Lifeboat Foundation

Government news resource covering technology, performance, employment, telework, cybersecurity, and more for federal employees.

China has 206 of the top 500 supercomputers — compared to the U.S.’s 124.

America is now home to the world’s speediest supercomputer. But the new list of the 500 swiftest machines underlines how much faster China is building them.

The list, published Monday, shows the Chinese companies and government pulling away as the most prolific producer of supercomputers, with 206 of the top 500. American corporations and the United States government designed and made 124 of the supercomputers on the list.

Continue reading “China Extends Lead as Most Prolific Supercomputer Maker” »

I get this question a lot. Today, I was asked to write an answer at Quora.com, a Q&A web site at which I am the local cryptocurrency expert. It’s time to address this issue here at Lifeboat.

Question

I have many PCs laying around my home and office.
Some are current models with fast Intel CPUs. Can
I mine Bitcoin to make a little money on the side?

Answer

Other answers focus on the cost of electricity, the number of hashes or teraflops achieved by a computer CPU or the size of the current Bitcoin reward. But, you needn’t dig into any of these details to understand this answer.

You can find the mining software to mine Bitcoin or any other coin on any equipment. Even a phone or wristwatch. But, don’t expect to make money. Mining Bitcoin with an x86 CPU (Core or Pentium equivalent) is never cost effective—not even when Bitcoin was trading at nearly $20,000. A computer with a fast $1500 graphics card will bring you closer to profitability, but not by much.

Continue reading “Extra PCs laying around? Why not mine Bitcoin?” »

Neither the machine or nor the professional human debaters were given prior knowledge of the subject of the on-stage debate, which took place in San Francisco on Monday.

The planned Aurora 21 “exascale” supercomputer may be the first that’s powerful enough to map the human brain.

IBM and the Department of Energy’s Oak Ridge National Laboratory have revealed the world’s “most powerful and smartest scientific supercomputer.” Known as Summit, IBM says that its new computer will be capable of processing 200,000 quadrillion calculations per second. To put that into perspective, if every person on Earth did a single calculation per second, it would take 305 days to do what Summit does in a single second. Assuming those numbers are accurate, that would make Summit the world’s fastest supercomputer. It would also mark the first time since 2012 that a U.S. computer held that title.

Summit has been in the works for several years now and features some truly impressive specs. According to Tech Crunch, the computer will feature 4,608 compute servers, 22 IBM Power9 chips and six Nvidia Tesla V100 GPUs each. In addition, the machine will feature more than 10 petabytes of memory. As the Nvidia GPUs attest, this machine will be primarily used for the development of artificial intelligence and machine learning. In addition to the work on A.I., Summit will also be used for research into energy and other scientific endeavors at Oak Ridge.

IBM was the Department of Energy’s general contractor for the Summit project, but it also had the help of several other partners within the tech industry. The GPUs were provided by Nvidia, which remains one of the leaders in cutting-edge GPU development. Mellanox and Redhat were also brought on to work on the development of Summit.

Summit, a supercomputer built in the United States for the Oak Ridge National Laboratory, is taking the lead from China for the title of world’s speediest computer.

In general, modelers attack the problem by breaking it into billions of bits, either by dividing space into a 3D grid of subvolumes or by parceling the mass of dark and ordinary matter into swarms of particles. The simulation then tracks the interactions among those elements while ticking through cosmic time in, say, million-year steps. The computations strain even the most powerful supercomputers. BlueTides, for example, runs on Blue Waters—a supercomputer at the University of Illinois in Urbana that can perform 13 quadrillion calculations per second. Merely loading the model consumes 90% of the computer’s available memory, Feng says.

For years such simulations produced galaxies that were too gassy, massive, and blobby. But computer power has increased, and, more important, models of the radiation-matter feedback have improved. Now, hydrodynamic simulations have begun to produce the right number of galaxies of the right masses and shapes—spiral disks, squat ellipticals, spherical dwarfs, and oddball irregulars—says Volker Springel, a cosmologist at the Heidelberg Institute for Theoretical Studies in Germany who worked on Millennium and leads the Illustris simulation. “Until recently, the simulation field struggled to make spiral galaxies,” he says. “It’s only in the last 5 years that we’ve shown that you can make them.”

The models now show that, like people, galaxies tend to go through distinct life stages, Hopkins says. When young, a galaxy roils with activity, as one merger after another stretches and contorts it, inducing spurts of star formation. After a few billion years, the galaxy tends to settle into a relatively placid and stable middle age. Later, it can even slip into senescence as it loses its gas and the ability make stars—a transition our Milky Way appears to be making now, Hopkins says. But the wild and violent turns of adolescence make the particular path of any galaxy hard to predict, he says.

Continue reading “Galaxy simulations are at last matching reality—and producing surprising insights into cosmic evolution” »

A new proof by SFI Professor David Wolpert sends a humbling message to would-be super intelligences: you can’t know everything all the time.

The proof starts by mathematically formalizing the way an “inference device,” say, a scientist armed with a supercomputer, fabulous experimental equipment, etc., can have knowledge about the state of the universe around them. Whether that scientist’s knowledge is acquired by observing their universe, controlling it, predicting what will happen next, or inferring what happened in the past, there’s a mathematical structure that restricts that knowledge. The key is that the inference device, their knowledge, and the physical variable that they (may) know something about, are all subsystems of the same universe. That coupling restricts what the device can know. In particular, Wolpert proves that there is always something that the inference device cannot predict, and something that they cannot remember, and something that they cannot observe.

“In some ways this formalism can be viewed as many different extensions of [Donald MacKay’s] statement that ‘a prediction concerning the narrator’s future cannot account for the effect of the narrator’s learning that prediction,’” Wolpert explains. “Perhaps the simplest extension is that, when we formalize [inference devices] mathematically, we notice that the same impossibility results that hold for predictions of the future—MacKay’s concern—also hold for memories of the past. Time is an arbitrary variable—it plays no role in terms of differing states of the universe.”

Continue reading “New proof reveals fundamental limits of scientific knowledge” »