Erik Lindahl on Bio-Research Advances, the March to Mixed-Precision and AI-HPC Synergies

By Tiffany Trader

June 8, 2017

At PRACEdays in Barcelona, HPCwire had the opportunity to interview Dr. Erik Lindahl, Stockholm University biophysics professor and chair of the PRACE Scientific Steering Committee about the goals of PRACE, the evolution of PRACEdays, and the latest bioscience and computing trends. Part one of that interview, available here, takes a in-depth look at how PRACE is enabling European HPC research. In part two, below, Lindahl offers his perspective on some of the trends making the biggest impact on HPC today, including the momentum for mixed-precision and the potential for AI synergies.

HPCwire: What excites you most about the field of bioinformatics right now?

Erik Lindahl: When it comes to bioinformatics in general that’s very much dominated by sequencing today, which is an amazing technology but it’s also one of the fields where it’s been hardest to use supercomputers because people are very much dependent on lots of scripts and things that aren’t really that paralyzed yet. When it comes to structural biology where I’m working, there’s an amazing generation of techniques that can determine the structures of small molecules that we have in our bodies — proteins, DNA, RNA — that are basically the work horses for everything. Almost anything in your body that does anything is a protein. We’ve been able to determine structures of proteins for decades, but historically we’ve always seen these as small rigid molecules because by the time you determine a crystal of something you’re going to determine at 100 Kelvin, they don’t move.

Erik Lindahl

The challenge with all these molecules is they would not work unless they actually moved because this ion channel is literally like a door or window in your cell. It will have to open hundreds or thousands of times per second to let through an ion, and that’s when you get a nerve signal. But with traditional techniques we’ve tried to study, you only get the still images you never get the movie. Both we and other groups have worked for years on simulating these channels. It’s just the last five years or so that computers are now fast enough that we can reach these biological timescales and actually see these channels opening and closing on a computer. And I get super excited because suddenly we can start to use these as computational microscopes that actually go beyond what we see in the lab because in the lab you get either the open or the closed states but in the computer we see the opening and closing. Then you can start to understand what happens if something went wrong, so if there was a small mutation of this channel that causes disease. Can you start to understand how should you design a drug to not just get a drug to bind but actually get the drug to say prevent the channel from closing so easily? So I’m very much on the research side here and my interest is understanding fundamental biology, but there are amazing applications opening just in the next few years. Give this 10 years and I think the majority of drugs are going to be designed this way.

HPCwire: Where does GROMACS fit into your research?

Lindahl: GROMACS is a product that started in the mid 1990s and that’s a product to simulate the motion of molecules, in particular bio-molecules. Molecular simulation is in principle a very easy problem but then it gets very hard. The idea of this is that everything in life including biology is really deterministic, so if you know all of the positions of atoms, and we know that these obey the normal laws of physics, you can actually calculate the forces on atoms for instance that two charges repel each other if they have the same sign; the attract each other if they have different signs.

So in principle these equations are not that difficult. The only problem is you have many of them and not just many, you have billions of equations that you need to solve. Just solving these equations is not going to be enough because when you calculate on all these forces you can move the atom say during a femtosecond, so you’re going to need to repeat this billions of times to get to the relevant timescales that would be milliseconds or something. So GROMACS started out as a code we developed as students [at the University of Groningen]. At the time I still remember when we were proud that we could run on over 20 cores. But over the years of course we’ve had to push this to hundreds of cores and thousands of cores and then tens of thousands of cores and for the last few years, hundreds of thousands of cores in some cases.

Initially this was just meant as our own research tool and we’re very happy that this has gone on in the field. While we lead the product the wonderful thing is that we’re having dozens of wonderful students and professors help develop this so this is turned into a community project where we jointly try to make the simulations more advanced. What is happening now is we’re trying to connect a whole range of experimental techniques. Twenty years ago we [as a field] were pretty happy to just sit in front of our computers but what’s happened in the last decade is that’s not sufficient anymore, you need to do both experiments and simulations and you need to constantly couple the simulations to the experiments and to me this has been like doing a second PhD. It’s wonderful; I’m very much a beginner in it but it’s revolutionizing everything we know about science.

HPCwire: So it’s more interdisciplinary…

Lindahl: First, it’s making it more interdisciplinary but the other thing is that when simulations first appeared, they were kind of tier two in science in the sense that you first determined the structure and then you also tried to simulate it a bit basically to confirm your ideas. This was when simulations were difficult, we didn’t necessarily always trust them, there were errors in many of these programs, there were errors in our models; we couldn’t simulate far enough to actually make biological predictions.

But what’s changed the last few years is the simulations have become so powerful that we not just we but even experimentalists tend to trust them. I wouldn’t say that they replace experiments, in some cases they do, but they have increasingly become a complement used very early in the pipeline, so nowadays we frequently use simulations and computational methods while we are determining the structure and that of course is taking a bit of a leap of faith on the experimental side which has forced us to be way more strict about the quality control in these programs.

My focus is more on the software side but one of the reasons why I love this field is how software and hardware develop together. The hardware is pointless without software that can use it, but the software is just as futile an exercise unless we have faster computers all the time.

HPCwire: Hardware, software and wet lab.

Lindahl: If we didn’t have the wet lab we wouldn’t have any biological knowledge at all and we couldn’t test our ideas.

HPCwire: You are also well-known for retooling GROMACS to take advantage of the increased performance of single-precision.

Lindahl: When I was a student in the late 1990s we were sitting down and trying to get these programs faster and as we were fairly proud these codes were fast and if you could get your codes to a couple of percent faster you were really happy – and then at some point we noticed that there were these new gaming instructions in modern CPUs. They were only published for gaming; they had very low accuracy and the idea is that there were a couple of operations in particular when you’re drawing shadows. It turns out when you’re drawing shadows, calculating distances are very important and you calculate distance by calculating inverse square roots.

I don’t remember exactly how I noticed, but at the time we spent about 85 percent of the time in our code calculating inverse square roots – that’s the bottleneck in all of these codes. At this point it dawned upon me if we can use these inverse square roots we could probably double the performance of these programs. The only problem is these instructions are meant for games; you typically don’t need 16 digits of accuracy in a game. So the hardest part was we had to move everything over to get by with single-precision. Of course changing to single-precision is easy but changing to single-precision and still maintaining your accuracy that’s hard. There are quite a few algorithms that you need to redo the way you sum things or change the order in which you do operations and in some cases even come up with a different algorithm so that you don’t need to rely on brute-force double-precision.

So this actually worked. It probably took us a year, and I don’t even want to remember the number of nights I spent coding assembly because that was the only way to access instructions at the time, but we doubled our performance by using single-precision for these instructions. This kept us very happy for about a decade and then in the early 2000s or so everybody started using GPUs and it was the same story all over again — initially GPUs were only targeting games at the time.

We were lucky. We had already done the strength reduction, we could code everything in single-precision. It was fairly easy for us to just switch over and use all the same algorithms on the GPUs. And of course since then GPUs have become better at double-precision but there is still a factor two difference and I think we are seeing that in all modern processors, not just based on the floating point. It’s because double-precision data also takes twice as much space. We’re heading into all this big data and artificial intelligence area and since data is becoming more important than the compute in many ways. If you can save a factor of two when it comes to storing your data, that is increasingly important. So I think that single-precision is here to stay — give us 5 to 10 years and I think we’re increasingly going to see the double-precision is a niche.

HPCwire: Will computational scientists be willing to make the trade-off between compute power and time to solution?

Lindahl: I think they will have to because I think we’ve seen this development a couple of times. There are certain aspects to double-precision that are important particularly to the national labs on the largest supercomputers, but most chip design is driven by the mass-market and the consumer market doesn’t really need double-precision that much so I think it will still be around but I suspect that it’s going to cost you more and more if you absolutely need double-precision. At some point I suspect that we’re going to be in situations similar to the vector machines we had in the 1990s. There were certainly some codes that only worked on a vector machine and there were centers that kept buying the vector machines because they were so dependent on the code that absolutely needed a vector machine. Then the vendors stopped producing these machines and it doesn’t matter how important your code is if you can’t buy such a machine and of course there are today there are no vector codes. We probably won’t have exactly the same development here, but this is an important momento mori. It doesn’t matter how important your code is or the amazing science you can do with it; if there is no computer that can run it it is no longer a useful code.

HPCwire: What potential do you see to bring AI into traditional modeling and simulation workflows?

Lindahl: I think there are two parts maybe even three. With AI the data is the most valuable part, but if you look at traditional computing applications we have huge amounts of data; this is what’s produced in all these simulations. I think there’s tremendous potential immediately to start using artificial intelligence and analyze all of the data produced in simulations – not just in life sciences, but fluid dynamics, everything that you see presented here. Give this two or three years and I bet that suddenly we will say, why didn’t we do this three years ago because the programs are already available. There are amazing algorithms to mine the data and find important events, but that’s kind of the low hanging fruit.

The harder part, I think, will force us to completely revisit how we do modeling and this is where we have a problem. We are frequently good at what we do because we’ve been doing it for 25 years. That’s good in many ways but it’s also very dangerous because the natural instinct is of course to apply the stuff you know to a problem. The difference with AI is just as I mentioned that computers developed to have new instructions, new architectures, accelerators you can use in new ways. The latest processors have roughly an order of magnitude more power when used for machine learning than for traditional calculations. Now this is even worse because you don’t even have single-precision, you have half-precision, and somewhere there, most traditional scientists, and I’ve done this too, start to say sorry half-precision is too little, I can’t get by with that. And that is true, you can’t do molecular simulation with half-precision you would lose too much. But I keep looking — if it’s a factor of 10 more powerful, I think maybe we should even forget about trying simulate all of the motions of atoms. Can we find other ways to mine experimental data? If you have a protein that moves from one state to another, rather than simulating how each atom moves, maybe we can use machine learning to predict how the protein would move. That’s of course a bit of blasphemy as a scientist in my own field we’re not supposed to do it this way. But I think we will gradually be forced to or rather the same thing there, that if you accept a bit a blasphemy suddenly you will realize that computers today — if they are a factor of 10 faster in a decade they might be a factor of 100 or 1,000 faster if you accept to do this in new ways and when that happens I think we will all need to move over.

HPCwire: Who is doing early work in this direction?

Lindahl: One of the works I am most impressed with is where researchers have started to use deep learning to solve a problem for which you would historically always use expensive quantum chemistry codes. In quantum chemistry, you have processions of atoms and then you solve extremely expensive equations to tell what is the energy and this means that given a set of coordinates for your atoms you should predict what an energy is. This is used lots in material science, occasionally life sciences too. It’s extremely costly; it’s orders of magnitudes more costly than the problems I work with, and you can’t even imagine doing this as a function of time in milliseconds, but what people have done is they have trained machine learning, deep learning networks to do this, so given a set of coordinates what should the energy be.

Here you’ve combined the traditional way of doing simulations because you are going to need to create a training set with millions of small simulations that given these coordinates this is what the energy should be. Then you train your network to predict energies based on coordinates, and then you can start feeding this network a new set of coordinates but instead of taking 24 hours you get the network in a millisecond – and then you get what is the atom.

There are of course cases where it’s not as good as quantum chemistry, but in a few examples they do surprisingly well. [Here’s an example of this research published earlier this year in the journal Chemical Science.] Particularly if you are in industry, the advantage of being able to do things in less than a second I think they frequently outweigh the added accuracy you would get in 24 hours. Here again we see this marriage that you can’t train this network unless people have access to these very large resources to create the training data but once you’ve trained the machine learning algorithms this becomes something that you can apply directly in industry.

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!

SRC Spends $200M on University Research Centers

January 16, 2018

The Semiconductor Research Corporation, as part of its JUMP initiative, has awarded $200 million to fund six research centers whose areas of focus span cognitive computing, memory-centric computing, high-speed communicat Read more…

By John Russell

US Seeks to Automate Video Analysis

January 16, 2018

U.S. military and intelligence agencies continue to look for new ways to use artificial intelligence to sift through huge amounts of video imagery in hopes of freeing analysts to identify threats and otherwise put their Read more…

By George Leopold

URISC@SC17 and the #LongestLastMile

January 11, 2018

A multinational delegation recently attended the Understanding Risk in Shared CyberEcosystems workshop, or URISC@SC17, in Denver, Colorado. URISC participants and presenters from 11 countries, including eight African nations, 12 U.S. states, Canada, India and Nepal, also attended SC17, the annual international conference for high performance computing, networking, storage and analysis that drew nearly 13,000 attendees. Read more…

By Elizabeth Leake, STEM-Trek Nonprofit

HPE Extreme Performance Solutions

HPE and NREL Take Steps to Create a Sustainable, Energy-Efficient Data Center with an H2 Fuel Cell

As enterprises attempt to manage rising volumes of data, unplanned data center outages are becoming more common and more expensive. As the cost of downtime rises, enterprises lose out on productivity and valuable competitive advantage without access to their critical data. Read more…

When the Chips Are Down

January 11, 2018

In the last article, "The High Stakes Semiconductor Game that Drives HPC Diversity," I alluded to the challenges facing the semiconductor industry and how that may impact the evolution of HPC systems over the next few years. I thought I’d lift the covers a little and look at some of the commercial challenges that impact the component technology we use in HPC. Read more…

By Dairsie Latimer

SRC Spends $200M on University Research Centers

January 16, 2018

The Semiconductor Research Corporation, as part of its JUMP initiative, has awarded $200 million to fund six research centers whose areas of focus span cognitiv Read more…

By John Russell

When the Chips Are Down

January 11, 2018

In the last article, "The High Stakes Semiconductor Game that Drives HPC Diversity," I alluded to the challenges facing the semiconductor industry and how that may impact the evolution of HPC systems over the next few years. I thought I’d lift the covers a little and look at some of the commercial challenges that impact the component technology we use in HPC. Read more…

By Dairsie Latimer

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

ANL’s Rick Stevens on CANDLE, ARM, Quantum, and More

January 8, 2018

Late last year HPCwire caught up with Rick Stevens, associate laboratory director for computing, environment and life Sciences at Argonne National Laboratory, f Read more…

By John Russell

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

The @hpcnotes Predictions for HPC in 2018

January 4, 2018

I’m not averse to making predictions about the world of High Performance Computing (and Supercomputing, Cloud, etc.) in person at conferences, meetings, causa Read more…

By Andrew Jones

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Leading Solution Providers

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Nvidia, Partners Announce Several V100 Servers

September 27, 2017

Here come the Volta 100-based servers. Nvidia today announced an impressive line-up of servers from major partners – Dell EMC, Hewlett Packard Enterprise, IBM Read more…

By John Russell

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This