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Pep Guardiola expected to leave Manchester City at end of the season

  • Guardiola has been manager of club for 10 years

  • He is set to go with one year left on his contract

Pep Guardiola is expected to leave Manchester City after 10 trophy-filled years as manager.

The club did not confirm reports on Monday night that Guardiola’s last game as City manager will be at home to Aston Villa on Sunday, the final day of the Premier League season. But increasingly figures around the club expect an announcement before the end of the season. Guardiola’s camp has been approached for comment.

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Kai Havertz header edges nervy Arsenal past Burnley and one step from title

It was a night when fervour and hope ran into yet more Arsenal anxiety. This was supposed to be straightforward, wasn’t it? Burnley’s relegation from the Premier League was confirmed on 22 April. They sacked their manager, Scott Parker, shortly afterwards and came here under the caretaker, Michael Jackson. They had avoided defeat only three times in their previous ten league matches, drawing all three.

It was not straightforward. Arsenal laboured under the spectre of the mother and father of all calamities. It nagged away during a traumatic second-half. Everybody knew that with the margins so tight it might take only one flash from Burnley; a bolt from the sky blue. If Arsenal do stagger over the line and win their first title since 2004, they will have done it in nerve-shredding fashion.

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Braziliaanse bondscoach Ancelotti neemt Neymar na bijna drie jaar afwezigheid weer op in WK-selectie

Kabinet heeft geen geld meer over voor luchtalarm, zou over anderhalf jaar verdwijnen

Amanohashidate

Teruhide Tomori has added a photo to the pool:

Amanohashidate

Location : Amanohashidate View Land
Monju, Miyazu, Kyoto.

天橋立 / 天橋立ビューランド 京都府宮津市字文珠

this isn't happiness.

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Fantasy friends, Sarah Theresa Lee







Fantasy friends, Sarah Theresa Lee

The Register

Biting the hand that feeds IT — Enterprise Technology News and Analysis

Uncle Sam's next big supercomputer might use something more exotic than GPUs

Of the world’s most powerful supercomputers, nine of the top 10 are powered by GPUs, but that might not be the case for much longer. As chipmakers like Nvidia prioritize AI FLOPS over the ultra-precise floating point calculations used in scientific computing, US National Labs are turning to new chip architectures to get their FP64 fix. Among the candidates is NextSilicon’s Maverick-2, a dataflow processor designed explicitly with the 64-bit floating point mathematics that dominate the Department of Energy’s most important simulations. Despite its name, the Department of Energy is concerned with far more than the US’ power grid. It operates some of the largest publicly known supercomputers in the world, which are responsible for everything from simulating the physics of nuclear weapons at the moment of criticality and bioweapons defense to public health and safety. Since the Titan Supercomputer made its debut in 2012, a growing number of these supercomputers have been powered by GPUs from Nvidia, and more recently AMD. But that’s not the case for Sandia National Laboratory’s new Spectra supercomputer, which was built in collaboration with Penguin Solutions and NextSilicon. Compared to exascale systems like Frontier or El Capitan, Spectra is tiny. The machine counts 64 nodes and 128 of NextSilicon’s “runtime-configurable” accelerators. But scale isn’t the point. Spectra is a test bed for NextSilicon’s Maverick-2. This week, Sandia gave the chips the thumbs up, announcing that the big iron had met all of its system acceptance requirements, opening the door for the chips to be deployed in larger systems in the future. Not another GPU Despite some similarities to Nvidia’s B200, Maverick-2 is a very different beast. Instead of the standard von Neumann compute architecture that underpins most CPUs and GPUs today, NextSilicon’s chips employ a reconfigurable dataflow architecture. The processor’s two compute dies comprise a grid of arithmetic logic units interconnected in a graph. Each unit is configured at runtime to perform a specific operation, whether it be addition, multiplication, or some other logic operation. But the chip’s real trick is overlapping data flow and compute. As soon as data reaches the next unit in the pipeline, it’s computed immediately, no waiting for load-store operations to shuffle data around. According to NextSilicon, this dramatically improves the performance and efficiency of the chips in real-world workloads. Dataflow architectures aren’t new. Groq, Cerebras, and SambaNova have all built chips based on the concept. However, all of these designs are aimed at AI inference or training. NextSilicon’s is one of the few we’ve seen aimed at HPC. Dataflow is notoriously difficult to program for, which is likely why the chip startups that have built chips around it have largely offered them as a managed or white glove service rather than selling bare metal servers. Rather than trying to port workloads to run on its chips, NextSilicon has built a compiler that it claims allows it to run any existing C, Python, Fortran, or CUDA codebases on its chips. As we understand it, it works by initially running these workloads on the CPU. The compiler then captures the compute graph, maps it to the chips, and then optimizes it to maximize performance. With Spectra, Sandia has now validated the parts across three key workloads: the high-performance conjugate gradient (HPCG) benchmark, the LAMMPS molecular dynamics test suite, and the Sparta Monte Carlo simulation suite. AI is changing GPUs NextSilicon’s focus on HPC comes in stark contrast to the next generation of GPUs from Nvidia. The company’s Rubin GPUs due out later this year promise gobs of memory bandwidth and up to 50 petaFLOPS of FP4 compute. This makes the chips strong contenders for AI inference and training workloads, which is probably why the DoE is also deploying them in systems like the Doudna supercomputer at Lawrence Berkeley National Laboratory. While FP64 compute remains relevant for many existing scientific workloads, for AI workloads, Nvidia's GPUs are still relevant to US Labs. However, all those AI FLOPS come at the expense of hardware FP64 vector and matrix performance. Rubin tops out at 33 teraFLOPS, making it slower than even Nvidia’s nearly four-year-old H100. But that’s not to say it’s not good for scientific computing. For matrix heavy workloads like High Performance Linpack (HPL), Nvidia is leaning on a somewhat controversial spin on the Ozaki scheme, which uses lower precision data types to emulate FP64 compute. Using this approach, Nvidia claims Rubin can deliver up to 200 teraFLOPS of FP64 matrix performance. We dug deeper into Nvidia’s emulated FP64 algorithms earlier this year, but suffice to say it’s not perfect. While it has shown promise in certain HPC workloads, in others, particularly vector-heavy ones, like computational fluid dynamics, it offers little if any benefit. Coincidentally, the latter happens to be the same kind of workload that NextSilicon has focused its attention on. We don’t yet have system-level benchmarks for NextSilicon’s hardware, much less Spectra, but we’re told a single Maverick-2 can deliver about 600 gigaFLOPS of FP64 compute HPCG. The startup claims this performance is roughly on par with leading GPUs while consuming half the power. While Nvidia is clearly prioritizing AI compute in its latest generation of GPUs, AMD has taken a different approach. Like Rubin, AMD’s new MI455X accelerators are tuned for AI inference and training, but it’s only one of several versions of the GPU the House of Zen has baked in TSMC’s oven. For the MI430X, AMD swapped out the AI-centric compute dies for some built specifically for HPC. Earlier this month, we learned the chip would deliver up to 200 teraFLOPS of peak FP64 grunt to the DoE’s upcoming Discovery and Europe's Alice Recoque supercomputers. Who needs GPUs anyway? Chip startups like NextSilicon still need to prove their chips can scale to larger systems. But, across the Pacific, China has already shown that, at least for scientific computing, it doesn’t need GPUs to compete with the West’s best supers. China has a history of building boutique silicon specifically to advance its national supercomputing capability. Some systems, like the Sunway TaihuLight supercomputer, used a custom manycore processor like 260 custom RISC processors. Others, like the Tianhe 2A, used a homegrown digital signal processor (DSP) called the Matrix 2000 for its FP64 compute. More recently, we caught wind of a new supercomputer, called the LineShine, that, similar to the TaihuLight machine, reportedly uses 47,000 custom CPUs, which are expected to push the machine to 2 exaFLOPS of FP64 grunt. Of course, because China doesn’t participate in the annual Top500 ranking of the fastest publicly known supers anymore, we may never know for sure. China’s use of boutique silicon is due in part to US trade restrictions on the sale of high-end accelerators in the region. Even where still legal, these chips have become a supply chain vulnerability for Beijing. In fact, the US government’s decision to bar Intel from selling its Xeon Phi processors to China drove the development of the Matrix 2000. In the US, the bigger challenge may be competing with chip designers' shareholders. AI has made Nvidia the most valuable company in the world; HPC by comparison remains an important, albeit niche market. ®

Slashdot

News for nerds, stuff that matters

A Master's Degree Isn't the Job Guarantee It Used To Be

An anonymous reader quotes a report from the Wall Street Journal: Going back to grad school has long been the Plan B of young professionals who aspire to climb higher in their careers or struggle to get promoted in a tough job market. New data show that getting a master's degree isn't the guarantee it used to be. The unemployment rate for workers under 35 with a master's degree has rarely been higher in the past 20 years, according to the Burning Glass Institute, a labor-market think tank focused on the future of work, which analyzed data collected by the U.S. Bureau of Labor Statistics going back to 2003.

At the same time, the unemployment rate for workers under 35 with a Ph.D., law degree or medical degree has rarely been lower. "For most of the past two decades, these lines moved together -- not anymore," said Gad Levanon, chief economist of Burning Glass. Levanon has a theory about why the payoffs for advanced degrees have uncoupled: "More degrees chasing fewer of the positions those degrees were meant to unlock." [...] While degrees from law school and medical school amount to a license to practice, master's degrees are more of a signal, Levanon said. And a signal loses value when so many people have one, he added: "It's hardly a sure bet to securing a good job."

Now master's-degree holders under 35 are at the 77th percentile of unemployment, where the 50th percentile is normal, according to the Burning Glass analysis. Even associate-degree holders have had a higher employment level for the past year. Unemployment among master's-degree holders has been worse only about a quarter of the time in the past 20-plus years. There was a stint during the Covid-19 pandemic when this cohort was out of work at higher rates, and a more prolonged stretch as the U.S. climbed out of the recession in 2008 and 2009. "Every indication is hiring managers now are more receptive than ever to the idea that a person doesn't need a graduate degree to be competitive," said Johnny C. Taylor Jr., president of SHRM, the chief lobbying group for human-resource professionals.

"We are seeing that, hands down, especially in the last two or three years with AI," he said of job readiness. Employers just want to know, "Can you do it?"

Read more of this story at Slashdot.

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“I have travelled all over the world in the last 30...

“I have travelled all over the world in the last 30 years, and have never seen anything like the density of assholes I just encountered in Japan, [i.e.] tourists being an unbearable menace specifically while on and around their phones.”

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Justitie Minnesota vervolgt ICE-agent om onder meer mishandeling

MINNEAPOLIS (ANP/AFP/RTR) - Aanklagers in de Amerikaanse staat Minnesota hebben een 53-jarige agent van de immigratie- en douanedienst ICE aangeklaagd wegens onder meer mishandeling met behulp van een gevaarlijk vuurwapen. De aanklacht heeft betrekking op schoten die half januari zijn gelost op een Venezolaanse migrant die in het been werd getroffen.

Dat gebeurde tijdens een door president Donald Trump bevolen grootschalige en omstreden actie van de federale dienst ICE tegen illegale immigratie in Minnesota. Daarbij werden ook twee Amerikanen gedood.