Lifeboat Foundation

From a performance standpoint we know building a homebrew Raspberry Pi cluster doesn’t make a lot of sense, as even a fairly run of the mill desktop x86 machine is sure to run circles around it. That said, there’s an argument to be made that rigging up a dozen little Linux boards gives you a compact and affordable playground to experiment with things like parallel computing and load balancing. Is it a perfect argument? Not really. But if you’re anything like us, the whole thing starts making a lot more sense when you realize your cluster of Pi Zeros can be built to look like the iconic Cray-1 supercomputer.

Continue reading “Cluster Your Pi Zeros In Style With 3D Printed Cray-1” »

A team of researchers affiliated with multiple institutions in China and the U.S. has found that it is possible to track the sliding of grain boundaries in some metals at the atomic scale using an electron microscope and an automatic atom tracker. In their paper published in the journal Science, the group describes their study of platinum using their new technique and the discovery they made in doing so.

Scientists have been studying the properties of metals for many years. Learning more about how crystal grains in certain metals interact with one another has led to the development of new kinds of metals and applications for their use. In their recent effort, the researchers took a novel approach to studying the sliding that occurs between grains and in so doing have learned something new.

When crystalline metals are deformed, the grains that they are made of move against one another, and the way they move determines many of their properties, such as malleability. To learn more about what happens between grains in such metals during deformity, the researchers used two types of technologies: transmission electron microscopy and automated atom-tracking.

There’s been a lot of focus on how both Intel and AMD are planning for the future in packaging their dies to increase overall performance and mitigate higher manufacturing costs. For AMD, that next step has been V-cache, an additional L3 cache (SRAM) chiplet that’s designed to be 3D die stacked on top of an existing Zen 3 chiplet, tripling the total about of L3 cache available. Today, AMD’s V-cache technology is finally available to the wider market, as AMD is announcing that their EPYC 7003X “Milan-X” server CPUs have now reached general availability.

As first announced late last year, AMD is bringing its 3D V-Cache technology to the enterprise market through Milan-X, an advanced variant of its current-generation 3rd Gen Milan-based EPYC 7,003 processors. AMD is launching four new processors ranging from 16-cores to 64-cores, all of them with Zen 3 cores and 768 MB L3 cache via 3D stacked V-Cache.

AMD’s Milan-X processors are an upgraded version of its current 3rd generation Milan-based processors, EPYC 7003. Adding to its preexisting Milan-based EPYC 7,003 line-up, which we reviewed back in June last year, the most significant advancement from Milan-X is through its large 768 MB of L3 cache using AMD’s 3D V-Cache stacking technology. The AMD 3D V-Cache uses TSMC’s N7 process node – the same node Milan’s Zen 3 chiplets are built upon – and it measures at 36 mm², with a 64 MiB chip on top of the existing 32 MiB found on the Zen 3 chiplets.

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We’ve been hearing for years how nanotechnology is going to change the world. In movies and in headlines, nanotechnology is almost like “future magic” that will make the impossible possible. But how realistic are those predictions? And how close are we to seeing some of them come true? Let’s take a look at the state of nanotechnology.

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Continue reading “So Where Exactly Are We With Nanotechnology? | Answers With Joe” »

Research On Humans Adapting, Living & Working In Space — Colonel (ret) Dr. Samantha Weeks, Ph.D., Polaris Dawn, Science & Research Director

Colonel (ret) Dr. Samantha “Combo” Weeks, Ph.D. is the Science & Research Director, of the Polaris Dawn Program (https://polarisprogram.com/dawn/), a planned private human spaceflight mission, operated by SpaceX on behalf of Shift4 Payments CEO Jared Isaacman, planned to launch using the Crew Dragon capsule.

Continue reading “Dr. Samantha Weeks, Ph.D. — Polaris Dawn — Science & Research Director” »

For about 9 months, Elon has been suggesting that Booster 4 with Starship 20 on top of it would do the first orbital test of Starship.

The big question was how safe would it be to launch with 29 Raptor engines at once? A lot of people were talking about Russia’s N1 rocket which failed in all four attempts with its 31 engines, causing one of the world’s largest nonnuclear explosions and killing over a hundred people in the process. The most Raptor engines that have ever been static fire tested at once is 6. It would be very difficult to rebuild the Starship tower if it was destroyed. Easily ten times as hard as building another Starship and booster.

Note that using so many engines is not impossible. For example, the Falcon Heavy launches with 27 engines and all its launches have been successful so far. The problem is that the Raptor is the world’s first full-flow staged-combustion-cycle engine and SpaceX has not perfected it yet. For example, the only Starship which successfully landed from a medium-height test almost missed the landing pad and was on fire when it landed. (All other medium-height test Starships exploded, one before it even hit the ground.)

Continue reading “Here’s some interesting news” »

As scientists prepared in 2010 to collapse the first particles in the Large Hadron Collider (LHC), media representatives imagined that the EU-wide experiment could create a black hole that could swallow and destroy our planet. How on earth, columnists rage, could scientists justify such a dangerous indulgence for the pursuit of abstract, theoretical knowledge?

Wed, Mar 23 at 10 PM CDT.

GUEST SPEAKERS:

Kelly fast, near-earth object observations program manager, NASA

Continue reading “Planetary Defense at NASA” »

For self-driving cars and other applications developed using AI, you need what’s known as “deep learning”, the core concepts of which emerged in the ’50s. This requires training models based on similar patterns as seen in the human brain. This, in turn, requires a large amount of compute power, as afforded by TPUs (tensor processing units) or GPUs (graphics processing units) running for lengthy periods. However, cost of this compute power is out of reach of most AI developers, who largely rent it from cloud computing platforms such as AWS or Azure. What is to be done?

Well, one approach is that taken by U.K. startup Gensyn. It’s taken the idea of the distributed computing power of older projects such as SETI@home and the COVID-19 focussed Folding@home and applied it in the direction of this desire for deep learning amongst AI developers. The result is a way to get high-performance compute power from a distributed network of computers.

Gensyn has now raised a $6.5 million seed led by Eden Block, a web3 VC. Also participating in the round is Galaxy Digital, Maven 11, Coinfund, Hypersphere, Zee Prime and founders from some blockchain protocols. This adds to a previously unannounced pre-seed investment of $1.1 millionin 2021 — led by 7percent Ventures and Counterview Capital, with participation from Entrepreneur First and id4 Ventures.