe to be used in digital models.”

 

Li, a computer architecture expert, noted that general purpose computing infrastructure has existed for a long time.

“I remember this is the same architecture of processor design I learned at school, and I still teach the same materials today. For the most part, when we’re trying to understand how CPUs work, and even some of the GPUs, those have been around for a long time. I don’t think there has been a lot of very revolutionary kind of changes for those architectures. There’s a reason for that, because we have developed, good tool chains, the compiler tool change people are educated to understand and program and build those systems. So anytime we want to make a big change [it has] to be competitive and as usable as what we know of today,” Li said.

On balance, she expects more incremental changes. “I think it’s not going to be just a big jump and we’ll get there tomorrow. We have to build on small steps looking at building on existing understanding and also evolving along with the application requirements. I do think that there will be places where we can increase energy efficiency. If we’re looking at the memory hierarchy, for example, we know caches and that it helps us with performance. But it’s also super inefficient from an energy performance standpoint. But this has worked for a long time, because traditional applications have good locality, but we are increasingly seeing new applications where [there] may not be as many localities so there’s a way for innovation in the memory hierarchy path. For example, we can design different memory, kind of reference patterns and infrastructures or applications that do not activate locality, for example. That will be one way of making the whole computing system much more efficient.”

Li noted the trend toward specialized computing was another promising approach: “If we use a general-purpose computing system like a CPU, there’s overhead that goes into fetching the instructions, decoding them. All of those are overheads are not directly solving the problem, but it’s just what you need to get the generality you need to solve all problems. Increasing specialization towards offloading different specialized tasks would be another kind of interesting perspective of approaching this problem.”

There was an interesting exchange between Shankar and Stevens over the large amount of energy consumed in training today’s large natural language processing models.

Shankar said, “I’m quoting from literature on deep neural networks or any of these image recognition networks. They scale quadratically with the number of data points. One of the latest things that is being hyped about in the last few weeks is a trillion parameter, natural language processing [model]. So here are the numbers. To train one of those models, it takes the energy equivalent to four cars being driven a whole year, just to train the model, including the manufacturing cost of the car. That is how much energy is spent in the training on this, so there is a real problem, right?”

Not so fast countered Stevens. “Consider using the same numbers for how much energy is going into Bitcoin, right? So the estimate is maybe something like 5 percent of global energy production. At least these neural network models are useful. They’re not just used for natural language processing. You can use it for distilling knowledge. You can use them for imaging and so forth. I want to shift gears a little bit. Governments around the world and VCs are putting a lot of money into quantum computing, and based on what you were talking about, it’s not clear to me that that’s actually the right thing we should be doing. We have lots of opportunities for alternative computing models, alternative architectures that could open up spaces that we know in principle can work. We have classical systems that can do this,” he said.

Today, there’s an army of computational scientists around the world seeking ways to advance computing, some of them focused on the energy aspect of the problem, others focused on other areas such on performance or capacity. It will be interesting to see if the framework and methodology embodied on Shankar’s forthcoming paper not only provokes discussion but also provides a concrete methodology for comparing computing system efficiency.

Link to ANL video: https://vimeo.com/event/2081535/17d0367863

Brief Shankar Bio

Sadasivan (Sadas) Shankar is Research Technology Manager at SLAC National Laboratory and Adjunct Professor in Stanford Materials Science and Engineering. He is also an Associate in the Department of Physics in Harvard Faculty of Arts and Sciences, and was the first Margaret and Will Hearst Visiting Lecturer in Harvard University and the first Distinguished Scientist in Residence at the Harvard Institute of Applied Computational Sciences. He has co-instructed classes related to materials, computing, and sustainability and was awarded Harvard University Teaching Excellence Award. He is involved in research in materials, chemistry, and specialized AI methods for complex problems in physical and natural sciences, and new frameworks for studying computing. He is a co-founder and the Chief Scientist in Material Alchemy, a “last mile” translational and independent venture for sustainable design of materials.

Dr. Shankar was a Senior Fellow in UCLA-IPAM during a program on Machine Learning and Many-body Physics, invited speaker in The Camille and Henry Dreyfus Foundation on application of Machine Learning for chemistry and materials, Carnegie Science Foundation panelist for Brain and Computing, National Academies speaker on Revolutions in Manufacturing through Mathematics, invited to White House event for Materials Genome, Visiting Lecturer in Kavli Institute of Theoretical Physics in UC-SB, and the first Intel Distinguished Lecturer in Caltech and MIT. He has given several colloquia and lectures in universities all over the world. Dr. Shankar also worked in the semiconductor industry in the areas of materials, reliability, processing, manufacturing, and is a co-inventor in over twenty patent filings. His work was also featured in the journal Science and as a TED talk.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Is Time Running Out for Compromise on America COMPETES/USICA Act?

June 22, 2022

You may recall that efforts proposed in 2020 to remake the National Science Foundation (Endless Frontier Act) have since expanded and morphed into two gigantic bills, the America COMPETES Act in the U.S. House of Representatives and the U.S. Innovation and Competition Act in the U.S. Senate. So far, efforts to reconcile the two pieces of legislation have snagged and recent reports... Read more…

Cerebras Systems Thinks Forward on AI Chips as it Claims Performance Win

June 22, 2022

Cerebras Systems makes the largest chip in the world, but is already thinking about its upcoming AI chips as learning models continue to grow at breakneck speed. The company’s latest Wafer Scale Engine chip is indeed the size of a wafer, and is made using TSMC’s 7nm process. The next chip will pack in more cores to handle the fast-growing compute needs of AI, said Andrew Feldman, CEO of Cerebras Systems. Read more…

AMD’s MI300 APUs to Power Exascale El Capitan Supercomputer

June 21, 2022

Additional details of the architecture of the exascale El Capitan supercomputer were disclosed today by Lawrence Livermore National Laboratory’s (LLNL) Terri Quinn in a presentation delivered to the 79th HPC User Forum Read more…

IDC Perspective on Integration of Quantum Computing and HPC

June 20, 2022

The insatiable need to compress time to insights from massive and complex datasets is fueling the demand for quantum computing integration into high performance computing (HPC) environments. Such an integration would allow enterprises to accelerate and optimize current HPC applications and processes by simulating and emulating them on today’s noisy... Read more…

Q&A with Intel’s Jeff McVeigh, an HPCwire Person to Watch in 2022

June 17, 2022

HPCwire presents our interview with Jeff McVeigh, vice president and general manager, Super Compute Group, Intel Corporation, and an HPCwire 2022 Person to Watch. McVeigh shares Intel's plans for the year ahead, his pers Read more…

AWS Solution Channel

Shutterstock 152995403

Bayesian ML Models at Scale with AWS Batch

This post was contributed by Ampersand’s Jeffrey Enos, Senior Machine Learning Engineer, Daniel Gerlanc, Senior Director for Data Science, and Brandon Willard, Data Science Lead. Read more…

Microsoft/NVIDIA Solution Channel

Shutterstock 261863138

Using Cloud-Based, GPU-Accelerated AI for Financial Risk Management

There are strict rules governing financial institutions with a number of global regulatory groups publishing financial compliance requirements. Financial institutions face many challenges and legal responsibilities for risk management, compliance violations, and failure to catch financial fraud. Read more…

Nvidia, Intel to Power Atos-Built MareNostrum 5 Supercomputer

June 16, 2022

The long-troubled, hotly anticipated MareNostrum 5 supercomputer finally has a vendor: Atos, which will be supplying a system that includes both Nvidia and Intel CPUs and GPUs across multiple partitions. The newly reimag Read more…

Is Time Running Out for Compromise on America COMPETES/USICA Act?

June 22, 2022

You may recall that efforts proposed in 2020 to remake the National Science Foundation (Endless Frontier Act) have since expanded and morphed into two gigantic bills, the America COMPETES Act in the U.S. House of Representatives and the U.S. Innovation and Competition Act in the U.S. Senate. So far, efforts to reconcile the two pieces of legislation have snagged and recent reports... Read more…

Cerebras Systems Thinks Forward on AI Chips as it Claims Performance Win

June 22, 2022

Cerebras Systems makes the largest chip in the world, but is already thinking about its upcoming AI chips as learning models continue to grow at breakneck speed. The company’s latest Wafer Scale Engine chip is indeed the size of a wafer, and is made using TSMC’s 7nm process. The next chip will pack in more cores to handle the fast-growing compute needs of AI, said Andrew Feldman, CEO of Cerebras Systems. Read more…

AMD’s MI300 APUs to Power Exascale El Capitan Supercomputer

June 21, 2022

Additional details of the architecture of the exascale El Capitan supercomputer were disclosed today by Lawrence Livermore National Laboratory’s (LLNL) Terri Read more…

IDC Perspective on Integration of Quantum Computing and HPC

June 20, 2022

The insatiable need to compress time to insights from massive and complex datasets is fueling the demand for quantum computing integration into high performance computing (HPC) environments. Such an integration would allow enterprises to accelerate and optimize current HPC applications and processes by simulating and emulating them on today’s noisy... Read more…

Q&A with Intel’s Jeff McVeigh, an HPCwire Person to Watch in 2022

June 17, 2022

HPCwire presents our interview with Jeff McVeigh, vice president and general manager, Super Compute Group, Intel Corporation, and an HPCwire 2022 Person to Watc Read more…

Nvidia, Intel to Power Atos-Built MareNostrum 5 Supercomputer

June 16, 2022

The long-troubled, hotly anticipated MareNostrum 5 supercomputer finally has a vendor: Atos, which will be supplying a system that includes both Nvidia and Inte Read more…

D-Wave Debuts Advantage2 Prototype; Seeks User Exploration and Feedback

June 16, 2022

Starting today, D-Wave Systems is providing access to a 500-plus-qubit prototype of its forthcoming 7000-qubit Advantage2 quantum annealing computer, which is d Read more…

AMD Opens Up Chip Design to the Outside for Custom Future

June 15, 2022

AMD is getting personal with chips as it sets sail to make products more to the liking of its customers. The chipmaker detailed a modular chip future in which customers can mix and match non-AMD processors in a custom chip package. "We are focused on making it easier to implement chips with more flexibility," said Mark Papermaster, chief technology officer at AMD during the analyst day meeting late last week. Read more…

Nvidia R&D Chief on How AI is Improving Chip Design

April 18, 2022

Getting a glimpse into Nvidia’s R&D has become a regular feature of the spring GTC conference with Bill Dally, chief scientist and senior vice president of research, providing an overview of Nvidia’s R&D organization and a few details on current priorities. This year, Dally focused mostly on AI tools that Nvidia is both developing and using in-house to improve... Read more…

Royalty-free stock illustration ID: 1919750255

Intel Says UCIe to Outpace PCIe in Speed Race

May 11, 2022

Intel has shared more details on a new interconnect that is the foundation of the company’s long-term plan for x86, Arm and RISC-V architectures to co-exist in a single chip package. The semiconductor company is taking a modular approach to chip design with the option for customers to cram computing blocks such as CPUs, GPUs and AI accelerators inside a single chip package. Read more…

The Final Frontier: US Has Its First Exascale Supercomputer

May 30, 2022

In April 2018, the U.S. Department of Energy announced plans to procure a trio of exascale supercomputers at a total cost of up to $1.8 billion dollars. Over the ensuing four years, many announcements were made, many deadlines were missed, and a pandemic threw the world into disarray. Now, at long last, HPE and Oak Ridge National Laboratory (ORNL) have announced that the first of those... Read more…

AMD/Xilinx Takes Aim at Nvidia with Improved VCK5000 Inferencing Card

March 8, 2022

AMD/Xilinx has released an improved version of its VCK5000 AI inferencing card along with a series of competitive benchmarks aimed directly at Nvidia’s GPU line. AMD says the new VCK5000 has 3x better performance than earlier versions and delivers 2x TCO over Nvidia T4. AMD also showed favorable benchmarks against several Nvidia GPUs, claiming its VCK5000 achieved... Read more…

Top500: Exascale Is Officially Here with Debut of Frontier

May 30, 2022

The 59th installment of the Top500 list, issued today from ISC 2022 in Hamburg, Germany, officially marks a new era in supercomputing with the debut of the first-ever exascale system on the list. Frontier, deployed at the Department of Energy’s Oak Ridge National Laboratory, achieved 1.102 exaflops in its fastest High Performance Linpack run, which was completed... Read more…

Newly-Observed Higgs Mode Holds Promise in Quantum Computing

June 8, 2022

The first-ever appearance of a previously undetectable quantum excitation known as the axial Higgs mode – exciting in its own right – also holds promise for developing and manipulating higher temperature quantum materials... Read more…

Nvidia Launches Hopper H100 GPU, New DGXs and Grace Superchips

March 22, 2022

The battle for datacenter dominance keeps getting hotter. Today, Nvidia kicked off its spring GTC event with new silicon, new software and a new supercomputer. Speaking from a virtual environment in the Nvidia Omniverse 3D collaboration and simulation platform, CEO Jensen Huang introduced the new Hopper GPU architecture and the H100 GPU... Read more…

PsiQuantum’s Path to 1 Million Qubits

April 21, 2022

PsiQuantum, founded in 2016 by four researchers with roots at Bristol University, Stanford University, and York University, is one of a few quantum computing startups that’s kept a moderately low PR profile. (That’s if you disregard the roughly $700 million in funding it has attracted.) The main reason is PsiQuantum has eschewed the clamorous public chase for... Read more…

Leading Solution Providers

Contributors

ISC 2022 Booth Video Tours

AMD
AWS
DDN
Dell
Intel
Lenovo
Microsoft
PENGUIN SOLUTIONS

Intel Reiterates Plans to Merge CPU, GPU High-performance Chip Roadmaps

May 31, 2022

Intel reiterated it is well on its way to merging its roadmap of high-performance CPUs and GPUs as it shifts over to newer manufacturing processes and packaging technologies in the coming years. The company is merging the CPU and GPU lineups into a chip (codenamed Falcon Shores) which Intel has dubbed an XPU. Falcon Shores... Read more…

AMD Opens Up Chip Design to the Outside for Custom Future

June 15, 2022

AMD is getting personal with chips as it sets sail to make products more to the liking of its customers. The chipmaker detailed a modular chip future in which customers can mix and match non-AMD processors in a custom chip package. "We are focused on making it easier to implement chips with more flexibility," said Mark Papermaster, chief technology officer at AMD during the analyst day meeting late last week. Read more…

India Launches Petascale ‘PARAM Ganga’ Supercomputer

March 8, 2022

Just a couple of weeks ago, the Indian government promised that it had five HPC systems in the final stages of installation and would launch nine new supercomputers this year. Now, it appears to be making good on that promise: the country’s National Supercomputing Mission (NSM) has announced the deployment of “PARAM Ganga” petascale supercomputer at Indian Institute of Technology (IIT)... Read more…

Nvidia Dominates MLPerf Inference, Qualcomm also Shines, Where’s Everybody Else?

April 6, 2022

MLCommons today released its latest MLPerf inferencing results, with another strong showing by Nvidia accelerators inside a diverse array of systems. Roughly fo Read more…

AMD’s MI300 APUs to Power Exascale El Capitan Supercomputer

June 21, 2022

Additional details of the architecture of the exascale El Capitan supercomputer were disclosed today by Lawrence Livermore National Laboratory’s (LLNL) Terri Read more…

Nvidia, Intel to Power Atos-Built MareNostrum 5 Supercomputer

June 16, 2022

The long-troubled, hotly anticipated MareNostrum 5 supercomputer finally has a vendor: Atos, which will be supplying a system that includes both Nvidia and Inte Read more…

Industry Consortium Forms to Drive UCIe Chiplet Interconnect Standard

March 2, 2022

A new industry consortium aims to establish a die-to-die interconnect standard – Universal Chiplet Interconnect Express (UCIe) – in support of an open chipl Read more…

Covid Policies at HPC Conferences Should Reflect HPC Research

June 6, 2022

Supercomputing has been indispensable throughout the Covid-19 pandemic, from modeling the virus and its spread to designing vaccines and therapeutics. But, desp Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire