An anonymous reader quotes a report from The Verge: Tesla says it has started production of its Dojo supercomputer to train its fleet of autonomous vehicles. In its second quarter earnings report for 2023, the company outlined "four main technology pillars" needed to "solve vehicle autonomy at scale: extremely large real-world dataset, neural net training, vehicle hardware and vehicle software." "We are developing each of these pillars in-house," the company said in its report. "This month, we are taking a step towards faster and cheaper neural net training with the start of production of our Dojo training computer."

The automaker already has a large Nvidia GPU-based supercomputer that is one of the most powerful in the world, but the new Dojo custom-built computer is using chips designed by Tesla. In 2019, Tesla CEO Elon Musk gave this "super powerful training computer" a name: Dojo. Previously, Musk has claimed that Dojo will be capable of an exaflop, or 1 quintillion (1018) floating-point operations per second. That is an incredible amount of power. "To match what a one exaFLOP computer system can do in just one second, you'd have to perform one calculation every second for 31,688,765,000 years," Network World wrote.

Nine days ago the U.S. government released a report on the advantages of studying "scientific and societal implications" of "solar radiation modification" (or SRM) to explore its possible "risks and benefits...as a component of climate policy."

The report's executive summary seems to concede the technique would "negate (explicitly offset) all current or future impacts of climate change" — but would also introduce "an additional change" to "the existing, complex climate system, with ramifications which are not now well understood." Or, as Politico puts it, "The White House cautiously endorsed the idea of studying how to block sunlight from hitting Earth's surface as a way to limit global warming in a congressionally mandated report that could help bring efforts once confined to science fiction into the realm of legitimate debate."

But again, the report endorsed the idea of studying it — to further understand the risks, and also help prepare for "possible deployment of SRM by other public or private actors." Politico emphasized how this report "added a degree of skepticism by noting that Congress has ordered the review, and the administration said it does not signal any new policy decisions related to a process that is sometimes referred to — or derided as — geoengineering." "Climate change is already having profound effects on the physical and natural world, and on human well-being, and these effects will only grow as greenhouse gas concentrations increase and warming continues," the report said. "Understanding these impacts is crucial to enable informed decisions around a possible role for SRM in addressing human hardships associated with climate change..."

The White House said that any potential research on solar radiation modification should be undertaken with "appropriate international cooperation."
It's not just the U.S. making official statements. Their report was released "the same week that European Union leaders opened the door to international discussions of solar radiation modification," according to Politico's report: Policymakers in the European Union have signaled a willingness to begin international discussions of whether and how humanity could limit heating from the sun. "Guided by the precautionary principle, the EU will support international efforts to assess comprehensively the risks and uncertainties of climate interventions, including solar radiation modification and promote discussions on a potential international framework for its governance, including research related aspects," the European Parliament and European Council said in a joint communication.
And it also "follows an open letter by more than 60 leading scientists calling for more research," reports Scientific American. They also note a new supercomputer helping climate scientists model the effects of injecting human-made, sun-blocking aerosols into the stratosphere: The machine, named Derecho, began operating this month at the National Center for Atmospheric Research (NCAR) and will allow scientists to run more detailed weather models for research on solar geoengineering, said Kristen Rasmussen, a climate scientist at Colorado State University who is studying how human-made aerosols, which can be used to deflect sunlight, could affect rainfall patterns... "To understand specific impacts on thunderstorms, we require the use of very high-resolution models that can be run for many, many years," Rasmussen said in an interview. "This faster supercomputer will enable more simulations at longer time frames and at higher resolution than we can currently support..."

The National Academies of Sciences, Engineering and Medicine released a report in 2021 urging scientists to study the impacts of geoengineering, which Rasmussen described as a last resort to address climate change.

"We need to be very cautious," she said. "I am not advocating in any way to move forward on any of these types of mitigation efforts. The best thing to do is to stop fossil fuel emissions as much as we can."

Google says it would take the world's leading supercomputer more than 47 years to match the calculation speed of its newest quantum computer, reports the Telegraph: Four years ago, Google claimed to be the first company to achieve "quantum supremacy" — a milestone point at which quantum computers surpass existing machines. This was challenged at the time by rivals, which argued that Google was exaggerating the difference between its machine and traditional supercomputers. The company's new paper — Phase Transition in Random Circuit Sampling — published on the open access science website ArXiv, demonstrates a more powerful device that aims to end the debate.

While [Google's] 2019 machine had 53 qubits, the building blocks of quantum computers, the next generation device has 70. Adding more qubits improves a quantum computer's power exponentially, meaning the new machine is 241 million times more powerful than the 2019 machine...

Steve Brierley, the chief executive of Cambridge-based quantum company Riverlane, said: "This is a major milestone. The squabbling about whether we had reached, or indeed could reach, quantum supremacy is now resolved."
Thanks to long-time Slashdot reader schwit1 for sharing the article.

Inflection AI, an AI startup company, has built a cutting-edge supercomputer equipped with 22,000 NVIDIA H100 GPUs. Wccftech reports: For those unfamiliar with Inflection AI, it is a business that aims at creating "personal AI for everyone." The company is widely known for its recently introduced Inflection-1 AI model, which powers the Pi chatbot. Although the AI model hasn't yet reached the level of ChatGPT or Google's LaMDA models, reports suggest that Inflection-1 performs well on "common sense" tasks, making it much more suitable for applications such as personal assistance.
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Coming back, Inflection announced that it is building one of the world's largest AI-based supercomputers, and it looks like we finally have a glimpse of what it would be. It is reported that the Inflection supercomputer is equipped with 22,000 H100 GPUs, and based on analysis, it would contain almost 700 four-node racks of Intel Xeon CPUs. The supercomputer will utilize an astounding 31 Mega-Watts of power.

The surprising fact about the supercomputer is the acquisition of 22,000 NVIDIA H100 GPUs. We all are well aware that, in recent times, it has been challenging to acquire even a single unit of the H100s since they are in immense demand, and NVIDIA cannot cope with the influx of orders. In the case of Inflection AI, NVIDIA is considering being an investor in the company, which is why in their case, it is easier to get their hands on such a massive number of GPUs.

Slashdot reader Zapotek brings an update from the Ecsypno skunkworks, where they've been busy with R&D for distributed computing systems: Armed with Cuboid, Qmap was built, which tackled the handling of nmap in a distributed environment, with great results. Afterwards, an iterative clean-up process led to a template of sorts, for scheduling most applications in such environments.

With that, Peplum was born, which allows for OS applications, Ruby code and C/C++/Rust code (via Ruby extensions) to be distributed across machines and tackle the processing of neatly grouped objects.

In essence, Peplum:

- Is a distributed computing solution backed by Cuboid.
- Its basic function is to distribute workloads and deliver payloads across multiple machines and thus parallelize otherwise time consuming tasks.
- Allows you to combine several machines and built a cluster/supercomputer of sorts with great ease.

After that was dealt with, it was time to port Qmap over to Peplum for easier long-term maintenance, thus renamed Peplum::Nmap.

We have high hopes for Peplum as it basically means easy, simple and joyful cloud/clustering/super-computing at home, on-premise, anywhere really. Along with the capability to turn a lot of security oriented apps into super versions of themselves, it is quite the infrastructure.
Yes, this means there's a new solution if you're using multiple machines for "running simulations, to network mapping/security scans, to password cracking/recovery or just encoding your collection of music and video" -- or anything else: Peplum is a F/OSS (MIT licensed) project aimed at making clustering/super-computing affordable and accessible, by making it simple to setup a distributed parallel computing environment for abstract applications... TLDR: You no longer have to only imagine a Beowulf cluster of those, you can now easily build one yourself with Peplum.
Some technical specs: It is written in the Ruby programming language, thus coming with an entire ecosystem of libraries and the capability to run abstract Ruby code, execute external utilities, run OS commands, call C/C++/Rust routines and more...

Peplum is powered by Cuboid, a F/OSS (MIT licensed) abstract framework for distributed computing — both of them are funded by Ecsypno Single Member P.C., a new R&D and Consulting company.

IBM has announced its goal to build a 100,000-qubit quantum computing machine within the next 10 years in collaboration with the University of Tokyo and the University of Chicago. MIT Technology Review reports: Late last year, IBM took the record for the largest quantum computing system with a processor that contained 433 quantum bits, or qubits, the fundamental building blocks of quantum information processing. Now, the company has set its sights on a much bigger target: a 100,000-qubit machine that it aims to build within 10 years. IBM made the announcement on May 22 at the G7 summit in Hiroshima, Japan. The company will partner with the University of Tokyo and the University of Chicago in a $100 million dollar initiative to push quantum computing into the realm of full-scale operation, where the technology could potentially tackle pressing problems that no standard supercomputer can solve.

Or at least it can't solve them alone. The idea is that the 100,000 qubits will work alongside the best "classical" supercomputers to achieve new breakthroughs in drug discovery, fertilizer production, battery performance, and a host of other applications. "I call this quantum-centric supercomputing," IBM's VP of quantum, Jay Gambetta, told MIT Technology Review in an in-person interview in London last week. [...] IBM has already done proof-of-principle experiments (PDF) showing that integrated circuits based on "complementary metal oxide semiconductor" (CMOS) technology can be installed next to the cold qubits to control them with just tens of milliwatts. Beyond that, he admits, the technology required for quantum-centric supercomputing does not yet exist: that is why academic research is a vital part of the project.

The qubits will exist on a type of modular chip that is only just beginning to take shape in IBM labs. Modularity, essential when it will be impossible to put enough qubits on a single chip, requires interconnects that transfer quantum information between modules. IBM's "Kookaburra," a 1,386-qubit multichip processor with a quantum communication link, is under development and slated for release in 2025. Other necessary innovations are where the universities come in. Researchers at Tokyo and Chicago have already made significant strides in areas such as components and communication innovations that could be vital parts of the final product, Gambetta says. He thinks there will likely be many more industry-academic collaborations to come over the next decade. "We have to help the universities do what they do best," he says.

Intel on Monday provided a handful of new details on a chip for artificial intelligence (AI) computing it plans to introduce in 2025 as it shifts strategy to compete against Nvidia and Advanced Micro Devices. From a report: At a supercomputing conference in Germany on Monday, Intel said its forthcoming "Falcon Shores" chip will have 288 gigabytes of memory and support 8-bit floating point computation. Those technical specifications are important as artificial intelligence models similar to services like ChatGPT have exploded in size, and businesses are looking for more powerful chips to run them.

The details are also among the first to trickle out as Intel carries out a strategy shift to catch up to Nvidia, which leads the market in chips for AI, and AMD, which is expected to challenge Nvidia's position with a chip called the MI300. Intel, by contrast, has essentially no market share after its would-be Nvidia competitor, a chip called Ponte Vecchio, suffered years of delays. Intel on Monday said it has nearly completed shipments for Argonne National Lab's Aurora supercomputer based on Ponte Vecchio, which Intel claims has better performance than Nvidia's latest AI chip, the H100. But Intel's Falcon Shores follow-on chip won't be to market until 2025, when Nvidia will likely have another chip of its own out.

The Top 500 list of the fastest supercomputers in the world was released today, and AMD continues its streak of impressive wins with 121 systems now powered by AMD's silicon -- a year-over-year increase of 29%. From a report: Additionally, AMD continues to hold the #1 spot on the Top 500 with the Frontier supercomputer, while the test and development system based on the same architecture continues to hold the second spot in power efficiency metrics on the Green 500 list. Overall, AMD also powers seven of the top ten systems on the Green 500 list. The AMD-powered Frontier remains the only fully-qualified exascale-class supercomputer on the planet, as the Intel-powered two-exaflop Aurora has still not submitted a benchmark result after years of delays.

In contrast, Frontier is now fully operational and is being used by researchers in a multitude of science workloads. In fact, Frontier continues to improve from tuning -- the system entered the Top 500 list with 1.02 exaflops of performance in June 2022 but has now improved to 1.194 exaflops, a 17% increase. That's an impressive increase from the same 8,699,904 CPU cores it debuted with. For perspective, that extra 92 petaflops of performance from tuning represents the same amount of computational horsepower as the entire Perlmutter system that ranks eighth on the Top 500.

An anonymous reader shared this report from the Verge: Meta is building its first custom chip specifically for running AI models, the company announced on Thursday. As Meta increases its AI efforts — CEO Mark Zuckerberg recently said the company sees "an opportunity to introduce AI agents to billions of people in ways that will be useful and meaningful" — the chip and other infrastructure plans revealed Thursday could be critical tools for Meta to compete with other tech giants also investing significant resources into AI.

Meta's new MTIA chip, which stands for Meta Training and Inference Accelerator, is its "in-house, custom accelerator chip family targeting inference workloads," Meta VP and head of infrastructure Santosh Janardhan wrote in a blog post... But the MTIA chip is seemingly a long ways away: it's not set to come out until 2025, TechCrunch reports.
Meta has been working on "a massive project to upgrade its AI infrastructure in the past year," Reuters reports, "after executives realized it lacked the hardware and software to support demand from product teams building AI-powered features."

As a result, the company scrapped plans for a large-scale rollout of an in-house inference chip and started work on a more ambitious chip capable of performing training and inference, Reuters reported...

Meta said it has an AI-powered system to help its engineers create computer code, similar to tools offered by Microsoft, Amazon and Alphabet.
TechCrunch calls these announcements "an attempt at a projection of strength from Meta, which historically has been slow to adopt AI-friendly hardware systems — hobbling its ability to keep pace with rivals such as Google and Microsoft."

Meta's VP of Infrastructure told TechCrunch "This level of vertical integration is needed to push the boundaries of AI research at scale." Over the past decade or so, Meta has spent billions of dollars recruiting top data scientists and building new kinds of AI, including AI that now powers the discovery engines, moderation filters and ad recommenders found throughout its apps and services. But the company has struggled to turn many of its more ambitious AI research innovations into products, particularly on the generative AI front. Until 2022, Meta largely ran its AI workloads using a combination of CPUs — which tend to be less efficient for those sorts of tasks than GPUs — and a custom chip designed for accelerating AI algorithms...

The MTIA is an ASIC, a kind of chip that combines different circuits on one board, allowing it to be programmed to carry out one or many tasks in parallel... Custom AI chips are increasingly the name of the game among the Big Tech players. Google created a processor, the TPU (short for "tensor processing unit"), to train large generative AI systems like PaLM-2 and Imagen. Amazon offers proprietary chips to AWS customers both for training (Trainium) and inferencing (Inferentia). And Microsoft, reportedly, is working with AMD to develop an in-house AI chip called Athena.

Meta says that it created the first generation of the MTIA — MTIA v1 — in 2020, built on a 7-nanometer process. It can scale beyond its internal 128 MB of memory to up to 128 GB, and in a Meta-designed benchmark test — which, of course, has to be taken with a grain of salt — Meta claims that the MTIA handled "low-complexity" and "medium-complexity" AI models more efficiently than a GPU. Work remains to be done in the memory and networking areas of the chip, Meta says, which present bottlenecks as the size of AI models grow, requiring workloads to be split up across several chips. (Not coincidentally, Meta recently acquired an Oslo-based team building AI networking tech at British chip unicorn Graphcore.) And for now, the MTIA's focus is strictly on inference — not training — for "recommendation workloads" across Meta's app family...

If there's a common thread in today's hardware announcements, it's that Meta's attempting desperately to pick up the pace where it concerns AI, specifically generative AI... In part, Meta's feeling increasing pressure from investors concerned that the company's not moving fast enough to capture the (potentially large) market for generative AI. It has no answer — yet — to chatbots like Bard, Bing Chat or ChatGPT. Nor has it made much progress on image generation, another key segment that's seen explosive growth.

If the predictions are right, the total addressable market for generative AI software could be $150 billion. Goldman Sachs predicts that it'll raise GDP by 7%. Even a small slice of that could erase the billions Meta's lost in investments in "metaverse" technologies like augmented reality headsets, meetings software and VR playgrounds like Horizon Worlds.

Alphabet's Google released new details about the supercomputers it uses to train its artificial intelligence models, saying the systems are both faster and more power-efficient than comparable systems from Nvidia. From a report: Google has designed its own custom chip called the Tensor Processing Unit, or TPU. It uses those chips for more than 90% of the company's work on artificial intelligence training, the process of feeding data through models to make them useful at tasks such as responding to queries with human-like text or generating images. The Google TPU is now in its fourth generation. Google on Tuesday published a scientific paper detailing how it has strung more than 4,000 of the chips together into a supercomputer using its own custom-developed optical switches to help connect individual machines.

Improving these connections has become a key point of competition among companies that build AI supercomputers because so-called large language models that power technologies like Google's Bard or OpenAI's ChatGPT have exploded in size, meaning they are far too large to store on a single chip. The models must instead be split across thousands of chips, which must then work together for weeks or more to train the model. Google's PaLM model - its largest publicly disclosed language model to date - was trained by splitting it across two of the 4,000-chip supercomputers over 50 days.

The US chip-maker Nvidia has said cryptocurrencies do not "bring anything useful for society" despite the company's powerful processors selling in huge quantities to the sector. From a report: Michael Kagan, its chief technology officer, said other uses of processing power such as the artificial intelligence chatbot ChatGPT were more worthwhile than mining crypto. Nvidia never embraced the crypto community with open arms. In 2021, the company even released software that artificially constrained the ability to use its graphics cards from being used to mine the popular Ethereum cryptocurrency, in an effort to ensure supply went to its preferred customers instead, who include AI researchers and gamers. Kagan said the decision was justified because of the limited value of using processing power to mine cryptocurrencies.

The first version ChatGPT was trained on a supercomputer made up of about 10,000 Nvidia graphics cards. "All this crypto stuff, it needed parallel processing, and [Nvidia] is the best, so people just programmed it to use for this purpose. They bought a lot of stuff, and then eventually it collapsed, because it doesn't bring anything useful for society. AI does," Kagan told the Guardian. "With ChatGPT, everybody can now create his own machine, his own programme: you just tell it what to do, and it will. And if it doesn't work the way you want it to, you tell it 'I want something different.'" Crypto, by contrast, was more like high-frequency trading, an industry that had led to a lot of business for Mellanox, the company Kagan founded before it was acquired by Nvidia. "We were heavily involved in also trading: people on Wall Street were buying our stuff to save a few nanoseconds on the wire, the banks were doing crazy things like pulling the fibres under the Hudson taut to make them a little bit shorter, to save a few nanoseconds between their datacentre and the stock exchange," he said. "I never believed that [crypto] is something that will do something good for humanity. You know, people do crazy things, but they buy your stuff, you sell them stuff. But you don't redirect the company to support whatever it is."

An anonymous reader writes: Last week, we learned that Microsoft spent hundreds of millions of dollars to buy tens of thousands of Nvidia A100 graphics chips so that partner OpenAI could train the large language models (LLMs) behind Bing's AI chatbot and ChatGPT.

Don't have access to all that capital or space for all that hardware for your own LLM project? Nvidia's DGX Cloud is an attempt to sell remote web access to the very same thing. Announced today at the company's 2023 GPU Technology Conference, the service rents virtual versions of its DGX Server boxes, each containing eight Nvidia H100 or A100 GPUs and 640GB of memory. The service includes interconnects that scale up to the neighborhood of 32,000 GPUs, storage, software, and "direct access to Nvidia AI experts who optimize your code," starting at $36,999 a month for the A100 tier.

Meanwhile, a physical DGX Server box can cost upwards of $200,000 for the same hardware if you're buying it outright, and that doesn't count the efforts companies like Microsoft say they made to build working data centers around the technology.

An anonymous reader quotes a report from The Guardian: The UK government is to invest 900 million pounds in a cutting-edge supercomputer as part of an artificial intelligence strategy that includes ensuring the country can build its own "BritGPT". The treasury outlined plans to spend around 900 million pounds on building an exascale computer, which would be several times more powerful than the UK's biggest computers, and establishing a new AI research body. An exascale computer can be used for training complex AI models, but also have other uses across science, industry and defense, including modeling weather forecasts and climate projections. The Treasury said the investment will "allow researchers to better understand climate change, power the discovery of new drugs and maximize our potential in AI.".

An exascale computer is one that can carry out more than one billion billion simple calculations a second, a metric known as an "exaflops". Only one such machine is known to exist, Frontier, which is housed at America's Oak Ridge National Laboratory and used for scientific research -- although supercomputers have such important military applications that it may be the case that others already exist but are not acknowledged by their owners. Frontier, which cost about 500 million pounds to produce and came online in 2022, is more than twice as powerful as the next fastest machine.

The Treasury said it would award a 1 million-pound prize every year for the next 10 years to the most groundbreaking AI research. The award will be called the Manchester Prize, in memory of the so-called Manchester Baby, a forerunner of the modern computer built at the University of Manchester in 1948. The government will also invest 2.5 billion pounds over the next decade in quantum technologies. Quantum computing is based on quantum physics -- which looks at how the subatomic particles that make up the universe work -- and quantum computers are capable of computing their way through vast numbers of different outcomes.

When Microsoft invested $1 billion in OpenAI in 2019, it agreed to build a massive, cutting-edge supercomputer for the artificial intelligence research startup. The only problem: Microsoft didn't have anything like what OpenAI needed and wasn't totally sure it could build something that big in its Azure cloud service without it breaking. From a report: OpenAI was trying to train an increasingly large set of artificial intelligence programs called models, which were ingesting greater volumes of data and learning more and more parameters, the variables the AI system has sussed out through training and retraining. That meant OpenAI needed access to powerful cloud computing services for long periods of time. To meet that challenge, Microsoft had to find ways to string together tens of thousands of Nvidia's A100 graphics chips -- the workhorse for training AI models -- and change how it positions servers on racks to prevent power outages. Scott Guthrie, the Microsoft executive vice president who oversees cloud and AI, wouldn't give a specific cost for the project, but said "it's probably larger" than several hundred million dollars. [...] Now Microsoft uses that same set of resources it built for OpenAI to train and run its own large artificial intelligence models, including the new Bing search bot introduced last month. It also sells the system to other customers. The software giant is already at work on the next generation of the AI supercomputer, part of an expanded deal with OpenAI in which Microsoft added $10 billion to its investment.

"IBM is the latest tech giant to unveil its own "AI supercomputer," this one composed of a bunch of virtual machines running within IBM Cloud," reports the Register: The system known as Vela, which the company claims has been online since May last year, is touted as IBM's first AI-optimized, cloud-native supercomputer, created with the aim of developing and training large-scale AI models. Before anyone rushes off to sign up for access, IBM stated that the platform is currently reserved for use by the IBM Research community. In fact, Vela has become the company's "go-to environment" for researchers creating advanced AI capabilities since May 2022, including work on foundation models, it said.

IBM states that it chose this architecture because it gives the company greater flexibility to scale up as required, and also the ability to deploy similar infrastructure into any IBM Cloud datacenter around the globe. But Vela is not running on any old standard IBM Cloud node hardware; each is a twin-socket system with 2nd Gen Xeon Scalable processors configured with 1.5TB of DRAM, and four 3.2TB NVMe flash drives, plus eight 80GB Nvidia A100 GPUs, the latter connected by NVLink and NVSwitch. This makes the Vela infrastructure closer to that of a high performance compute site than typical cloud infrastructure, despite IBM's insistence that it was taking a different path as "traditional supercomputers weren't designed for AI."

It is also notable that IBM chose to use x86 processors rather than its own Power 10 chips, especially as these were touted by Big Blue as being ideally suited for memory-intensive workloads such as large-model AI inferencing.
Thanks to Slashdot reader guest reader for sharing the story.

sciencehabit shares a report from Science Magazine: Fifty years ago today, astronauts aboard Apollo 17, NASA's last crewed mission to the Moon, took an iconic photograph of our planet. The image became known as the Blue Marble -- the first fully illuminated picture of Earth, in color, taken by a person. Now, scientists have re-created that image during a test run of a cutting-edge digital climate model. The model can simulate climatic phenomena, such as storms and ocean eddies, at 1-kilometer resolution, as much as 100 times sharper than typical global simulations.

To re-create the swirling winds of the Blue Marble -- including a cyclone over the Indian Ocean -- the researchers fed weather records from 1972 into the supercomputer-powered software. The resulting world captured distinctive features of the region, such as upwelling waters off the coast of Namibia and long, reedlike cloud coverage. Experts say the stunt highlights the growing sophistication of high-resolution climate models. Those are expected to form the core of the European Union's Destination Earth project, which aims to create a 'digital twin' of Earth to better forecast extreme weather and guide preparation plans.

The next wave of Moore's Law will rely on a developing concept called system technology co-optimization, Ann B. Kelleher, general manager of technology development at Intel told IEEE Spectrum in an interview ahead of her plenary talk at the 2022 IEEE Electron Device Meeting. From a report: "Moore's Law is about increasing the integration of functions," says Kelleher. "As we look forward into the next 10 to 20 years, there's a pipeline full of innovation" that will continue the cadence of improved products every two years. That path includes the usual continued improvements in semiconductor processes and design, but system technology co-optimization (STCO) will make the biggest difference. Kelleher calls it an "outside-in" manner of development. It starts with the workload a product needs to support and its software, then works down to system architecture, then what type of silicon must be within a package, and finally down to the semiconductor manufacturing process. "With system technology co-optimization, it means all the pieces are optimized together so that you're getting your best answer for the end product," she says.

STCO is an option now in large part because advanced packaging, such as 3D integration, is allowing the high-bandwidth connection of chiplets -- small, functional chips -- inside a single package. This means that what would once be functions on a single chip can be disaggregated onto dedicated chiplets, which can each then be made using the most optimal semiconductor process technology. For example, Kelleher points out in her plenary that high-performance computing demands a large amount of cache memory per processor core, but chipmaker's ability to shrink SRAM is not proceeding at the same pace as the scaling down of logic. So it makes sense to build SRAM caches and compute cores as separate chiplets using different process technology and then stitch them together using 3D integration. A key example of STCO in action, says Kelleher, is the Ponte Vecchio processor at the heart of the Aurora supercomputer. It's composed of 47 active chiplets (as well as 8 blanks for thermal conduction). These are stitched together using both advanced horizontal connections (2.5 packaging tech) and 3D stacking. "It brings together silicon from different fabs and enables them to come together so that the system is able to perform against the workload that it's designed for," she says.

Nvidia and Microsoft announced Wednesday a multi-year collaboration to build an AI supercomputer in the cloud, adding tens of thousands of Nvidia GPUs to Microsoft Azure. ZDNet: The new agreement makes Azure the first public cloud to incorporate Nvidia's full AI stack -- its GPUs, networking, and AI software. By beefing up Azure's infrastructure with Nvidia's full AI suite, more enterprises will be able to train, deploy, and scale AI -- including large, state-of-the-art models. "AI technology advances as well as industry adoption are accelerating," Manuvir Das, Nvidia's VP of enterprise computing, said in a statement. "The breakthrough of foundation models has triggered a tidal wave of research, fostered new startups, and enabled new enterprise applications."

Intel's latest plan to ward off rivals from high-performance computing workloads involves a CPU with large stacks of high-bandwidth memory and new kinds of accelerators, plus its long-awaited datacenter GPU that will go head-to-head against Nvidia's most powerful chips. From a report: After multiple delays, the x86 giant on Wednesday formally introduced the new Xeon CPU family formerly known as Sapphire Rapids HBM and its new datacenter GPU better known as Ponte Vecchio. Now you will know them as the Intel Xeon CPU Max Series and the Intel Data Center GPU Max Series, respectively, which were among the bevy of details shared by Intel today, including performance comparisons. These chips, set to arrive in early 2023 alongside the vanilla 4th generation Xeon Scalable CPUs, have been a source of curiosity within the HPC community for years because they will power the US Department of Energy's long-delayed Aurora supercomputer, which is expected to become the country's second exascale supercomputer and, consequently, one of the world's fastest.

In a briefing with journalists, Jeff McVeigh, the head of Intel's Super Compute Group, said the Max name represents the company's desire to maximize the bandwidth, compute and other capabilities for a wide range of HPC applications, whose primary users include governments, research labs, and corporations. McVeigh did admit that Intel has fumbled in how long it took the company to commercialize these chips, but he tried to spin the blunders into a higher purpose. "We're always going to be pushing the envelope. Sometimes that causes us to maybe not achieve it, but we're doing that in service of helping our developers, helping the ecosystem to help solve [the world's] biggest challenges," he said. [...] The Xeon Max Series will pack up to 56 performance cores, which are based on the same Golden Cove microarchitecture features as Intel's 12th-Gen Core CPUs, which debuted last year. Like the vanilla Sapphire Rapids chips coming next year, these chips will support DDR5, PCIe 5.0 and Compute Express Link (CXL) 1.1, which will enable memory to be directly attached to the CPU over PCIe 5.0.

The NOEMA radio telescope, located on the Plateau de Bure in the French Alps, is now equipped with twelve antennas, making it the most powerful radio telescope of its kind in the northern hemisphere. Phys.Org reports: Eight years after the inauguration of the first NOEMA antenna in 2014, the large-scale European project is now complete. Thanks to its twelve 15-meter antennas, which can be moved back and forth on a specially developed rail system up to a distance of 1.7 kilometers long, NOEMA is a unique instrument for astronomical research. The telescope is equipped with highly sensitive receiving systems that operate close at the quantum limit. During observations, the observatory's twelve antennas act as a single telescope -- a technique called interferometry. After all the antennas have been pointed towards one and the same region of space, the signals they receive are combined with the help of a supercomputer. Their detailed resolution then corresponds to that of a huge telescope whose diameter is equal to the distance between the outermost antennas.

The respective arrangement of the antennas can extend over distances from a few hundred meters to 1.7 kilometers. The network thus functions like a camera with a variable lens. The further apart the antennas are, the more powerful is the zoom: the maximum spatial resolution of NOEMA is so high that it would be able to detect a mobile phone at a distance of over 500 kilometers. NOEMA is one of the few radio observatories worldwide that can simultaneously detect and measure a large number of signatures -- i.e., "fingerprints" of molecules and atoms. Thanks to these so-called multi-line observations, combined with high sensitivity, NOEMA is a unique instrument for investigating the complexity of cold matter in interstellar space as well as the building blocks of the university. With NOEMA, over 5,000 researchers from all over the world study the composition and dynamics of galaxies as well as the birth and death of stars, comets in our solar system or the environment of black holes. The observatory captures light from cosmic objects that has traveled to Earth for more than 13 billion years. NOEMA has "observed the most distant known galaxy, which formed shortly after the Big Bang," notes the report. It also "measured the temperature of the cosmic background radiation at a very early stage of the universe, a scientific first that should make it possible to trace the effects of dark energy driving the universe apart."