Velkommen alle, jeg er min grønne kredsjere til at introducere hendes klikker. Jesus Pérez Rios fra Stonybroek Universitet, som du kan se her. Ja, Jesus har været meget... Hvis man kender hans kæreste kæreste, så har han været i vejret meget. Han tog hans kæreste kæreste i Madrid i 2012. Han tog hans kæreste kæreste kæreste i Lavertois-Emigue-Conton, i Osset, ved Folier du Lille, som kæreste kæreste kæreste. I deres kæreste han til stedet, til Pendue Universitet, og i en gruppe af Chris Screams, som du kan sige om, at nogle af de her kæreste kæreste kæreste kæreste kæreste kæreste kæreste kæreste kæreste kæreste. I den samme universitet, som du fortsætter på, i en gruppe af François Rubégeau, så går du til 2016, og i 2017 og 2018 går du til Universitetet, og så går du til Rappel, for Torico, hvor du var en social professor for to år. Og så går du til Europa igen, i en gruppe af Gerd Meier, der er et fætabære Institut af Max Planckens Society i Berlin, som nogle af os kan vente, at det er en af de bedste places i Europa at være, og være der for et par år, tre år. Og så since last year, you've been at Stonely Brook University, and François has already a very long track record in, I'm saying more or less anything you can do with cold atoms and molecules, ions and so. So even he wrote a book, also an introduction to cold and also cold chemistry atoms, molecules, ions and ribbrax. So this topic you're going to hear about today is just one of the many topics that you are touching upon in your research, so please. This is, I would say, the most provocative of what we do, and it's a way to provoke you guys to do science, like to be more than what you guys do. Okay, so let's start. In a group I want to do some advertising, okay? So we do ammo physics, even though I'm going to talk about dark matter, but I will try to show you the connection, okay? And we do theory and computational, right? So we use computers to the simulations, because sometimes reality is harder than we expected. And we have four lines of research. One is cold and tropical chemistry, as Michael mentioned. We do free-boy physics, where basically we study atmospheric physics, like ozone formation, stuff like that. Then we do data science, where basically we use machine learning techniques to understand better ammo systems. And finally, this is the topic that we're going to discuss today with you guys, is physics beyond the standard model. Of course, if I mention this, it looks like highly physics, but we do ammo physics, with the hope that we can help this guy from highly physics, because they're pretty lost. This is my group. No, indeed they are lost. Everyone is lost somehow, right? So this is my wonderful group. So I have two lines. This is the upper line, the guys in the states.  So they prefer to stay in Europe. So what can I do? Okay, so this is my whole group. And today's talk is about the universe.  So indeed this is kind of the motivation that the old philosophers of, even from the very beginning of human history, this is what helped us to do science, right? So all the scientific knowledge is coming from curiosity. For me, as an ammo scientist, I can see all bunch of stars of planets, like atoms and molecules, right? This is the basic building blocks of the world as we see it, right? And this is kind of the spectra of ammo physics. So we can go as cool as we like it. It's fine, we have BECs as people here doing. And also we can go higher in energy up to this energy. At this energy, you break the hydrogen atom, and then you have electrons and protons, so then we don't have atomic physics anymore. We go into the high energy region, okay? And this is how I see the world. But that's a very narrow view, and I cannot do nothing about it. I'm very ignorant. So then we have the heine physicists that say, no man, we can do even better. We can really truly understand the fundamentals of physics, okay? And this is the standard model. This is the most beautiful theory ever made by humankind, supposed to contain all the information in the universe, okay, and explain everything. That's not true, by the way. But anyway, so we are working through that. So it turns out that all this knowledge, the very beginning of human history, only contains 4% of the energy of the universe. So all the things that you guys do in your lab, all the things that I'm doing, it's only 4% of the universe. So every time they see that, they feel really sad. Really, really sad. I mean, I'm spending my whole life doing this, and I can only understand 4% of the universe, what I'm doing with my life, right? So it's kind of pretty depressing. But this is the way it goes. There's nothing about it. But here's the thing. There are two big chunks that we don't know nothing. Dark matter and dark energy. So let's start with dark matter, okay? So what is dark matter? And this talk is about how we can use, what we know from atoms and molecules to try to figure out a little bit the properties of dark matter. Okay? So let's see what is dark matter. And I will try to convince you that dark matter exists, which most people believe that they do not exist. Okay, whatever. This is a free country, right? Okay. So this is the famous thing about, that you have seen and pressured all of you about the rotation of galaxies, right? So basically, so the rotation of stars. So basically the idea is to measure the velocity of the stars, right? And then as you are moving farther away, the velocity should be lower, right? This is what you would expect. So basically you look into the core of the galaxy as you are moving farther away, you have less stars, you have less mass. I mean, the mass is going to a constant, and then you have to go down. But indeed this is what this is observed. Okay? And that observation is, it goes with 1 over r squared. So the density is different from what you would expect from the typical visual matter. All the matter, like an image light. And this is the famous thing. Okay? Indeed, you can generalize that to any kind of galaxies. So this is a paper that got from 1991. People already knew all these things. Then in order to understand what they observed, you need to include the component of the disk of the halo and also the gas, excuse me. So again, we seem to have evidence that there is something out there that is interacting through gravity, but that's not the mid-light. It's something that we cannot see. What is that? Well, we are so original that the name that we come up was Dark Matter. Not very original because it's dark. Okay? And I'm going to tell you the story about who was the first person to come up with this name. It's a very fine story. Funny and kind of sad, too, I have to say. The real story of Dark Matter starts with the comma cluster. In the same way that you can look into the velocity of stars as a function of the distance from the center of the galaxy, you can look into the velocity of galaxies in a cluster.  Okay? So it's a bunch of things orbiting through each other. And then if you are going far away from the system, you have more mass, and then the galaxies here will be much faster than the galaxies over here, right? So you can go and use the real theorem, which I guess that everyone knows about it. If not, it's time to learn, right? And then you can always infer from the velocity what is the mass that your system has, right? So the guys, I mean, the astronomers, not me, go look at the velocity by measuring the Doppler shift of the lines of hydrogen for instance, and then you can see what is the total mass that is in that cluster, right? That's it. This is how it works. So then what it turns out is like the mass that they need to explain the velocity is 400 times more than what they observe. This is a fact. Okay? This is not a rational fact. This is no theory. This is no prediction. This is just a fact. And indeed the guy that did this first was Fritz Swicky. Sorry. I mean, he's Swiss German, okay? But I don't know German. And this guy, 1933, looking to the Coma Cluster, proposed for the first time, doing a theory, sorry for my German, okay? But he was the first one to introduce that term. And you know what happened. No one believed on him. The whole committee believed that this guy was crazy. Was so crazy. So he was so crazy that has this wonderful quote. And I like it. A strongman started spherical bastards. No matter how you look at them, they are just bastards. And this is true. And he was very pissed off with them, okay? This is, honestly, this is, and he was very pissed off. And I'm going to tell you something. After this guy, I mean, could just start to cry and have some problem. No. The guy was the first one to observe a neutron star. So this guy did all his career at Caltech. And he's a very, very good astronomer by the way. But he has some issue with the dark matter. Then the last test is to look into the comodical scale. So right now we start from galaxies. We move to clusters, which is something bigger. And now we are going to look into the whole universe. I'm going to show you a simulation called the typical embody simulation, which is, I didn't do that. It's a very complicated simulation.  We put it together in a random configuration, and we let the system evolve. Let's see what happens. So you let the system evolve. Matter like to clamp, and for some kind of structure. It's beautiful by the way. And now you can see what that means. Well, this is an example, but now this is this famous result for the simulation. So what we have here, this is observation. So this is like the distribution of clusters of galaxies in the universe. Okay, observation. And this is the simulation. On the model I mentioned before, so you have just a bunch of variants there, you put dark matter, which is something that interacts with gravity. And you see that you can match basically all the observations. This is pretty impressive. And we are talking about cosmological scale, size of the universe. Indeed, in order to understand this very famous figure and pressure, some of you have seen that, which is kind of the famous power spectra of the CMBO cosmic microwave background, had these wonderful peaks. So in order to explain those peaks, you need to use what the so-called lambda CDM. Lambda for the cosmological constant of Albert Einstein and called dark matter. So you need to include dark matter in order to explain this. So again, we have observational evidence that dark matter is out there. Or at least there is something dark that we need to include in our models. So now the thing is like, yeah, we know that it's out there, but what is made of? This is the one million dollar question, maybe more than one million, but important question. So right now I hope to convince you that we have evidence that dark matter is needed to understand galaxies, cluster of galaxies and cosmology. So even in all scales you can explain dark matter. And also you can come up with the idea of modifying gravity, right? That's an alternative. However, those theories can only work in one of them. Of course, this one always will fail. You can only use modified gravity to understand the CMBO. And this one, it will work. So you can modify gravity like with a you have a potential kind to make this guy work. Maybe this guy too, but this one is a problem. So far dark matter is the best theory that we have to understand the universe as the way we see it. Without counting dark energy that's a different business and not for today, okay? That's even harder. I would like to mention this. When I put distance in parsec, even I don't know what is a parsec, right? I know it's big, but I don't know how big it is. So a galaxy is pretty large, right? Way bigger than Europe, right? 10 to 17 kilometers is the size of a galaxy. So we go from 10 to 17 to 10 to the 23 kilometers. So from this scale to 6 kilometers, and still we need dark matter. The same model that can explain everything in this order of magnitude. This is pretty robust evidence that we need this guy. And with that, I would like now to pass about how we detect our matter. Of course, we don't detect our matter because no one ever sees it, right? Otherwise, you will get an overprice. So this is the typical approach. From high in physics, it's like either you do a beam experiment like having high energy particles colliding with some kind of material and then you see you lost some kind of momentum. If you lose some kind of momentum, it means like there is some kind of invisible particle that you are creating. Okay? So that particle should be beyond a standard model. Then we have the typical synone experiment, which is liquid synone. And then if there is an event such that your synone gets ionized, then you will see an assimilation signal. So like kind of spark. This is how we detect synone. Synone experiment. This is the typical approach right now. However, there is something that we have to keep in mind. This is the permitted space for dark matter. And this is one of the main problem. The mass goes. I mean the mass of dark matter can go from 30 to our masses to 10 to minus 22 EV. Okay? So we are talking about more or less 30 hours of magnitude. Parameter space. And when you go to accelerators, you can only, only, I mean there is a huge chunk of it, okay? But usually you are going to affect this range. What about the rest? What you can do with, I mean we need new approaches, new machines, new methods to try to explore different mass ranges of dark matter. And one of them is molecules. So why molecules? Because first, molecules are part of the universe, right? So we are here, made our molecules. And also thanks to the wonderful machining that you guys have in the lab, you can control molecules very well. Much better than anyone in the world. So you can prepare guys in a particular quantum state. You can look into interference. You can look at all kind of different effects. All these things that you guys study are very sensitive to small changes. Dark matter is a small change. So then, if you have full control of your system, you are basically sensitive to any kind of weird stuff. And that's the point why molecules are important. Thanks to you guys. And this is the outlook of my talk. I will try to mention what is the principle of the detection. And then I will show you our proposal for very simple excitation. And finally we talk about the migtal effect, which is a kind of funny thing. The principle, the principle is fairly simple. Indeed, I start in this business thanks to this paper. These guys, of course, was part of the PRD, high energy journal. I was reading that and was like shocked. I always thought, naively, that dark matter is dark, so why should I care, right? It's fine. This is a first statement. Dark matter is mostly dark, but at some point it's not dark. Indeed, these guys propose, like for instance, you have a, let's assume, that you have an H2 gas, a gas of hydrogen, so that you can control everything. Let's put it underground, no background, nothing. Then the principle, hydrogen, should be stable, right? But if one of the hydrogen atoms, sorry, hydrogen molecules breaks down, maybe due to the interaction with neutrinos or maybe with dark matter. So these guys propose that as a way to detect it. And this is something that motivates me. So then in this paper, 2012, there was the idea, like if dark matter interacts with a molecule, basically it could happen at vibrational or rotational excitation. And turns out that the typical energy scale for molecular excitation is going to be very good for light-dark matter. As a result, since the energy spacing of molecules is very small compared with atoms, we have a system that can complement what we can do with typical high-energy physics approach, and then we can explore even more. That is why molecules can be very interesting for high-energy physics. It's not because they like molecules, they don't care about molecules, they don't care about the system, they only care about having a system sensitive to new stuff. The systems that they are used to work, they cannot go to low masses, because the energy of those systems are very high. But molecules are preferred for that. I here with this plot, I just want to convey the general knowledge. Like we have dark matter colliding with your molecule, something will happen and then we have different scenarios. Make that vibrational excitation and then a typical experiment. Or a mental setup that may work, hopefully. Let's see how it works, the vibrational excitation. This is pretty simple. So simple that I is always fun to explain. The idea is the following. Let's assume that we have CO gas, okay? Carbon monoxide, something you can buy in a bottle.  You isolate the guy, you put on the ground everything is good. So, in principle, all the guys should be in the ground state, because there is nothing there that can populate the guy to excited state. Which are cold enough, so you are in thermal equilibrium. Now, let's assume that this is a dark matter event, like colliding with your molecule. Now the molecule will be excited to some vibrational state. So, what happened next? So, the idea is the following. You have that tank full of CO, then you just need to detect the photons, because the guy will be excited, and when they excite, every time they excite, that means you have one, two, three, four, or whatever, you will get photons out. You have to detect them basically surrounding your thing. You're always supposed to see darkness, right? You're supposed not to see any photon, because the guys are in the ground state. But if something excited guy, then you will see the signal of the photons. However, there is something that we have to take care of. Black body relations are there, right? Something that can mess you up. You have to take care of. But there is something that we can work it out. As a result, we can put constraints on the temperature that we need and the density. So, we need to have the gas fairly cold, cold 50 Kelvin, way hotter than what you guys are used to. But this thing is supposed to be big, like meter side. I mean, a clear start of one meter side is not trivial. And the density cannot be too high either, because the density is too high, basically, what can happen, like you can quench the excitation via collision with another molecule. And we want to avoid collisional quenching, right? So we want to have pure photonic emission. Okay? So now I'm going to describe the two main processes that can happen. Let's start with this one. This one we call the cascade process. And it's in a condition such that the guy goes to this state, for instance. And then the density is such that it's solo, that the guy has time to decay to another, another, another. So basically the guy is cascading, because there's no collision. It's very low density. The guy keeps going down and down.  This is the cascade signal. It can happen, on the other hand, that you can be in the opposite regime. You can still have an excitation to the same state. But turns out now you have collisions. These collisions are going to quench your system, right? So you start with guy N, zero. Now this guy will go with one. This guy will go one down.  And there's something cool. You can keep doing that with another guy, another guy, another guy. But at the end, they will go to the V-equal one. So we choose the density or the pressure, whatever you prefer, such that you cannot quench this guy. So all the things will accumulate until you get the V-equal one. So you have a V-equal two end. You have N excitation in the V-equal one. And then you will emit it and get it down. And collect it with your IR photo detector, single photo detector. Okay, that's another business for an auto, how to design these things. But that basically are the two ways how we can detect the signals. Here's an example of how it looks like. A typical signal. Here we have the vibrational state of the CO. And this is the rotational state of CO. Let's assume that the guys start over here. So in this region, the vibrational quenching is very inefficient. So the guy will have cascade. Going cascade like that. At this time, now, the collisions are effective. Because now you're in a vibrational state where it can quench easily. So then the guys start to quench, quench, quench. And then you accumulate. And then you get all these guys out. This is another effect. First time you have another event here. This is what you have. So basically, in the way how we design these things, no matter what happens, you have photons out. You can read them. And more than once, you can measure coincidences. And in that way, you can get almost background free signal. And this is the benefit of using molecules, in particular CO. We use CO, because CO doesn't like to quench. CO is a very weird molecule. Even CO has a lower cross-section for quenching than H2. CO is amazing. It doesn't like to quench. Which is something that we like, too. And this is the end what we get. So this is our prediction. Here, what we have in the y-axis, is the cross-section between that matter. It doesn't matter what kind of model it is. Because we are not interested in that. We are not interested in, like, what is the minimum cross-section between something colliding with our nuclei, such that we can be sensitive, too. As a function of the mass of that thing. And here, in gray, we have things that has been already ruled out by astrophysical observations, seen on experiments, some other things. And you can see this, a big chunk of the parameter space that has not been ruled out. Using our setup, we can be sensitive to this region with something decent. Like 20 centimeter side. An area of 1 centimeter square. It can be done right now. It can be hard, but it can be done. Then, of course, in this business, you always can go to futuristic things, right? Let's assume that now we have a volume of 2 meter side. That may be too futuristic to be true. But anyway, we can do it, right? We are theoreticians. We go to 2 meters. And then the area is 1 meter square. In that case, for the photoetectronus, we can go as low as this. So we can be sensitive to a huge chunk of the parameter space, which again is something that you cannot do with other systems. Another benefit of molecules is the spin. We can play with different isotopes, right? You have carbon 13. You can have different isotopes of oxygen if you like it. As a result, you can be sensitive to a spin, independent or spin-dependent interaction. You can interact with the spin of the neutron, the spin of the proton. As a result, you can be sensitive to even more than matter models. That's for regression. Now we are going to go to the MIGDAL effect. How many of you know what is the MIGDAL effect? No one, right? It's okay. I mean, you don't have to know. I didn't know until a year ago, okay? So it's fine. So this is something that is funny. MIGDAL effect. MIGDAL was like, I worked a baby in 1930, something. I tried to get the baby what is in Russian. I don't understand Russian, so I cannot show to you guys. So the idea was the following. He was envisioning the following problem. I have an atom. But the nuclei are going to be radioactive. I'm going to decay through a beta decay. So now the guy goes to a beta decay. So then the neutron goes to a proton. We have a neutrino and an electron. So basically, your guy will get out one electron. An electron will fly out. As a result, your nuclei will get a recoil. Right? Because your electron is going out, then you have a recoil. If the recoil happened too fast, even you can ionize the atom. That's the MIGDAL effect, because your charge, your nuclei is moving on time. So you have an independent apple moment, right? So that's kind of like a microwave field. So you can ionize and even excite electrons. This is the MIGDAL effect. He did it, of course, for this particular nuclear right of decay. But now we can do it without matter. Now the question, what we have. And what happened, instead of having a right to decay, we have done matter colliding with an atom, let's say. But the recoil is so fast. So the recoil is so fast that it can even ionize or excite your atom or your molecule in this case. And this is what we study. So what we did here is we generalized the idea of MIGDAL to molecules. Okay? And it was pretty fun, to be honest. The idea is like instead of going from one state to another in an atom, now we go from one potential energy curve to another. And now we have different reverberation states, as you know, different states. Okay? And then this recoil can give you from one guy to another. Let's see how that goes. In order to calculate the probability, sorry, this is a bunch of math, but you should be able to handle it, okay? If not tell me please, I will try to make it simple. This is fairly simple. In order to calculate what is the probability to end up in one electronic state, is the following. It's just the overlap between the initial wave function, which you control at your initial state that you're preparing your gas, right? To the final state. And now you have these two guys over here, which is basically playing wave, which is basically like when you have a kick, and these guys are moving. You have different coefficients, that depends on the properties of the nuclei. Okay? And Q here is the momentum transfer. That's the momentum that the dark matter is transferring to your guys. So now we can write the position of my nuclei in the interest of the center of mass, the relative distance, and the position of the electrons. I do that. I put it back. And this is what I got. I got the probability to come separated, as expected in two terms.  We don't care like kind of Frank-Conton factor business. But then we have an electronic coupling here. Let's see how that looks like. I'm pretty sure some of you already know the answer. Right? People doing quantum optics should know that. So what we have here is like this guy. Basically. It's the same as this. We have a penalty over here, which is the mass of the electron versus the mass of the molecule. So usually you will get this like zero. This is very small, but in our case we care. Then we have the momentum transfer, and then we have this guy over here. This guy over here is the transition dipole moment between the excitation. So this is basically the same as having like a typical transition between that guy and that guy. Okay? And then we have the same selection rules that you have for E1. The same selection rules like a dipole allows transition. But the transition is triggered by a dark matter event. Okay? Now the middle effect has another term. And it's the following. We can never forget that the electronic state is not a pure state. What do you mean? Like pure, like what we call the state is a single sigma. It's not really a single sigma. We have some component with other guys, right? The quality of the quantum number that you have depends on the validity of the Bohr-Hemmer approximation. But we can always go beyond Bohr-Hemmer, and then we can use non-adiabatic effects. When we have non-adiabatic effects, basically we have a final wave function plus some kind of new component from other guys. Because we need to see how this electronic state covers for the rest. Using the second-reperation theory, we see that we can compute that excess or that combination wave function like that. And this delta V is just the gradient of the nuclear wave function times the gradient in the real direction. So when you apply that, at the end what you get is like the new chunk of wave function that is shared with other electronic states. Is this thing over here, where this guy is the non-adiabatic coupling term, which is basically, as you expect, it's just the coupling within your electronic state and the gradient of the electronic state. Sandwich, this is what you get. And this is what we calculate, or I calculate how the physicists don't care about how to do that. This is what I do, this is my job. So this is the amplitude for the transition. And how we calculate that is through quantum chemistry. So what I do is I try a different basic set that makes sense for the molecule at hand, this case, CO and two, whatever. Then I optimize the geometry, I choose a different method, I use MRCI, and then type of calculation. Then we do a single point and then we do an electronic cover map. We have to a single point to calculate numerically the gradient and the overlap is necessary to know what is the probability, because we have the sandwich between the wave function and the gradient of the wave function at a different point. And this is what we have as a result. So this is for instance for CO. This is the so-called delimodulation. This is the modulation that you will expect to have in a dark matter signal just because we are, just because the rotation of Earth. So we have a delimodulation, and you think it's very large for both cases. For the center of mass, this case, and also for the non-diabatic coupling. And what is more important is this graph. In this graph I'm showing the neutron, I mean the dark matter, or in this case, anything colliding with your nuclei as a function of the mass of that thing. And as you can see here, this is the state of the art. This is what people have prepared as the best thing. Now they are trying to use silicons. They predict to use semiconductors to detect dark matter, because then after a dark matter event you can excite an electron from the valence band to the conducting band. Okay? So our guy, N2, is over here, not very good, but CO, is as good as semiconductors. So when I'm saying like if you use a CO, the detector as the one that we propose, we can beat or it be as good as semiconductor devices. And they have a different technicalities, but we are competitive with them. So again, molecules are really good and can be a potential good candidate to detect our matter in this range of mass. But you know what is even more funny. MIGDAL proposed the MIGDAL effect in 1930 something. No one ever measured that effect in atoms or molecules. That's funny. Yeah, that's right. No one ever, I don't know how to explain this, no one ever tried to systematically study how MIGDAL effect appears. One way to do it is through neutrons. Neutrons is the closest thing that we have to a dark matter, right? Kind of. It's neutral, so do not interact with the charge. Right? Basically do not interact with anything. As long as you are far away from any nuclear resonance for a fission of fission, you are good to go. And this is what we are trying to do. So we are proposing now in the Overreach National Lab, we are trying to design an experiment such that we can really measure the MIGDAL effect and with that calibrate or proposal to build the real detector for that matter. The idea is the following. We have a gas of CO molecules and we have a neutron beam, very low energy. He has to go low energy because we don't want to buy an ICO. So the neutrons have to be low. That's not a problem, you guys can't do it. There's only two things. Three or four labs in the state that can do that. It's a very complicated and expensive thing and you have to go there. So there's nothing that you can build in your lab with neutrons. So the idea is like now we're going to go from here to there. But the benefit of the MIGDAL effect, then only about the coupling, like you go to a state with different symmetry. So what it means is like, when you excite a guy, it may decay to an intermediate state and this signal is unique. This photon is of resonance with any transition from the ground state to another guy. What it means is like you have a background free detection scheme. Then you put all this with UV photo detector which are fairly simple, way easier than IR photons to detect. So it's using photo multipliers that can work. And then the good thing is like we can do it even at room temperature. Because we only care about vibrational excitation. We don't care about rotation. So even if the gas is in rotation, we don't care. We are good to go. And then we can do it in high pressure. We don't care about collision. As long as we don't quench the electronic state. So we have to be sure like the lifetime of this guy is way longer than the typical collision of time. Sorry, the other way around. Otherwise the guy will decay. Okay? So now to conclude. I hope to convince the Murgurs at gas phase are potential to become an effective thermal detector. We are sensitive to spin and spin independent. The Murgurs are making that effect incorporating non-alibatic effects. An important thing is like we have a daily modulation as I mentioned also directionality. I didn't put a point of that because you guys don't care much about directionality. But the good thing is like if you align your molecules and let you feel or something like that. Then you can see where the momentum transfer happened. And then you can see where the direction of that matter. Because we don't know what is the direction of that matter. We know that we are moving in the galaxy, right? Because the solar system is moving with the sun. In the arm of the galaxy we know what is the velocity but we don't know what is the direction of that matter. And that's something that can help us. And to that I would like to say like maybe we can work together. At least I'm doing that, I'm having a lot of fun. I hope that you guys can also do that. Indeed, Michael already did something about habit and spectroscopy. And so far so good, right? I guess he cannot complain. And now I will use five minutes. This is another thing that we do. So we just published a few reports on this thing. This is one of the models for that matter, okay? Or a physical instrument model. Like this is again an assumption. Okay, you can believe it or not. It's up to you. Standard model particles, everyone believe on that, right? Then we have the so-called hidden sector. Hidden sector is a bunch of weird things that we did in the dark side. We don't know. I mean, we cannot see them. But somehow these are portals that look like very science fiction, right? But this is a real model, okay? There are some portals, different ways to connect the unknown to the known world. Because somehow in our world we should see some evidence of this unknown world. And the portals we can put constraints on how important are these guys. It's something that Michael did. So the idea is like, let's say you have a bunch of molecules in our system. But if there's something out there, there's some kind of vibration or field going through that. So basically your molecules will feel it. So now you go and do hypersignal spectroscopy, your atoms and molecules, you should be able to see or to measure the effects of these weird things that you're not aware of. In this case we choose positronium, which is a fairly simple system. It's just a bound state of a positron and an electron, fairly simple. Okay? And what happened is the following. We have the singlet and the triplet. Triplets are long-lived and singlet. These guys annihilate superfast. They're not good. But then what happened is like in the experiment they can measure these transitions very, very accurately. It's just a way that Michael did in his experiment. But now we can also put constraints on different models like action like particles that fit for what you want. Because they can measure these really well. And then based on that we can see how this thing can be realistic or not. This is an example. Let's assume that we have a scalar interaction. I'm going to assume that they have something out there which interacts with a scalar field. Okay? Then this is what we got. So we have the p-s bound, so the positronium bound. And this is the G2 of the electron. So the geomitiv factor of the electron. The geomitiv factor of the electron is much better than positronium. So long story short, you want to put constraints on this. Use G2 of the electron, not positronium. It's not good. We can go and assume and then we have the following. We have the same as before. These guys were worse. But then we have this dashed line. This dashed line is like if we use reverse states of positronium. You can also do that. Okay? People can do that. But we don't get much. Okay? So the thing is like, if you really want to compete with this guy, they should be able to measure lines in positronium with 100 Hz precision, which is way beyond they can do right now. Right now, the velocity can do 100 kHz. Not the best. So long story short, it's hard that positronium is going to be useful for a physical and a standard model with this scalar model at least. Okay? Then we can do the same for action like particles, which is kind of a spin-spin interaction. And again, long story short, G2 is much better than the positronium. In this case, again, we have to go like very, very high precision, something that now they cannot do it. Maybe in 10 years they could, but maybe it's no point to go for different speedings. And just to finalize, I would like to advertise this. Right, Michael? Do you remember that? I invite him to contribute to this, but he keeps forgetting. It's fine. So we start, I mean, this is a field that now is growing, at least in the States. There is even in demo, there is a new topical group about high precision.  And with that, they can put constraints on many crazy things that you can imagine. One of them is their model. So based on that and also my, I mean, what we like, we start this Cambridge Elements and fix it in the standard model without becoming a regular system. And this is open to anyone to contribute. Okay, it's kind of, these Cambridge Elements is like something in between a chapter and a review. It has something about your personal view of the field and has something about a review of the field. And this is open to anyone. I invite Michael, I hope that he can contribute someday. And this is just a way to advertise what we are doing in this direction. I would like to touch that AIMO and Hanging Physics can work together holding their hands, I hope. And just to finalize, I would like to say that I would like to thank Professor Michael Duesen for the invitation all of you and the funding. And I feel like a kidney can't stop. Thank you very much.


Universal acceptance is a priority for ICANN, not just in promoting awareness around the world and in the DNS industry, but internally as an organization. ICANN Org's engineering and IT team has been working to make our own internal systems and applications UA and EAI ready. We've updated many of our systems to accept, validate, process, store and display non-ASCII characters and long and short TLDs and IDNs. We're currently working closely with our email security vendor to test and ensure that ICANN will be able to support EAI in the near future. By working to make our systems UA ready, ICANN Org is looking to set an example and provide insight to help other organizations achieve UA readiness. Learn how you can too. Thank you.


If you're watching this, you're probably considering a new heating and cooling system for your home. A well-installed system is likely the last 15 to 20 years. If your new HVAC was a newborn, it would be graduating high school before you buy another one. If you choose incorrectly, typically the only way to fix it is to replace it again. That may sound scary, but you already know everything you need to know about HVAC, heating, ventilation, and air conditioning from your car. Every car with air conditioning can do five of six things that every HVAC system should be able to do, but most home HVAC systems can't do any. Let's take a quick look at what those are. First is load matching. By far the most important factor in creating a comfortable space is matching the amount of heating or cooling the HVAC puts out to what the home or car needs at that moment. Every car can vary how much heating or cooling it puts out, as well as the fan speed. Meanwhile, over 80% of home systems are oversized, single stage or single speed systems, meaning they are either on or they are off, making temperatures race up and down, which leads to poor comfort. Second is filtration. When you drive, there's a lot of garbage in the air, car and truck exhaust, pollen, road dust, and so forth. Your home needs to keep those same things out, plus filter out pollutants inside your home. Every car has a high performance filter that helps reduce pollutants getting into your car. Most homes have mediocre filters with far worse performance, where the pressure inside the duct system is too high to use high performance filters. Third is dehumidification. Typically, the most important factor in creating a healthy environment in your car or home is keeping the humidity in a healthy range. By pushing the AC button in your car, you can have dehumidification 24-7-365, but very few homes have this capability. Keeping humidity levels in check reduces the spread of disease, helps prevent mold, mildew, and odors. And this is particularly important in spring and fall when the air is damp, but not much cooling is needed. Fourth is fresh air. Every car can bring in outside air and heat, cool, or filter it. This helps the air smell fresh. It reduces the spread of airborne diseases and is an important factor in indoor air quality. Few homes have this capability. Fifth is mixing. In your car, you can mix and stir the air to keep you comfortable depending on the season by sending it to your face, feet, or the windshield. Mixing the air reduces room-to-room temperature differences in your home and can provide continuous filtration and fresh air as well. Most homes have an inefficient fan motor that makes this noisy and expensive to do. Sixth is humidification. Adding moisture to the air is the only thing that your car can't do. Dry air is uncomfortable, it leads to dry skin, increased dust, and can promote the spread of viruses. Every HVAC system should be able to do these six functions. Load matching, filtration, dehumidification, fresh air, mixing, and humidification. Every car can do five of the six functions of HVAC well. Most home systems can't do any. In case having control over some or all of these conditions is something you want your new system to be capable of, work together with your HVAC 2.0 contractor to choose the path that works best for you. If you'd like to learn more about the six functions of HVAC, see the HVAC 101 guide linked below, or ask your HVAC 2.0 contractor about it.


Now, you have the basic theoretical knowledge of document parser template editor tool. It's time to see this tool in action. In this video, we'll be going to learn how easy it is to extract only the data that you need by defining a template with just a couple of clicks. Now, as I said, document parser template editor tool offers a simple and intuitive parsing interface that allows you to build a template for extracting your vital data. So, before we create our own template and go into the detail, let me show you one sample PDF file and see how easily you can extract out data using premade templates. Now, let's open a sample PDF file by click on this button and as its name implies, it is a template editor tool. Yes, template can be quickly created and updated without requiring any special technical knowledge. You can save templates in your local drive using which you can process another same kind of PDF documents. Well, this template file is nothing but a set of instruction created in YAML or JSON format. Now, let me show you one sample template file and see how it looks like. Alright, so as you can see here that this template file contains a metadata of the extraction region from which we want to extract the data from our PDF file. Now, let's move on to our document parser tool. Alright, now for this sample file, I have already defined the template. So, let me load the template by clicking on this button. Let me select this file. Now, once the template file loaded here, you can see the different different extraction region. I have defined to extract the data like contact person, business name, business address or this whole table itself. So, when you execute this template, you will also get this whole tables data. Now, let's click on run template button to see our extracted data. Alright, so in the grid tab, you can see our defined fields and its value like phone number and email and you can also see the table data over here. See, it's extracted out all the table from the PDF. Currently, these tools support the output data in JSON and YAML and CSV and you can also get this extracted data in plain text. For that, you need to click on this tab and here you can see our data as plain text. You can also achieve the same thing using PDF.co Cloud API. It means you can connect document parser tool with other apps or your own apps also. We will see that demo in a very few minutes. Now, let's dive some deeper and create our own template. So for that, I have to open another sample PDF file. But before that, let us close this result. Let me open another PDF file. Now, let us open this file. Now, as I said earlier that this tool is also able to auto-detect some of the common fields like invoice number, invoice date, etc. So for that, let me click on detect object button. Here you can see the message five fields were detected and added to the template. Let me click on OK button. So as you can see here that some of the templates are automatically defined over here like date issued, invoice number, etc. Now, before we go ahead, let's give the name of our template. You can set some template settings by clicking on this button. Like if you want to versioning your template, then you can set your template version in this text box. And then you can set your culture from this drop down as per your need. And if your PDF contains scan images, then you can set this OCR related settings from here. Now, let's start defining our own template to create the template. All you need to do is define the areas within the PDF. You can define the areas either by manually selecting it like this, say this company address, or by adding different, different extraction object. Now, if we talk about the extraction object, then this tool provide us six different types of option using which we can extract out our data as per the requirement. So first, let us talk about this rectangle field. When you select this option, it will add one rectangle extraction region or object like this. You can place it at particular location from which you want to extract out the data. So in our case, let me put on this build to location. Let me adjust it such that all data are covered. And you can set this object related property over here. And you can also test this individual object by clicking on this button. And here we get our expected data. Now, let us move on. And the next object is rectangle table. And when you select this option, it will add object like this. And you can adjust this object as per your need on the table. Say, for example, I want to extract out only this three rows from this table. Now, once you set this object, you can test this individual object by clicking on this test button. So as you can see here that we will get our expected data. This object will extract data based on coordinates. Now, let me close this and move ahead. Another one is static value field. Say, if you want to define some static value while extracting out the data, then you can use this field. Say, for example, Now, let me add static value for currency. So first, let me give the name. And then let me define the value. And that's it. And same way, you can extract out particular grid cell value using this object. Then next important object is search based field. Now, let me select this option. This object scan the document and search for the text defined in this text box. These tools allows macro based text extraction. You can set the macro by this two button. Now, say if I select this zip number, which is of course a number. Now, when I click on this auto detect macro button, then as you can see here that this tool has automatically detected that, Hey, this is the number pattern. So I should use number macro. Now you can say this button is a kind of magical button. It scans selected area and find first matching macros. This tool uses a lot of macros. Say if you want to manually add the macros, then you can do that by clicking on this insert macro button. And here you can see the list of macros, which have created for different different purposes. Macro is similar to a regular expression. Just instead of writing a complex rejects pattern to detect something, you can use this ready to use macros. For example, instead of using regular expression to detect this date, you can use or you can select date macros from the list or just use this auto detect macro button, which will automatically find matching macros for that area. Macros are the kind of powerful but easier replacement for regular expression. Now let's continue with our search based field object. So this object selects only text that matches a given pattern with the option to include preceding or trailing text. For example, let's create a template for this total. So as you can see that this word starts with the total followed by colon and space. And then there is a dollar and then there is a number. So let us write the expression in this text box. First, we will write total followed by colon and space. Now let me add the macro for this dollar symbol and let me find for dollar. And after dollar, there is a number. So let me add the macro for number. And if I test this object, then I will get the value like this. So same way, all these objects allow the templates to be flexible, simple and powerful. We can continue defining templates until we have selected all of the text which is important in the PDF to extract. And once you are done, you need to run the template through run template button. So let's click on run template button. And here we have our extracted data like invoice number that issued company name, our static value currency, which is USD and our total and our extracted table data. And same thing in JSON and same data in CSV over here. And as I said, you can also achieve the same thing using PDF.co cloud API. So first, let me open the documentation of cloud API for some better understanding.


Hello, everyone. Today we're talking five ways with the CNI. I'm Stig Telfer. I'm CTO of a technical consultancy business called StackHPC. We specialize in high-performance cloud infrastructure and platforms for scientific computing. And I'm Eras Cohen. I'm responsible for cloud networking at NVIDIA. In this talk we will discuss and compare some of the most common Kubernetes networking configurations for performance-intensive workloads. High-performance computing or HPC is a field of computer science that solves complex problems such as fluid dynamics for aircraft design, for example, large-scale weather forecast or that drug discovery through the use of large-scale compute simulations. HPC is one of the most compute network and storage-intensive workloads. Technologies developed for HPC often make their way to more standout data center applications. Another example of compute-intensive applications are artificial intelligence or AI. AI is probably the greatest revolution of our time. It allows computers to solve problems that only a few years ago seemed impossible. AI can create images and text from human description, translate languages, recommend specific items from wealth of options, and even write code. AI is very common these days and used in many web services and other common applications. Both AI and HPC are compute-intensive and they typically cannot run on a single server. They require a cluster of servers and run as a distributed application. When we're running an application in that fashion, networking becomes a critical element for the proper execution of the application. When we're looking at the network consideration, the first and foremost is throughput or bandwidth. And here we would like to have as much as we can. Today we're running at 100 gig and going forward we're already deploying 400 gigabit per second. But latency and the predictability of latency is as important for those kind of workloads. And obviously we would like to have low and predictable latency across all packet types. When we're running a lot of networking, the CPU is very busy with the network itself and concerns quite a lot of cores, CPU cores. We would like obviously to free up those CPUs for running the application itself and therefore we're looking for CPU offload capabilities so that the networking will be handled by the NIC and not by the CPU. And lastly in many of those application environments, GPUs are in play. And we would like to make sure that not only the CPU can access the network properly, but also the GPU. Technologies like GPU Direct become very important. RDMA can address a lot of those requirements and I'll explain RDMA. RDMA or Remote Direct Memory Access is a transport service similar as the TCP and UDP, but it is a more modern one. In addition to send and receive, it also supports memory-ridden write semantics, which allows us to write very efficient applications. It also supports natively kernel bypass, which allows to reduce significantly the latency so that the application can send and receive without going through the kernel. And it supports natively how they're offloads with the right how they're like the NVIDIA how they're, for example, which allows us to transfer hundreds of gigabits per second without any CPU intervention. RDMA was designed initially for technology called Infiniband, but for a long time now it's also available on Ethernet under the name of Frocky. RDMA overconverged Ethernet. In this talk we will see how Rocky can be used with Kubernetes. Now let's review the various network configurations that we will use for this talk and we'll start with Calico. Calico is a popular open source networking and network security platform for Kubernetes created by Tigera. Calico addresses connectivity between pods to pods and pods to service and service to pods and then it includes ingress and egress load balancing. Calico also provides network security features which is out of the scope of our talk today. Calico has a central controller that calculates the policies and routes and store them in an ETCD data store. On each node Calico runs three components. ConfD which monitors the ETCD data store for configuration changes and update bird configuration files. Felix which configures IP tables and Linux routes based on the policies and connectivity. And bird which is an open source BGP agent which advertises Felix routes. Calico supports both BGP and VxLan routing. In our talk we will use VxLan overlay. In addition Calico have two data path implementation. Standard Linux which uses Linux route and IP table as I said before and EBPF which is extended Berkeley packet filters. For our talk we use the standard Linux option. The next CNI is OVN Kubernetes. OVN Kubernetes is the OVN CNI for Kubernetes environment naturally. It uses OVN open virtual networking which is an open source SDM controller that uses OVS. It's from the same project. OVN uses logical components such as switches and routers. It has support security and access control list, load balancers and many other features. OVN programs OVS as its data plane. For those of you that are not familiar with OVS, OVS is a virtual switch that is flow based meaning it can be programmed to implement most of the data plane elements of switches and routers. Next CNI is SRIOV. Single route IO virtualization or SRIOV is a PCI technology which allows to segment the PCI device into virtual devices and assign them to the pod or VM through a virtual function which exposes basically the device or sub-device into the pod or the VM. In networking the device is pretty simple even though it can support advanced capabilities like RDMA. The networking element is pretty simple and it behaves like a MAC VLAN. There's no overlay, no SDN capabilities other than basic switching and quality of service for virtual function. However, SRIOV provides us with very, very high performance close to bare metal as it bypassed the entire virtualization stack. To use SRIOV in Kubernetes we're using the SRIOV CNI and also in addition the SRIOV device plugin. SRIOV CNI cannot support all the network requirements of a pod and therefore it is used typically as a secondary CNI and primary CNI like Calico or OVN together with primary CNI as Calico or OVN through the use of MULTUS which is a meta-plugin, a meta-CNI. Next configuration is accelerated OVN. Before I explain the technology let's first understand the challenges of software-based virtual switches. OVS is built of two components. A kernel model and a user space demon. When a packet comes in it will go to the kernel module or the fast path and classify the packet and see if there is a rule what to do with that packet. If there is a rule the packet will be executed meaning will be sent to the pod or do something else. If there is a miss this packet will go to the user space demon for further analysis and once a decision is made what to do with this new flow it will program the kernel data plane and from that point on packets will just go through the fast path. Now both the kernel module and the user space demon are CPU running on the CPU and the general purpose CPU is not very good in processing high rate of packets. And therefore OVS and many of the other virtual switches have very low bandwidth and very high latency and high CPU utilization. In addition because the interface to the pod or to the VM is ETH pair none of the advanced features of the modern NICs are supported like RDMA and DPDK and so on. To address these challenges NVIDIA added to our NICs an embedded switch. OVS was enhanced through a set of API to program that embedded switch on the NIC and program its data plane. The pods are now connected directly to the NIC through advanced SRIOV mode called eSwitch mode. Every packet that comes from the network will hit the eSwitch first and will classify the packets there. If there is a rule that defines what to do with that the packet will be executed and be sent to the pod and vice versa. If there is no such rule the packet will go into OVS kernel module and from there will continue just like before. With this approach we actually can have the cake and eat it too. We have one primary CNI OVN in this case that supports all the capabilities needed for Kubernetes, all the Kubernetes services, all the network services, the quality of service and visibility and so on. It supports bonding which legacy SRIOV does not and it supports security. But unlike the virtual switches and other CNIs that are software based it provides bare metal performance of the server and in addition it provides additional capabilities that the network adapter may have such as RDMA and DPDK. So those were the configuration that we will run in our lab. Let me just explain what lab did we build. We actually build two labs one for Kubernetes bare metal and one for Kubernetes over OpenStack. On the Kubernetes over bare metal we have two worker nodes. The servers are quite high-end for the performance benchmarking. We're running HPE DL380 Gen10 with 8380 CPUs, two numers each one of 40 cores, so overall 80 cores and half a terabyte of RAM. And those devices and those servers running in Vida Connectix 6DX with two ports of 100 gigabit each. On the OS we're running Ubuntu 2004 with latest kernel and then Vida offered for latest networking drivers. Kubernetes version is 1.23.7 and we deployed with CubeSpray and the pod is running Ubuntu again with the offered packages. The network interfaces are bonded and we're running at 9k MTU connecting to a switch and SN3700, 32 ports of up to 200 gigabit per second. So plenty of interfaces running cumulus Linux 4.4. The masternode is a lower grade server running Ubuntu 2004 and the same Kubernetes version. So this is the bare metal environment. The Kubernetes over OpenStack environment is obviously the same hardware, two compute nodes. But this time on the operating system we did install Yoga OpenStack and we created a VM running Ubuntu 2004 and the latest kernels and offered packages. And inside the VM we basically deployed the same Kubernetes as before and then obviously the pods running in that Kubernetes. The switches and all the rest is exactly the same. Of course in the controller node we did need to install OpenStack Yoga controller. Now the networking on the nested environment, Kubernetes over OpenStack is a little bit tricky. We do have the Connectix device and inside the host we're running OVN with OVS offload. So this OVS is offloaded the data plane into the Connectix as I explained before. Inside the VM we're running Calico which is connected to the OVS non-accelerated and that's the primary network for the pod. And in addition we have a secondary OVN, SRIOV network going to the pod. And so the application can use RDMA which basically goes to the virtual function and then we'll go out through the NIC with the rules and SDN layers of the OVS. Or it can use either the TCP from the net device of the virtual function and then it's accelerated or from the Calico which is non-accelerated. And with this I would like to hand it to Stig to discuss our benchmarks and results. We have an awesome lab environment. Now we need to define some representative test cases. Eris mentioned those key considerations for networking, high bandwidth, low latency, low CPU overhead and predictable fairness under contention. We created a suite of test cases that should be both insightful and representative for HPC and AI applications. This benchmark suite is also open source and available on GitHub today under this link. It uses the volcano drop scheduler to run through a set of test cases and extract results in an automated and repeatable process. It uses Kubeflow's MPI operator to orchestrate the execution of those parallel applications that we're benchmarking. First up we can start on familiar ground by benchmarking TCP performance using the ubiquitous IPERF benchmark. We use version 2 of the application here. We measure aggregate TCP bandwidth for increasing numbers of concurrent IPERF clients. In these tests we're using pod-to-pod networking, which is more representative for these scenarios, rather than include service IPs which would not be representative for most of our use cases. I'm plotting the individual client bandwidth here as a stacked bar. This shows us fairness. Even fairness results in even width stripes and it's fairly easy to see that. The aggregate bandwidth is the total height of the bar. All the graphs are scaled to the physical limits of our lab networking hardware, 200 gigabits a second per server. We set the scene here with the baseline performance of host networking by passing the CNI all together. Now a common choice in high bandwidth networking is to increase the Ethernet frame size or MTU from 1500 pints, which is the standard, to 9000, which is known as a jumbo frame. This has the effect of increasing application bandwidth while reducing the CPU overhead of packet processing. On this slide we have two charts measuring the effect of a 9000-byte MTU and its uplift on performance. It's actually not such a massive difference in results here, due probably to the high-end network cards that we're using. But what is not shown is the extra work behind the scenes that the host kernel is doing in packet processing load for standard frame sizes. This hidden cost is not shown in the end results, but we'll return to expose and explore this a bit later. So we've adopted a 9000-byte MTU for the benchmarks in this presentation as being considered representative of HPC networking use cases. Before we go on though, it's just amazing to see that a modern hardware server can readily saturate 200 gigabits a second of networking. First up, let's start with CNI results with a couple of standard choices. Two of the most popular CNIs for Kubernetes are Calico and OVN. In this slide Calico is the chart on the left and OVN is the chart on the right. Calico is configured with default settings. We've done nothing special here. It's using VXLAN as the transport layer. OVN is configured without SDN acceleration at this stage. It's also at default settings. We'll return to the SDN acceleration shortly. The difference in performance here is stark. We can see that Calico costs almost no additional overhead over host networking and is delivering good fairness. Both CNIs get 30.5 gigabits a second for a single IPer TCP stream. But for OVN, beyond a small number of concurrent TCP streams, our performance tails off to a fraction of peak bandwidth. Why is that? We can speculate about the different fortunes of these two CNIs. For OVN, offloading performance may be impacted by GNIV encapsulation or the complexity of OVN's programmable SDN pipeline. Routing, NAT, security groups and connection tracking are all parts of a flexible solution in OVN but make it harder to comply offloads. OVN may also be affected by latent configuration in our lab environment for hardware acceleration. It's present here but not used for this test, which may be affecting the capacity of the network receive path. It's a theory but we don't have time to test it. For Calico, we appear to be getting benefit from the stateless offloads of the Kinect X6 network cards we are using. In particular, the network card can do TCP segmentation offload to a VXLAN VNI, the kind of networking used by Calico, enabling the kernel's networking stack to process network data in much bigger chunks in a Calico configuration. There's a need here for further investigation, clearly, but for lack of time. With OVN, there may well be room to improve on these results. A popular choice of CNI for high performance use cases is to use SRIOV, in which pod networking has direct access to NIC hardware. Unfortunately, there is no bonding support in legacy SRIOV and our test configuration has bonding enabled. So this restricts us to only one port of our dual port network interface in the test hardware. And this is clear from the results, where the bandwidth quickly hits a cap of 100 gigabits a second. The other network interface, of course, can be used for Calico or another CNI, providing flexible options alongside the SRIOV interface. The state-of-the-art today is the SDN acceleration offloads, as Erez described. We can transfer OVN's SDN flow rules from the host processor to the network card hardware, directly offloading the packet processing work of OpenVSwitch from host kernel to network card. This technology combines the flexibility of OVN software-defined networking with the performance of hardware offloading. But does it fulfil this potential? Well, on the left, we see the accelerated OVN in a bare metal Kubernetes environment. And on the right, we see accelerated OVN in OpenStack, running Kubernetes worker nodes as VMs, providing direct access to hardware using the Mac VLAN CNI. In both cases, our single client TCP performance is over 50 gigabits a second, somehow exceeding the performance of the host networking configuration, which is hard to explain, but may be due to evolutions in our lab setup tuning. With accelerated OVN, we achieve very high performance, quickly saturating the 200 gigabits a second available. I should say the bare metal accelerated OVN graph on the left is actually the best result of five runs. Aggregate performance can be variable, in particular for smaller number of streams in a bonded network configuration. This is likely due to the static nature of lag distribution functions on the bond, creating uneven balance in the flows between the two ports. The same effect would apply to the Mac VLAN chart on the right, but unfortunately we only gathered one result here and didn't have the time to return to this configuration and repeat the experiment. One thing to note is the unfairness apparent in the OVN configuration. The different strike widths of the bare metal accelerated OVN result imply significant unfairness between clients under contention, which requires some further investigation to understand a little better. One theory is that a reduced number of NIC receive queues used in the OVN virtual functions was leading to uneven performance. Cutting edge developments such as these push the capabilities of today's hardware and software to their limits. The benefit we mentioned earlier of hardware offloading is clear when you examine the host CPU load during the test execution. With the Calico test on the top row here, the system's CPU load was measured at about 30% while the tests were underway. With accelerated OVN, the CPU load drops to only 15%, giving a delta of 15% of our CPU load between the two CNIs. Now the CPUs we're using in this test are 40 core platinum Ice Lake Xeons. That's a part with an $8,600 list price. Saving 15% of the CPU cores in this system on a dual socket configuration would be 12 cores or $2,600. If we are working these systems hard, as we really should be, then that's a lot of CPU resource that is not going into our workloads. We can also observe the accelerated OVN flow offloads at work during the benchmarking. In our tests, up to 120 SDN flow rules were offloaded from open V-switch from in the kernel, direct to the NIC hardware. In fact, the Kinect X6 can support hundreds of thousands or even millions of hardware offloaded flows. We're barely scratching the surface of this capability here. This can be particularly powerful for advanced networking applications, concurrently serving many thousands of connections. On to the next test. For HBC and AI applications, we usually implement parallelism using a library called the Message Passing Interface, or MPI. This defines how parallel processes can work together by sharing a dataset and communicating efficiently with one another. MPI embodies a very different paradigm. We don't really see so much of this in cloud computing, and it's vitally important for HBC and also large-scale AI. MPI's design also fits naturally with the RDMA network protocols that Ares was describing earlier. The network performance of MPI can be measured using a suite of benchmarks, a standard one of which is called IMB, or the Intel MPI Benchmarks. One of the simplest of those benchmarks is a pair-wise ping-pong in which messages of different size are bounced between processes on two servers. In this benchmark, we are most interested in the latency for transmitting short messages and the bandwidth for transferring large ones. Here we can see the performance of our range of CNIs for the MPI ping-pong benchmark. The left chart shows short message latency, lower is better. The lowest latency is legacy SRIOV at about 1.83 microseconds message latency. To get a lower latency than this, you would need to go to an advanced HPC network fabric like Infiniband. Accelerated OVN delivers a message latency we measured at about 4.4 microseconds for bare metal and 4.8 microseconds for virtualised open stack configurations. Quantifying the overhead of virtualisation as a small fraction of a microsecond in latency. The right chart shows large message bandwidth, higher is better. The highest performance is accelerated OVN in bare metal and open stack configurations. Accelerated OVN on bare metal reaches over 180 gigabits a second bandwidth for transmitting a 32 megabyte message. In both latency and bandwidth, we can see that hardware offloaded configurations perform far better. We can also see clear advantages of RDMA over TCP, both for lower latency and higher bandwidth. On these charts, results for RDMA protocols are marked with pluses and data points for TCP results are marked with crosses. The difference is clear. So the results for synthetic benchmarks show a clear difference, but what about real world application codes? Computational fluid dynamics, the simulation of air or water flows, for example, is a domain that makes extensive use of parallel programming. Openfoam is the leading open source code for this. We have a benchmark here where we model the vortices created when a fluid blows over the top of a square box, as shown in this animation. To simulate this scenario, we will use 48 parallel processes that work together using MPI. We also wanted to explore the effect of pod organisation. What if we put all the processes in our parallel workload into one pod versus what do we use more pods with fewer processes in each? Within a pod, parallel processes will communicate by using shared memory. Between pods, they should use the local networking but within the host. Between hosts, they will use the Ethernet networking and the CNI. We set up a few test cases to explore the effect of this. So how did we get on? In this chart, we are plotting runtime of our fluid dynamics model for different pod to process geometries. In all cases, we used 48 parallel processes. A lower runtime on this graph means better performance. Disappointingly, there is no obvious effect to the different pod configurations. Our lines are pretty flat. Perhaps MPI was smarter than we thought and was using the same shared memory transport both within pods and between pods on the same node. Unfortunately, we did not have time to return to this setup and investigate this further. What is clear, depending on the CNI and protocol used, our simulation took between 125 seconds and 407 seconds to complete. Network performance is clearly a critical factor for parallel simulations such as this. Highest performance was with accelerated OVN and legacy SRIOV. Accelerated OVN yielded about 125 seconds of runtime. Once again, RDMA protocol data points are marked with pluses and TCP data points are marked with crosses. One interesting thing, we can see the hardware accelerated CNIs that supported RDMA protocol have a clear advantage over everything else. But when the same CNIs run with TCP instead of RDMA, the application performance drops from 125 seconds runtime to 244 seconds. That is almost the same runtime as achieved with Calico. The implication here is that the full potential of the hardware accelerated offloads is only fully realised when using RDMA protocols. Once again, the non-offloaded software only OVN is having a hard time in this configuration. A very different but equally intensive workload is genome sequencing, something that played a vital role of course in the world's response to the COVID pandemic. We wanted to include a test case for base calling, the first stage in the pipeline for processing data for the DNA sequencer technology developed by companies such as Oxford Nanopore. Base calling uses neural networks to extract base sequences from the noises signal data from the sequencer device. This application harnesses the machine learning power of a GPU. About a terabyte of signal data is processed for each genome sequenced. Using open source code from Oxford Nanopore, we created a Kubernetes deployment where the base calling process is presented as a service. We wanted to see what effect this would have on sequencing performance. We measured sequencing performance for host networks and also for CNIs. Unfortunately, for lack of time and due to various integration issues, we only managed to run the base calling benchmark on host networking and the Calico CNI. However, what we can see from this single comparison is encouraging. There was no obvious impact to performance for using Kubernetes service networking for base calling. Indeed, the base calling rate was actually about 1% higher when put behind a service IP in Calico, which is hard to explain, but within the margin of variability of the benchmark performance. In summary, we found that Kubernetes platforms perform well for HBC and AI application workloads at the small to moderate scale of our test lab. Kubernetes would make a convenient job execution framework when coupled with interactive software platforms like Jupyter, and this doesn't have to be a compromise of convenience. For infrastructure without SDN acceleration, our experiments showed Calico performed particularly well, although it is hard to separate out the uplift of all the different offloads and accelerations a modern high-end network card will perform. Further work is definitely needed to understand some of the issues we encountered with performance along the way. For the most demanding use cases, SDN acceleration offloads enable the highest performance, supporting the use of RDMA protocols, which our results showed make a significant difference. Our results also showed that with hardware accelerated networking, performance of Kubernetes both on bare metal hosts and in open stack VMs can be almost indistinguishable from performance on bare metal. With the right networking in place, we can harness the advantages of Kubernetes either on bare metal or in VMs without sacrificing performance. SDN offloads are now at a coming-of-age moment, where the technology remains complex but delivers ultimate performance for cloud-native environments. Finally, I would like to say a huge thanks on behalf of Ares and myself to the NVIDIA and StackHBC engineering teams who performed this research and made this presentation possible. These people are heroes. We hope you've enjoyed it and found it useful. Please do feel free to scan the QR code here and leave us some feedback. Thanks very much.


On the eve of International Women's Day, McCann New York Advertising Agency and its client, State Street Global Advisors installed a statue of a young girl, standing proud and defiant facing the charging iconic bull on Wall Street. This powerful statue sparked a lot of debate about women and especially about women in executive roles and in businesses. I'm here on Sprowl to talk to Cal students about what they think about this image and its meaning. I'm Natalia Ryzak and this is Cal TV News. So what first comes to mind when you look at this image? I see more representation in terms of working on an area in the U.S. or known in the world as Puperdominantly Male. Women empowerment. I think that it's just awesome. It's a really timely thing to have and I think it symbolizes the transitions that this country is going through right now.  So I think that really represents women. This was commissioned on International Women's Day, which was also known as a day without women. Do you know anybody who participated or wore red to show solidarity? I had quite a few girls in my class participate by not coming to school that day. Every girl I know participated whether it was on social media or at Cal. Nat also includes men. I saw a lot of men posting stuff on social media, a lot of men talk about it on campus, which is awesome. My girlfriend wore red and a couple other people I know. I don't know anyone that that went on strike, but I understand if they did. I know a couple people in my community back home that didn't go to work or didn't do the daily things that they usually would do just to like represent the boy that would be created if women weren't in place in the world. And why do you think this was so powerful to be placed in Wall Street? Because it's a traditionally male dominated industry and I think that you know by giving women kind of some image that supports that you know this no longer has to be all about men. It's important to add this piece next to the bull because it's kind of defying something that's been there for so long. I worked on Wall Street over the summer and so it is something that you know you don't see that much management that are of the female gender and so I think having this shows that you know women today are as equally if not smarter than all the guys and we're as equally capable of doing the same things that have been expected and have been done by males. So what do you think about this image? Leave a comment below and tell us your thoughts. I'm Natalia Ryzak and this is Cal TV News. What's up I'm Laura Flores with this week's Cal TV Hotspot recommending


The Mayor has been a great mayor and the city has been transformed under his leadership in a very positive way and so but it's somebody who's going to be new and they're going to have new priorities, new emphasis even if they do some of the same things and it's exciting to see that change. I saw the mayor go through a lot of different changes when he first came into being the mayor of the city you know it's too bad he doesn't have another 10 years of youth left in him. He was a great mayor but I think he ruled with an iron thumb. The B.R.A. is really something I think that needs to be looked at because there's a huge impact on the city and we need to know that the people there are transparent also because it's a lot to benefit from them and the school committee whole new school committee is going to have to come in you know police department whole new police chief I mean what aren't we changing. I think either way most likely each whoever wins will probably be a one term candidate they've been so used to Minino for so long that if it's not working the same way no matter what direction this one tries to lead that eventually they'll just say you know it's not working out we need a different type of candidate. Minino is a fantastic mayor who did a lot of things successfully as mayor and politically that allowed him to be in to returned office and if he ran again this year he would have won again and either John Connelly or Marty Walsh will have to do tremendous job to replicate that and I think it will be very difficult.