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      in Energy

      Ian Hutchinson: A lifetime probing plasma, on Earth and in space

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      Ordinary folks gazing at the night sky can readily spot Earth’s close neighbors and the light of distant stars. But when Ian Hutchinson scans the cosmos, he takes in a great deal more. There is, for instance, the constant rush of plasma — highly charged ionized gases — from the sun. As this plasma flows by solid bodies such as the moon, it interacts with them electromagnetically, sometimes generating a phenomenon called an electron hole — a perturbation in the gaseous solar tide that forms a solitary, long-lived wave. Hutchinson, a professor in the MIT Department of Nuclear Science and Engineering (NSE), knows they exist because he found a way to measure them.

      “When I look up at the moon with my sweetheart, my wife of 48 years, I imagine that streaming from its dark side are electron holes that my students and I predicted, and that we then discovered,” he says. “It’s quite sentimental to me.”

      Hutchinson’s studies of these wave phenomena, summed up in a paper, “Electron holes in phase space: What they are and why they matter,” recently earned the 2022 Ronald C. Davidson Award for Plasma Physics presented by the American Physical Society’s Division of Plasma Physics.

      Measuring perturbations in plasma

      Hutchinson’s exploration of electron holes was sparked by his work over many decades in fusion energy, another branch of plasma physics. He has made many contributions to the design, operation, and experimental investigation of tokamaks — a toroidal magnetic confinement device — intended to replicate and harness the fiery thermonuclear reactions in the plasma of stars for carbon-free energy on Earth. Hutchinson took a particular interest in how to measure the plasma, notably the flow at the edges of tokamaks.

      Heat generated from fusion reactions may escape magnetic confinement and build up along these edges, leading to potential temperature spikes that impact the performance of the confinement device. Hutchinson discovered how to interpret signals from small probes to measure and track plasma velocity at the tokamak’s edge.

      “My theoretical work also showed that these probes quite likely induce electron holes,” he says. But proving this contention required experiments at resolutions in time and space beyond what tokamaks allow. That’s when Hutchinson had an important insight.

      “I realized that the phenomena we were trying to investigate can actually be measured with exquisite accuracy by satellites that travel through plasma surrounding Earth and other solid bodies,” he says. Although plasmas in space are at a much larger scale than the plasmas generated in the laboratory, measurements of these gases by a satellite is analogous “to a situation where we fly a tiny micron-sized spacecraft through the wakes of probes at the edge of tokamaks,” says Hutchinson.

      Using satellite data provided by NASA, Hutchinson set about analyzing solar plasma as it whips by the moon. “We predicted instabilities and the generation of electron holes,” he recounts. “Our theory passed with flying colors: We saw lots of holes in the wake of the moon, and few elsewhere.”

      Developing tokamaks

      Hutchinson grew up in the English midlands and attended Cambridge University, where he became “intrigued by plasma physics in a course taught by an entertaining and effective teacher,” he says.

      Hutchinson headed for doctoral studies at Australian National University on fellowship. The experience afforded him his first opportunity for research on plasma confinement. “There I was at the ends of the Earth, and I was one of very few scientists worldwide with a tokamak almost to myself,” he says. “It was a device that had risen to the top of everyone’s agenda in fusion research as something we really needed to understand.”

      His dissertation, which examined instabilities in plasma, and his hands-on experience with the device, brought him to the attention of Ronald Parker SM ’63, PhD ’67, now emeritus professor of nuclear science and engineering and electrical engineering and computer science, who was building MIT’s Alcator tokamak program.

      In 1976, Hutchinson joined this group, spending three years as a research scientist. After an interval in Britain, he returned to MIT with a faculty position in NSE, and soon, a leadership role in developing the next phase of the Institute’s fusion experiment, the Alcator-C Mod tokamak.

      “This was a major development of the high-magnetic field approach to fusion,” says Hutchinson. Powerful magnets are essential for containing the superhot plasma; the MIT group developed an experiment with a magnetic field more than 150,000 times the strength of the Earth’s magnetic field. “We were in the business of determining whether tokamaks had sufficiently good confinement to function as fusion reactors,” he says.

      Hutchinson oversaw the nearly six-year construction of the device, which was funded by the U.S. Department of Energy. He then led its operation starting in 1993, creating a national facility for experiments that drew scientists and students from around the world. At the time, it was the largest research group on campus at MIT.

      In their studies, scientists employed novel heating and sustainment techniques using radio waves and microwaves. They also discovered new methods for performing diagnostics inside the tokamak. “Alcator C-Mod demonstrated excellent confinement in a more compact and cost-effective device,” says Hutchinson. “It was unique in the world.”

      Hutchinson is proud of Alcator C-Mod’s technological achievements, including its record for highest plasma pressure for a magnetic confinement device. But this large-scale project holds even greater significance for him. “Alcator C-Mod helped beat a new path in fusion research, and has become the basis for the SPARC tokamak now under construction,” he says.

      SPARC is a compact, high-magnetic field fusion energy device under development through a collaboration between MIT’s Plasma Science and Fusion Center and startup Commonwealth Fusions Systems. Its goal is to demonstrate net energy gain from fusion, prove the viability of fusion as a source of carbon-free energy, and tip the scales in the race against climate change. A number of SPARC’s leaders are students Hutchinson taught. “This is a source of considerable satisfaction,” he says. “Some of their down-to-Earth realism comes from me, and perhaps some of their aspirations have been molded by their work with me.” 

      A new phase

      After leading Alcator C-Mod for 15 years and generating hundreds of journal articles, Hutchinson served as NSE’s department head from 2003 to 2009. He wrote the standard textbook on measuring plasmas, and has more recently written “A Student’s Guide to Numerical Methods” (2015), which evolved from a course he taught to introduce graduate students to computational problem-solving in physics and engineering.

      After this, his 40th year on the MIT faculty, Hutchinson will be stepping back from teaching. “It’s important for new generations of students to be taught by people at the pinnacle of their mental and intellectual capacity, and when you reach my age, you’re aware of the fact that you’re slowing down,” he says.

      Hutchinson’s at no loss for ways to spend his time. As a devout Christian, he speaks and writes about the relationship between religion and science, trying to help skeptics on both sides find common ground. He sings in two choral groups, and is very busy grandparenting four grandsons. For a complete change of pace, Hutchinson goes fly fishing.

      But he still has plans to explore new frontiers in plasma physics. “I’m gratified to say I still do important research,” he says. “I’ve solved most of the problems in electron holes, and now I need to say something about ion holes!” More

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      in Energy

      Machine learning facilitates “turbulence tracking” in fusion reactors

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      Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.

      A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions.

      Monitoring the formation and movements of these structures, called filaments or “blobs,” is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics. Individual blob information must be tracked by marking them manually in video data. 

      The researchers built a synthetic video dataset of plasma turbulence to make this process more effective and efficient. They used it to train four computer vision models, each of which identifies and tracks blobs. They trained the models to pinpoint blobs in the same ways that humans would.

      When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved.

      Because millions of video frames are captured during just one fusion experiment, using machine-learning models to track blobs could give scientists much more detailed information.

      “Before, we could get a macroscopic picture of what these structures are doing on average. Now, we have a microscope and the computational power to analyze one event at a time. If we take a step back, what this reveals is the power available from these machine-learning techniques, and ways to use these computational resources to make progress,” says Theodore Golfinopoulos, a research scientist at the MIT Plasma Science and Fusion Center and co-author of a paper detailing these approaches.

      His fellow co-authors include lead author Woonghee “Harry” Han, a physics PhD candidate; senior author Iddo Drori, a visiting professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL), faculty associate professor at Boston University, and adjunct at Columbia University; as well as others from the MIT Plasma Science and Fusion Center, the MIT Department of Civil and Environmental Engineering, and the Swiss Federal Institute of Technology at Lausanne in Switzerland. The research appears today in Nature Scientific Reports.

      Heating things up

      For more than 70 years, scientists have sought to use controlled thermonuclear fusion reactions to develop an energy source. To reach the conditions necessary for a fusion reaction, fuel must be heated to temperatures above 100 million degrees Celsius. (The core of the sun is about 15 million degrees Celsius.)

      A common method for containing this super-hot fuel, called plasma, is to use a tokamak. These devices utilize extremely powerful magnetic fields to hold the plasma in place and control the interaction between the exhaust heat from the plasma and the reactor walls.

      However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor.

      “Knowing what the blobs are doing strongly constrains the engineering performance that your tokamak power plant needs at the edge,” adds Golfinopoulos.

      Researchers use a unique imaging technique to capture video of the plasma’s turbulent edge during experiments. An experimental campaign may last months; a typical day will produce about 30 seconds of data, corresponding to roughly 60 million video frames, with thousands of blobs appearing each second. This makes it impossible to track all blobs manually, so researchers rely on average sampling techniques that only provide broad characteristics of blob size, speed, and frequency.

      “On the other hand, machine learning provides a solution to this by blob-by-blob tracking for every frame, not just average quantities. This gives us much more knowledge about what is happening at the boundary of the plasma,” Han says.

      He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.

      Simulating blobs

      To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.

      “Sometimes they change direction or speed, sometimes multiple blobs merge, or they split apart. These kinds of events were not considered before with traditional approaches, but we could freely simulate those behaviors in the synthetic data,” Han says.

      Creating synthetic data also allowed them to label each blob, which made the training process more effective, Drori adds.

      Using these synthetic data, they trained the models to draw boundaries around blobs, teaching them to closely mimic what a human scientist would draw.

      Then they tested the models using real video data from experiments. First, they measured how closely the boundaries the models drew matched up with actual blob contours.

      But they also wanted to see if the models predicted objects that humans would identify. They asked three human experts to pinpoint the centers of blobs in video frames and checked to see if the models predicted blobs in those same locations.

      The models were able to draw accurate blob boundaries, overlapping with brightness contours which are considered ground-truth, about 80 percent of the time. Their evaluations were similar to those of human experts, and successfully predicted the theory-defined regime of the blob, which agrees with the results from a traditional method.

      Now that they have shown the success of using synthetic data and computer vision models for tracking blobs, the researchers plan to apply these techniques to other problems in fusion research, such as estimating particle transport at the boundary of a plasma, Han says.

      They also made the dataset and models publicly available, and look forward to seeing how other research groups apply these tools to study the dynamics of blobs, says Drori.

      “Prior to this, there was a barrier to entry that mostly the only people working on this problem were plasma physicists, who had the datasets and were using their methods. There is a huge machine-learning and computer-vision community. One goal of this work is to encourage participation in fusion research from the broader machine-learning community toward the broader goal of helping solve the critical problem of climate change,” he adds.

      This research is supported, in part, by the U.S. Department of Energy and the Swiss National Science Foundation. More

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      in Security

      Scientists chart how exercise affects the body

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      Exercise is well-known to help people lose weight and avoid gaining it. However, identifying the cellular mechanisms that underlie this process has proven difficult because so many cells and tissues are involved.

      In a new study in mice that expands researchers’ understanding of how exercise and diet affect the body, MIT and Harvard Medical School researchers have mapped out many of the cells, genes, and cellular pathways that are modified by exercise or high-fat diet. The findings could offer potential targets for drugs that could help to enhance or mimic the benefits of exercise, the researchers say.

      “It is extremely important to understand the molecular mechanisms that are drivers of the beneficial effects of exercise and the detrimental effects of a high-fat diet, so that we can understand how we can intervene, and develop drugs that mimic the impact of exercise across multiple tissues,” says Manolis Kellis, a professor of computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and a member of the Broad Institute of MIT and Harvard.

      The researchers studied mice with high-fat or normal diets, who were either sedentary or given the opportunity to exercise whenever they wanted. Using single-cell RNA sequencing, the researchers cataloged the responses of 53 types of cells found in skeletal muscle and two types of fatty tissue.

      “One of the general points that we found in our study, which is overwhelmingly clear, is how high-fat diets push all of these cells and systems in one way, and exercise seems to be pushing them nearly all in the opposite way,” Kellis says. “It says that exercise can really have a major effect throughout the body.”

      Kellis and Laurie Goodyear, a professor of medicine at Harvard Medical School and senior investigator at the Joslin Diabetes Center, are the senior authors of the study, which appears today in the journal Cell Metabolism. Jiekun Yang, a research scientist in MIT CSAIL; Maria Vamvini, an instructor of medicine at the Joslin Diabetes Center; and Pasquale Nigro, an instructor of medicine at the Joslin Diabetes Center, are the lead authors of the paper.

      The risks of obesity

      Obesity is a growing health problem around the world. In the United States, more than 40 percent of the population is considered obese, and nearly 75 percent is overweight. Being overweight is a risk factor for many diseases, including heart disease, cancer, Alzheimer’s disease, and even infectious diseases such as Covid-19.

      “Obesity, along with aging, is a global factor that contributes to every aspect of human health,” Kellis says.

      Several years ago, his lab performed a study on the FTO gene region, which has been strongly linked to obesity risk. In that 2015 study, the research team found that genes in this region control a pathway that prompts immature fat cells called progenitor adipocytes to either become fat-burning cells or fat-storing cells.

      That finding, which demonstrated a clear genetic component to obesity, motivated Kellis to begin looking at how exercise, a well-known behavioral intervention that can prevent obesity, might act on progenitor adipocytes at the cellular level.

      To explore that question, Kellis and his colleagues decided to perform single-cell RNA sequencing of three types of tissue — skeletal muscle, visceral white adipose tissue (found packed around internal organs, where it stores fat), and subcutaneous white adipose tissue (which is found under the skin and primarily burns fat).

      These tissues came from mice from four different experimental groups. For three weeks, two groups of mice were fed either a normal diet or a high-fat diet. For the next three weeks, each of those two groups were further divided into a sedentary group and an exercise group, which had continuous access to a treadmill.

      By analyzing tissues from those mice, the researchers were able to comprehensively catalog the genes that were activated or suppressed by exercise in 53 different cell types.

      The researchers found that in all three tissue types, mesenchymal stem cells (MSCs) appeared to control many of the diet and exercise-induced effects that they observed. MSCs are stem cells that can differentiate into other cell types, including fat cells and fibroblasts. In adipose tissue, the researchers found that a high-fat diet modulated MSCs’ capacity to differentiate into fat-storing cells, while exercise reversed this effect.

      In addition to promoting fat storage, the researchers found that a high-fat diet also stimulated MSCs to secrete factors that remodel the extracellular matrix (ECM) — a network of proteins and other molecules that surround and support cells and tissues in the body. This ECM remodeling helps provide structure for enlarged fat-storing cells and also creates a more inflammatory environment.

      “As the adipocytes become overloaded with lipids, there’s an extreme amount of stress, and that causes low-grade inflammation, which is systemic and preserved for a long time,” Kellis says. “That is one of the factors that is contributing to many of the adverse effects of obesity.”

      Circadian effects

      The researchers also found that high-fat diets and exercise had opposing effects on cellular pathways that control circadian rhythms — the 24-hour cycles that govern many functions, from sleep to body temperature, hormone release, and digestion. The study revealed that exercise boosts the expression of genes that regulate these rhythms, while a high-fat diet suppresses them.

      “There have been a lot of studies showing that when you eat during the day is extremely important in how you absorb the calories,” Kellis says. “The circadian rhythm connection is a very important one, and shows how obesity and exercise are in fact directly impacting that circadian rhythm in peripheral organs, which could act systemically on distal clocks and regulate stem cell functions and immunity.”

      The researchers then compared their results to a database of human genes that have been linked with metabolic traits. They found that two of the circadian rhythm genes they identified in this study, known as DBP and CDKN1A, have genetic variants that have been associated with a higher risk of obesity in humans.

      “These results help us see the translational values of these targets, and how we could potentially target specific biological processes in specific cell types,” Yang says.

      The researchers are now analyzing samples of small intestine, liver, and brain tissue from the mice in this study, to explore the effects of exercise and high-fat diets on those tissues. They are also conducting work with human volunteers to sample blood and biopsies and study similarities and differences between human and mouse physiology. They hope that their findings will help guide drug developers in designing drugs that might mimic some of the beneficial effects of exercise.

      “The message for everyone should be, eat a healthy diet and exercise if possible,” Kellis says. “For those for whom this is not possible, due to low access to healthy foods, or due to disabilities or other factors that prevent exercise, or simply lack of time to have a healthy diet or a healthy lifestyle, what this study says is that we now have a better handle on the pathways, the specific genes, and the specific molecular and cellular processes that we should be manipulating therapeutically.”

      The research was funded by the National Institutes of Health and the Novo Nordisk Research Center in Seattle. More

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      in Energy

      MIT students contribute to success of historic fusion experiment

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      For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition in a laboratory, a grand challenge of the 21st century. The High-Energy-Density Physics (HEDP) group at MIT’s Plasma Science and Fusion Center has focused on an approach called inertial confinement fusion (ICF), which uses lasers to implode a pellet of fuel in a quest for ignition. This group, including nine former and current MIT students, was crucial to an historic ICF ignition experiment performed in 2021; the results were published on the anniversary of that success.

      On Aug. 8, 2021, researchers at the National Ignition Facility (NIF), Lawrence Livermore National Laboratory (LLNL), used 192 laser beams to illuminate the inside of a tiny gold cylinder encapsulating a spherical capsule filled with deuterium-tritium fuel in their quest to produce significant fusion energy. Although researchers had followed this process many times before, using different parameters, this time the ensuing implosion produced an historic fusion yield of 1.37 megaJoules, as measured by a suite of neutron diagnostics. These included the MIT-developed and analyzed Magnetic Recoil Spectrometer (MRS). This result was published in Physical Review Letters on Aug. 8, the one-year anniversary of the ground-breaking development, unequivocally indicating that the first controlled fusion experiment reached ignition.

      Governed by the Lawson criterion, a plasma ignites when the internal fusion heating power is high enough to overcome the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop that very rapidly increases the plasma temperature. In the case of ICF, ignition is a state where the fusion plasma can initiate a “fuel burn propagation” into the surrounding dense and cold fuel, enabling the possibility of high fusion-energy gain.

      “This historic result certainly demonstrates that the ignition threshold is a real concept, with well-predicted theoretical calculations, and that a fusion plasma can be ignited in a laboratory” says HEDP Division Head Johan Frenje.

      The HEDP division has contributed to the success of the ignition program at the NIF for more than a decade by providing and using a dozen diagnostics, implemented by MIT PhD students and staff, which have been critical for assessing the performance of an implosion. The hundreds of co-authors on the paper attest to the collaborative effort that went into this milestone. MIT’s contributors included the only student co-authors.

      “The students are responsible for implementing and using a diagnostic to obtain data important to the ICF program at the NIF, says Frenje. “Being responsible for running a diagnostic at the NIF has allowed them to actively participate in the scientific dialog and thus get directly exposed to cutting-edge science.”

      Students involved from the MIT Department of Physics were Neel Kabadi, Graeme Sutcliffe, Tim Johnson, Jacob Pearcy, and Ben Reichelt; students from the Department of Nuclear Science and Engineering included Brandon Lahmann, Patrick Adrian, and Justin Kunimune.

      In addition, former student Alex Zylstra PhD ’15, now a physicist at LLNL, was the experimental lead of this record implosion experiment. More

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      in Energy

      Taking a magnifying glass to data center operations

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      When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner — and the team is looking for ways to improve.

      “We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can,” says Siddharth Samsi, a research scientist in the LLSC. 

      To gain insight into this challenge, the LLSC has been collecting detailed data on TX-GAIA usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community.

      Their goal is to empower computer scientists and data center operators to better understand avenues for data center optimization — an important task as processing needs continue to grow. They also see potential for leveraging AI in the data center itself, by using the data to develop models for predicting failure points, optimizing job scheduling, and improving energy efficiency. While cloud providers are actively working on optimizing their data centers, they do not often make their data or models available for the broader high-performance computing (HPC) community to leverage. The release of this dataset and associated code seeks to fill this space.

      “Data centers are changing. We have an explosion of hardware platforms, the types of workloads are evolving, and the types of people who are using data centers is changing,” says Vijay Gadepally, a senior researcher at the LLSC. “Until now, there hasn’t been a great way to analyze the impact to data centers. We see this research and dataset as a big step toward coming up with a principled approach to understanding how these variables interact with each other and then applying AI for insights and improvements.”

      Papers describing the dataset and potential applications have been accepted to a number of venues, including the IEEE International Symposium on High-Performance Computer Architecture, the IEEE International Parallel and Distributed Processing Symposium, the Annual Conference of the North American Chapter of the Association for Computational Linguistics, the IEEE High-Performance and Embedded Computing Conference, and International Conference for High Performance Computing, Networking, Storage and Analysis. 

      Workload classification

      Among the world’s TOP500 supercomputers, TX-GAIA combines traditional computing hardware (central processing units, or CPUs) with nearly 900 graphics processing unit (GPU) accelerators. These NVIDIA GPUs are specialized for deep learning, the class of AI that has given rise to speech recognition and computer vision.

      The dataset covers CPU, GPU, and memory usage by job; scheduling logs; and physical monitoring data. Compared to similar datasets, such as those from Google and Microsoft, the LLSC dataset offers “labeled data, a variety of known AI workloads, and more detailed time series data compared with prior datasets. To our knowledge, it’s one of the most comprehensive and fine-grained datasets available,” Gadepally says. 

      Notably, the team collected time-series data at an unprecedented level of detail: 100-millisecond intervals on every GPU and 10-second intervals on every CPU, as the machines processed more than 3,000 known deep-learning jobs. One of the first goals is to use this labeled dataset to characterize the workloads that different types of deep-learning jobs place on the system. This process would extract features that reveal differences in how the hardware processes natural language models versus image classification or materials design models, for example.   

      The team has now launched the MIT Datacenter Challenge to mobilize this research. The challenge invites researchers to use AI techniques to identify with 95 percent accuracy the type of job that was run, using their labeled time-series data as ground truth.

      Such insights could enable data centers to better match a user’s job request with the hardware best suited for it, potentially conserving energy and improving system performance. Classifying workloads could also allow operators to quickly notice discrepancies resulting from hardware failures, inefficient data access patterns, or unauthorized usage.

      Too many choices

      Today, the LLSC offers tools that let users submit their job and select the processors they want to use, “but it’s a lot of guesswork on the part of users,” Samsi says. “Somebody might want to use the latest GPU, but maybe their computation doesn’t actually need it and they could get just as impressive results on CPUs, or lower-powered machines.”

      Professor Devesh Tiwari at Northeastern University is working with the LLSC team to develop techniques that can help users match their workloads to appropriate hardware. Tiwari explains that the emergence of different types of AI accelerators, GPUs, and CPUs has left users suffering from too many choices. Without the right tools to take advantage of this heterogeneity, they are missing out on the benefits: better performance, lower costs, and greater productivity.

      “We are fixing this very capability gap — making users more productive and helping users do science better and faster without worrying about managing heterogeneous hardware,” says Tiwari. “My PhD student, Baolin Li, is building new capabilities and tools to help HPC users leverage heterogeneity near-optimally without user intervention, using techniques grounded in Bayesian optimization and other learning-based optimization methods. But, this is just the beginning. We are looking into ways to introduce heterogeneity in our data centers in a principled approach to help our users achieve the maximum advantage of heterogeneity autonomously and cost-effectively.”

      Workload classification is the first of many problems to be posed through the Datacenter Challenge. Others include developing AI techniques to predict job failures, conserve energy, or create job scheduling approaches that improve data center cooling efficiencies.

      Energy conservation 

      To mobilize research into greener computing, the team is also planning to release an environmental dataset of TX-GAIA operations, containing rack temperature, power consumption, and other relevant data.

      According to the researchers, huge opportunities exist to improve the power efficiency of HPC systems being used for AI processing. As one example, recent work in the LLSC determined that simple hardware tuning, such as limiting the amount of power an individual GPU can draw, could reduce the energy cost of training an AI model by 20 percent, with only modest increases in computing time. “This reduction translates to approximately an entire week’s worth of household energy for a mere three-hour time increase,” Gadepally says.

      They have also been developing techniques to predict model accuracy, so that users can quickly terminate experiments that are unlikely to yield meaningful results, saving energy. The Datacenter Challenge will share relevant data to enable researchers to explore other opportunities to conserve energy.

      The team expects that lessons learned from this research can be applied to the thousands of data centers operated by the U.S. Department of Defense. The U.S. Air Force is a sponsor of this work, which is being conducted under the USAF-MIT AI Accelerator.

      Other collaborators include researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Charles Leiserson’s Supertech Research Group is investigating performance-enhancing techniques for parallel computing, and research scientist Neil Thompson is designing studies on ways to nudge data center users toward climate-friendly behavior.

      Samsi presented this work at the inaugural AI for Datacenter Optimization (ADOPT’22) workshop last spring as part of the IEEE International Parallel and Distributed Processing Symposium. The workshop officially introduced their Datacenter Challenge to the HPC community.

      “We hope this research will allow us and others who run supercomputing centers to be more responsive to user needs while also reducing the energy consumption at the center level,” Samsi says. More

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      in Energy

      New hardware offers faster computation for artificial intelligence, with much less energy

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      As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

      Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

      A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

      Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

      “With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

      “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

      “The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

      These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

      “Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

      Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

      Accelerating deep learning

      Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

      The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

      In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

      The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

      To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

      PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

      Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

      Surprising speed

      PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

      “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

      “The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

      Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

      Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

      Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

      At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

      “Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

      “The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

      “Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

      “This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

      This research is funded, in part, by the MIT-IBM Watson AI Lab. More

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      in Energy

      How the universe got its magnetic field

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      When we look out into space, all of the astrophysical objects that we see are embedded in magnetic fields. This is true not only in the neighborhood of stars and planets, but also in the deep space between galaxies and galactic clusters. These fields are weak — typically much weaker than those of a refrigerator magnet — but they are dynamically significant in the sense that they have profound effects on the dynamics of the universe. Despite decades of intense interest and research, the origin of these cosmic magnetic fields remains one of the most profound mysteries in cosmology.

      In previous research, scientists came to understand how turbulence, the churning motion common to fluids of all types, could amplify preexisting magnetic fields through the so-called dynamo process. But this remarkable discovery just pushed the mystery one step deeper. If a turbulent dynamo could only amplify an existing field, where did the “seed” magnetic field come from in the first place?

      We wouldn’t have a complete and self-consistent answer to the origin of astrophysical magnetic fields until we understood how the seed fields arose. New work carried out by MIT graduate student Muni Zhou, her advisor Nuno Loureiro, a professor of nuclear science and engineering at MIT, and colleagues at Princeton University and the University of Colorado at Boulder provides an answer that shows the basic processes that generate a field from a completely unmagnetized state to the point where it is strong enough for the dynamo mechanism to take over and amplify the field to the magnitudes that we observe.

      Magnetic fields are everywhere

      Naturally occurring magnetic fields are seen everywhere in the universe. They were first observed on Earth thousands of years ago, through their interaction with magnetized minerals like lodestone, and used for navigation long before people had any understanding of their nature or origin. Magnetism on the sun was discovered at the beginning of the 20th century by its effects on the spectrum of light that the sun emitted. Since then, more powerful telescopes looking deep into space found that the fields were ubiquitous.

      And while scientists had long learned how to make and use permanent magnets and electromagnets, which had all sorts of practical applications, the natural origins of magnetic fields in the universe remained a mystery. Recent work has provided part of the answer, but many aspects of this question are still under debate.

      Amplifying magnetic fields — the dynamo effect

      Scientists started thinking about this problem by considering the way that electric and magnetic fields were produced in the laboratory. When conductors, like copper wire, move in magnetic fields, electric fields are created. These fields, or voltages, can then drive electrical currents. This is how the electricity that we use every day is produced. Through this process of induction, large generators or “dynamos” convert mechanical energy into the electromagnetic energy that powers our homes and offices. A key feature of dynamos is that they need magnetic fields in order to work.

      But out in the universe, there are no obvious wires or big steel structures, so how do the fields arise? Progress on this problem began about a century ago as scientists pondered the source of the Earth’s magnetic field. By then, studies of the propagation of seismic waves showed that much of the Earth, below the cooler surface layers of the mantle, was liquid, and that there was a core composed of molten nickel and iron. Researchers theorized that the convective motion of this hot, electrically conductive liquid and the rotation of the Earth combined in some way to generate the Earth’s field.

      Eventually, models emerged that showed how the convective motion could amplify an existing field. This is an example of “self-organization” — a feature often seen in complex dynamical systems — where large-scale structures grow spontaneously from small-scale dynamics. But just like in a power station, you needed a magnetic field to make a magnetic field.

      A similar process is at work all over the universe. However, in stars and galaxies and in the space between them, the electrically conducting fluid is not molten metal, but plasma — a state of matter that exists at extremely high temperatures where the electrons are ripped away from their atoms. On Earth, plasmas can be seen in lightning or neon lights. In such a medium, the dynamo effect can amplify an existing magnetic field, provided it starts at some minimal level.

      Making the first magnetic fields

      Where does this seed field come from? That’s where the recent work of Zhou and her colleagues, published May 5 in PNAS, comes in. Zhou developed the underlying theory and performed numerical simulations on powerful supercomputers that show how the seed field can be produced and what fundamental processes are at work. An important aspect of the plasma that exists between stars and galaxies is that it is extraordinarily diffuse — typically about one particle per cubic meter. That is a very different situation from the interior of stars, where the particle density is about 30 orders of magnitude higher. The low densities mean that the particles in cosmological plasmas never collide, which has important effects on their behavior that had to be included in the model that these researchers were developing.   

      Calculations performed by the MIT researchers followed the dynamics in these plasmas, which developed from well-ordered waves but became turbulent as the amplitude grew and the interactions became strongly nonlinear. By including detailed effects of the plasma dynamics at small scales on macroscopic astrophysical processes, they demonstrated that the first magnetic fields can be spontaneously produced through generic large-scale motions as simple as sheared flows. Just like the terrestrial examples, mechanical energy was converted into magnetic energy.

      An important output of their computation was the amplitude of the expected spontaneously generated magnetic field. What this showed was that the field amplitude could rise from zero to a level where the plasma is “magnetized” — that is, where the plasma dynamics are strongly affected by the presence of the field. At this point, the traditional dynamo mechanism can take over and raise the fields to the levels that are observed. Thus, their work represents a self-consistent model for the generation of magnetic fields at cosmological scale.

      Professor Ellen Zweibel of the University of Wisconsin at Madison notes that “despite decades of remarkable progress in cosmology, the origin of magnetic fields in the universe remains unknown. It is wonderful to see state-of-the-art plasma physics theory and numerical simulation brought to bear on this fundamental problem.”

      Zhou and co-workers will continue to refine their model and study the handoff from the generation of the seed field to the amplification phase of the dynamo. An important part of their future research will be to determine if the process can work on a time scale consistent with astronomical observations. To quote the researchers, “This work provides the first step in the building of a new paradigm for understanding magnetogenesis in the universe.”

      This work was funded by the National Science Foundation CAREER Award and the Future Investigators of NASA Earth and Space Science Technology (FINESST) grant. More

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      in Energy

      MIT expands research collaboration with Commonwealth Fusion Systems to build net energy fusion machine, SPARC

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      MIT’s Plasma Science and Fusion Center (PSFC) will substantially expand its fusion energy research and education activities under a new five-year agreement with Institute spinout Commonwealth Fusion Systems (CFS).

      “This expanded relationship puts MIT and PSFC in a prime position to be an even stronger academic leader that can help deliver the research and education needs of the burgeoning fusion energy industry, in part by utilizing the world’s first burning plasma and net energy fusion machine, SPARC,” says PSFC director Dennis Whyte. “CFS will build SPARC and develop a commercial fusion product, while MIT PSFC will focus on its core mission of cutting-edge research and education.”

      Commercial fusion energy has the potential to play a significant role in combating climate change, and there is a concurrent increase in interest from the energy sector, governments, and foundations. The new agreement, administered by the MIT Energy Initiative (MITEI), where CFS is a startup member, will help PSFC expand its fusion technology efforts with a wider variety of sponsors. The collaboration enables rapid execution at scale and technology transfer into the commercial sector as soon as possible.

      This new agreement doubles CFS’ financial commitment to PSFC, enabling greater recruitment and support of students, staff, and faculty. “We’ll significantly increase the number of graduate students and postdocs, and just as important they will be working on a more diverse set of fusion science and technology topics,” notes Whyte. It extends the collaboration between PSFC and CFS that resulted in numerous advances toward fusion power plants, including last fall’s demonstration of a high-temperature superconducting (HTS) fusion electromagnet with record-setting field strength of 20 tesla.

      The combined magnetic fusion efforts at PSFC will surpass those in place during the operations of the pioneering Alcator C-Mod tokamak device that operated from 1993 to 2016. This increase in activity reflects a moment when multiple fusion energy technologies are seeing rapidly accelerating development worldwide, and the emergence of a new fusion energy industry that would require thousands of trained people.

      MITEI director Robert Armstrong adds, “Our goal from the beginning was to create a membership model that would allow startups who have specific research challenges to leverage the MITEI ecosystem, including MIT faculty, students, and other MITEI members. The team at the PSFC and MITEI have worked seamlessly to support CFS, and we are excited for this next phase of the relationship.”

      PSFC is supporting CFS’ efforts toward realizing the SPARC fusion platform, which facilitates rapid development and refinement of elements (including HTS magnets) needed to build ARC, a compact, modular, high-field fusion power plant that would set the stage for commercial fusion energy production. The concepts originated in Whyte’s nuclear science and engineering class 22.63 (Principles of Fusion Engineering) and have been carried forward by students and PSFC staff, many of whom helped found CFS; the new activity will expand research into advanced technologies for the envisioned pilot plant.

      “This has been an incredibly effective collaboration that has resulted in a major breakthrough for commercial fusion with the successful demonstration of revolutionary fusion magnet technology that will enable the world’s first commercially relevant net energy fusion device, SPARC, currently under construction,” says Bob Mumgaard SM ’15, PhD ’15, CEO of Commonwealth Fusion Systems. “We look forward to this next phase in the collaboration with MIT as we tackle the critical research challenges ahead for the next steps toward fusion power plant development.”

      In the push for commercial fusion energy, the next five years are critical, requiring intensive work on materials longevity, heat transfer, fuel recycling, maintenance, and other crucial aspects of power plant development. It will need innovation from almost every engineering discipline. “Having great teams working now, it will cut the time needed to move from SPARC to ARC, and really unleash the creativity. And the thing MIT does so well is cut across disciplines,” says Whyte.

      “To address the climate crisis, the world needs to deploy existing clean energy solutions as widely and as quickly as possible, while at the same time developing new technologies — and our goal is that those new technologies will include fusion power,” says Maria T. Zuber, MIT’s vice president for research. “To make new climate solutions a reality, we need focused, sustained collaborations like the one between MIT and Commonwealth Fusion Systems. Delivering fusion power onto the grid is a monumental challenge, and the combined capabilities of these two organizations are what the challenge demands.”

      On a strategic level, climate change and the imperative need for widely implementable carbon-free energy have helped orient the PSFC team toward scalability. “Building one or 10 fusion plants doesn’t make a difference — we have to build thousands,” says Whyte. “The design decisions we make will impact the ability to do that down the road. The real enemy here is time, and we want to remove as many impediments as possible and commit to funding a new generation of scientific leaders. Those are critically important in a field with as much interdisciplinary integration as fusion.” More