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IBM Storage test contents

inner HPC-Hyperion research Interviews: Catalog’s Dave Turek on the Promise of DNA-primarily based statistics Storage and Compute | A2010-590 Real test Questions and boot camp

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Dave Turek is one of the venerable names in HPC. Having long served at IBM in senior strategic roles, he has spent decades at supercomputing’s leading edge. In 2020, Turek joined a Cambridge, MA business called Catalog, a project-funded startup immersed within the rising and exotic world of DNA-primarily based statistics storage and compute.

in this interview conducted with the aid of insideHPC on behalf of HPC trade analyst firm Hyperion research, Turek explains the basics of DNA-based technology, its performance characteristics, initial workloads and markets to which it’s expected to be applied and a timeline for industrial viability.

in response to Turek, DNA-based mostly expertise has an enormous twin-talents to deliver long-term, highly sturdy and dense information storage together with, on the compute side, intense excessive efficiency at low energy consumption. Turek, who is Catalog’s CTO, quickens so as to add that the DNA produced by way of Catalog is strictly artificial, it’s now not biologically active and therefore now not an environmental threat.

Interview Transcript:

Doug Black: hello, I’m Doug Black, editor in chief at internal HPC, and these days we’re talking on behalf of Hyperion analysis with Dave Turk, he is chief know-how officer at Catalog, which is a Cambridge Mass.-primarily based startup concerned in synthetic DNA-primarily based information storage and computation. Dave, welcome.

Dave Turek: Thanks Doug. quality to be right here.

Black: Why don’t they start with some simple definitions — what is synthetic DNA and its use in storage and compute; supply us a primary figuring out of the way you keep statistics in DNA.

Turek: by way of artificial DNA, they imply DNA that they manufacture in the laboratory, it’s not the DNA that you simply’re going to find in a living organism. And by means of virtue of doing that, they can really manage and make sure that the DNA that they assemble isn't biologically active. almost, they put stop indications in it, and you definitely can’t take these out. and you couldn’t do the rest with the DNA that we’re working with to make it's biologically lively.

All appropriate, now to code data into DNA they ought to have some variety of scheme to narrate records to the molecule in DNA. And the approach they take is terribly plenty an method corresponding to stacking Lego constructing blocks, if you will. So think about you have a box of Lego items, they’re all of the equal measurement, and naturally everybody is aware Legos, that you could take them and stack them on good of each and every other. but now imagine the Legos all have diverse colorations, so you attain in, you take out five items, you create a stack on good of every different, it’s pink, yellow, eco-friendly, blue, white. You attain in again, you create yet another stack of five items, except now it’s purple, black, brown, green, purple. And simply by means of inspection, you might study and say, ‘neatly, I’ve got two stacks, they seem variety of the identical, they’re the equal length and every little thing. but i can tell they’re distinct because they’re distinctive colors.’

We basically do that with DNA, apart from as an alternative of having Lego building blocks, they create these pre-manufactured artificial pieces of DNA known as oligonucleotides. They’re very, very small in comparison to the sort of DNA you may see in a person, 20 or 30 base pairs in comparison to 3.5 billion. And they take this stuff, and they well-nigh stack them on suitable of every other. The stacking process in DNA is called ligation, and we’re stitching them collectively, in case you will, chemically, and by virtue of doing that, taking small items, join them collectively in a prescribed way to create an extended piece.

That longer piece, which they refer to as an identifier, contains with it two bits of tips. It represents, first of all, an tackle in a bitstream of information. And secondly, it tells you no matter if the cost at that handle is a one or a 0. So going back to my Lego example, if they study their 5 Lego stacks – red, yellow, eco-friendly, blue, on one hand, and brown, pink, black, white, crimson, then again – the first one may be the tackle 2335 in the bitstream, and the 2d one can be the handle 1,235,654 within the identical bitstream. the primary one may have a worth of one, the 2nd one could have a value of zero. And here is what they do with DNA.

Now in back, and you may see this in the monitor, is the desktop that they invented to automate this technique. So they definitely construct these stacks of DNA at a cost of greater than 500,000 per 2nd — 500,000 chemical reactions per second — creating, in case you will, stack fashions of DNA that include the information that dictates both a one or a 0 in a specific address in a bitstream. So standard, you’re working in megabits per 2nd and terabits per day. And here's only a prototype desktop, a laptop design to explore the scalability and other limiting components of manipulating DNA.

Black: So inform us concerning the initial markets, the workloads, the use circumstances, that Catalog is concentrated on?

Turek: smartly, if they begin by speaking about why DNA it’ll begin to provide us some idea of the markets that we’re making an attempt to attack. So we’re the usage of DNA because it’s about 1,000,000 times greater dense in terms of information storage than universal storage media. That’s a big mark downs when it comes to average fees.

The 2nd thing is it by no means goes obsolete. So in case you go in on new generations of tape (storage), periodically, you understand there’ll be innovation. Two years down the highway, you’ll should do migration, you may also should get new machine, and many others. And in case you wait too long with the technology, (it) may develop into out of date effectively on account of the lack of support, when it comes to machine drivers, working gadget and so forth.

DNA molecules are always. If they encode some records in a DNA molecule these days, 100,000 years from now it’ll nonetheless be a DNA molecule, it’ll nevertheless be readable, so there’s no obsolescence concerned in what we've here.

after which the closing aspect is, it basically takes little or no energy. as soon as we, encode statistics into a molecule, they can desiccate it and put in a bit verify tube (that) would doubtless hang the whole contents of the Library of Congress, and it doesn’t require any power to maintain it, it is going to just take a seat there on the shelf, and one thousand years from now that you could select it up and you may function on it.

So with that in mind, of path, people naturally jumped to the conception of: wouldn’t this be a very good cold storage or archive form of potential. And that’s genuine. Their ambition is a bit distinct. What they want to do is to encode the statistics into DNA, but they want to do it in a way that enables energetic storage. So no longer that you simply without difficulty have statistics sitting in a check tube in your shelf, but they feel they will compute on DNA-encoded statistics with extra DNA that they might put into something like this and in reality do search queries, do compute, do many of the issues you can do with general computers, but do it in a extremely low energy, low density, reasonably-priced form of mannequin.

Black: On the energy consumption subject, inform us concerning the relative energy consumption between a DNA storage equipment and its equal disk or tape — natural storage tape-primarily based equipment when it comes to watts, terabytes per watt or different metric. And what use instances are there (which have) no vigor consumption with a DNA primarily based gadget at all?

Turek: The range in energy savings will vary any place from one to two orders of magnitude, when it comes to customary technologies. And once I say well-known applied sciences, I suggest tape, disk and compute bundled together to facilitate the storage of records, the retrieval of information, some analysis of the records. The exact breakdown in terms of these categories remains in the discovery, however we’ve carried out some calculations that indicate we’re within the one to two orders of magnitude change from these types of widely wide-spread technologies.

Domains where energy is without difficulty comfortably absent is in case you have passive storage, and also you’re just inserting statistics into DNA, perhaps desiccated within the variety of examine tube I showed you, and you may put it on a shelf someplace and it simply sits there, no energy required. The quantity of energy required to retrieve it goes to be pretty mild. And the quantity of energy required to compute on it could be a feature of the character of how you compute.

but right here’s an interesting component about DNA and about DNA storage. You ought to droop the theory of file constructions pushed by using physical media that they now have today. So for instance, with tape, data is encoded serially on the tape equipment. And so if you want to discover the 5 millionth bit in that volume of tape, you ought to go through the first four,999,999 bits earlier than you get to it. So in that sense, digital storage has difficulty scaling as statistics grows because the extra records you have got, the extra you must search sequentially, or through an index scheme from supply, to get to the facts you’re targeting.

In a DNA world, they will try this simply with only 1 chemical operation. So statistics isn't saved in a genuine structure the way you believe about electronic media, nevertheless it’s basically a pool of records in a liquid. think of a flask with a bunch of DNA and liquid in it. If I are looking to find a specific piece of data in that, I’ll with no trouble inject a probe into that fluid, and it’ll come returned and we’ll discover the specific piece of facts that I’m attempting to find. It doesn’t should search it in any sort of prescribed means. It’s a big skills when it comes to speed, but there’s also a massive competencies in terms of the volume of energy consumed. I don’t ought to search through billions of bytes of facts to locate what I’m hunting for sequentially. and i can simply go at once to the goal with gigantic rate reductions in time and energy.

Black: captivating. So when does Catalog expect to have a commercially attainable DNA-primarily based compute platform in the marketplace?

Turek: We’re targeting anywhere from two-and-a-half to 3 years from the place they stand these days. Of course, they need to grasp the subject of facts encoding first. and then we've a couple of projects currently underway to examine computation in opposition t that facts.

Computation is nothing greater than the transformation of kept information to create a new type of suggestions no longer at once represented within the kept records. So for example, if you take two matrices and multiply them together, you create a new matrix. That’s the output of the computation. we've about seven or eight distinct computation projects going on nowadays that they think will show up in areas like digital sign processing for your transforms, they now have the skill to create logic gates into Boolean operations, they have the capability to do branching styles of computations as well – ‘if, then, else,’ these forms of issues. they have the skill to function on graphical representations of facts as well.

And they suppose the amalgamation of these different tactics will open up a big set of alternatives in a variety of diverse market segments. they have, for instance, spoken to people who are working in seismic processing the place options that you simply could see mirrored in digital signal processing are acceptable. We’ve talked to americans who're doing inferencing in opposition t graphic files and audio information. once again, the invocation of modified types of capabilities embedded and things like digital signal processing develop into crucial.

we'd are expecting with the aid of the conclusion of 2022 to have an indication of a computational approach to a significant issue in hand at that time. by severe problem, I mean a significant volume of information, a computation that has price at a cost point that people can examine and parent no matter if it presents value to them or now not.

Black: ok, super. Now, Catalog has put out counsel and announcements and so forth that states that Shannon, which is your DNA creator, is able to thousands of chemical reactions per 2d. How does that performance metric translate right into a compute or statistics storage performance metric that HPC users, architects and records middle managers can relate to, equivalent to FLOPS, job run time, terabytes per 2nd, IOPS or latency?

Turek: With respect to storage first, the laptop as it’s at present designed — which is nothing more actually than a prototype machine to support us explore complications of scalability and so forth — produces about 500,000 so referred to as “reaction spots” a second. every a kind of response spots is a vicinity the place this constructing of an extended DNA molecule takes vicinity. So: 500,000 per second.

Now, we've the capability to put in error correction right here as neatly, as a result of there can be a misfire or whatever thing like that in the compositing of the DNA and the place that’s of activity to us. however currently, this laptop is calibrated to be in a position to produce a couple of terabyte of statistics per day. So per se, that’s now not competitive with typical technologies. however remember, this computer isn't a industrial computing device. here's a laptop designed to help us explore limits, design parameters and different environmental kinds of concerns that they will scale up.

It seems the desktop relies on using inkjet printer technology. In back of me which you could see some points of that. They definitely can start to expand the efficiency of the machine exponentially by way of the addition of individual further inkjet printheads. So the nature of ways we’ve architected the gadget says that the boom of efficiency is exponential as they add extra so-called “processing features.” For us the processing features is an inkjet printhead with a few nozzles in it as well. So if you go from let’s say 30 printheads, which is what they now have today, to let’s say one hundred printheads, which is neatly in the realm of possibility, it’s going to be a many, many orders of magnitude enhance within the volume of facts that may well be stored. That’s the storage a part of the equation.

The computation a part of the equation is a little extra nuanced to truly focus on because it’s doubtful no matter if or now not they want to talk about this when it comes to prevalent FLOPS and OPS and issues like that. I suppose the dimension that they wish to put attention on to is the mind-blowing degrees of parallelism, and assist individuals keep in mind the way through which a chemical operation takes region in contrast to a compute operation.

So for example, in a compute operation, it can be an essential where you discuss “do A, then do B, then do C, then do D.” For us, it’s more declarative. they might take this volume of records that we've in a liquid, and they might with ease drop a set of probes into it to start to execute the variety of performance that we’re attempting to symbolize from a computational standpoint. the interpretation may additionally not be as direct as any person might like. however at the conclusion of the day, what they want to do is measure time to insight, that’s the primary criteria.

So if they can invoke degrees of parallelism to head many orders of magnitude beyond what you may do with a conventional supercomputer, and they can by advantage of that dramatically reduced time to perception, those other parameters don't seem to be as expressive in terms of what we’re trying to achieve as the time to insight dimension.

So it’s beginning the undertaking and conclusion the endeavor – how a whole lot time became required? What was the charge of getting from element A to factor B? And we’ll post more on that right through the direction of the arrival 12 months.

the arrival year is all committed to inserting some genuine meat on the bones of what we’re speaking about right here as they movement from the concept that we’ve been engaged on within the final 12 months to the implementation of practical representations of both storage and compute that they spoke of these days.

Black: good enough, very enjoyable stuff. Catalog has put out an announcement that references automation as a key feature. are you able to share how automation is a differentiator for DNA-primarily based expertise and for Catalog?

Turek: tons of the theory in the back of DNA storage is reflected within the literature in terms of what they might call benchtop chemistry, where under very exact conditions laboratory chemists are effectuating the encoding system that they undertake. What we’ve executed is they automatic all of that. And the cause they did it really is twofold. One is, you want to ascertain even if or now not this is commercially potential. And it’s very complicated to extrapolate from benchtop experiments the kinds of issues that you would see if you tried to elevate issues to a commercial degree. In different words, doing it one or two times doesn’t necessarily offer you a clear indication of what happens if you do it 20 million instances.

The different element that automation has accomplished for us is present consistency within the approach. Benchtop chemistry is fraught with refined changes — everyday, grownup to person — that can also deliver compounding types of records from the experiments you’re running and the conclusions that you just draw. by means of automating, they get a manner that’s extra controllable and gives us stronger insight faster in terms of any anomalies they may see, in addition to enabling us to explore the limits of what the possibilities are.

Black: David, finally, describe what you suggest, what Catalogue capacity, by way of “transportable DNA computing platform”?

Turek: well, as which you can see in again of me right here, the machine they now have now could be relatively good sized, it’s within the shape of an L. One leg is probably 13 ft, the different is 10 or eleven, some thing like that. We’re now not portable. but what they want to do is miniaturize every little thing, they wish to make use of microfluidics, they wish to in the reduction of the amount of chemistry involved with the aid of at least three orders of magnitude. They are looking to discover laboratory-on-a-chip sorts of constructs. And ultimately, they can quite simply see ourselves getting this all the way down to a desktop kind of unit, or maybe even making it absolutely portable where you can function both when it comes to information storage, facts manipulation and compute in an atmosphere that’s untethered from the kind of infrastructure that they have here these days.

That’s neatly in all fairness. We’ve already begun to explore ideas like that. and that i believe that as they get settled on the nature of the compute paradigms that they are looking to implement we’ll start the use of that to inform the way they need to pursue this inspiration of miniaturization of the typical manner.

however that you may predict a process that’s laptop in size, that the chemistry is pretty much invisible to you since the volumes are so low, and the performance and the energy consumption could be off the charts.

Black: thanks, David. I’m Doug Black of insideHPC, on behalf of Hyperion analysis, thanks so much for being with us today.

Turek: thank you.




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