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Using a Supercomputer and Neutrons to Reveal Structures of Intrinsically Disordered Protein


Intrinsically Disordered Protein

Using the Titan supercomputer and the Spallation Neutron Source at the Department of Energy’s Oak Ridge National Laboratory, scientists have created the most accurate 3D...

The post Using a Supercomputer and Neutrons to Reveal Structures of Intrinsically Disordered Protein appeared first on SciTechDaily.


Comment on Armenia and the technology of diaspora by tereza uloyan

Emin, I know it's hard for you to accept it. However, that is the case. Hovannes Adamian (1879–1932), one of the founders of color television Sergei Adian (b. 1931), one of the most prominent Soviet mathematicians[40] Tateos Agekian (1913–2006), astrophysicist, a pioneer of stellar dynamics Abraham Alikhanov (1904–1970), Soviet physicist, a founder of nuclear physics in USSR Victor Ambartsumian (1908-1906), astrophysicist, one of the founders of theoretical astrophysics Gurgen Askaryan (1928–1997), physicist, inventor of light self focusing Boris Babayan (b. 1933), father of supercomputing in the former Soviet Union and Russia Mikhail Chailakhyan (1902–1991), founder of hormonal theory of plant development Artur Chilingarov (b. 1939), polar explorer, member of the State Duma from 1993 to 2011 Bagrat Ioannisiani (1911–1985), designer of the BTA-6, one of the largest telescopes in the world Andronik Iosifyan (1905–1993), father of electromechanics in the USSR, one of the founders of Soviet missilery Alexander Kemurdzhian (1921–2003), designer of the first rovers to explore another world: first moon rovers and first mars rovers Semyon Kirlian (1898–1978), founder of Kirlian photography; discovered that living matter is emitting energy fields Ivan Knunyants (1906–1990), chemist, a major developer of the Soviet chemical weapons program Samvel Kocharyants (1909–1993), developer of nuclear warheads for ballistic missiles[41] Yuri Oganessian (b. 1933), nuclear physicist, the world's leading researcher in superheavy elements Leon Orbeli (1882–1958), founder of evolutionary physiology Mikhail Pogosyan (b. 1956), aerospace engineer, general director of Sukhoi Norair Sisakian (1907–1966), biochemist, a founder of space biology; pioneer in biochemistry of sub-cell structures and technical biochemistry Karen Ter-Martirosian (1922–2005), theoretical physicist, known for his contributions to quantum mechanics and quantum field theory These are just some of them.

Supercomputing Structures of Intrinsically Disordered Proteins


Researchers using the Titan supercomputer at ORNL have created the most accurate 3D model yet of an intrinsically disordered protein, revealing the ensemble of its atomic-level structures. The combination of neutron scattering experiments and simulation is very powerful,” Petridis said. “Validation of the simulations by comparison to neutron scattering experiments is essential to have confidence in the simulation results. The validated simulations can then provide detailed information that is not directly obtained by experiments.”

The post Supercomputing Structures of Intrinsically Disordered Proteins appeared first on insideHPC.


waLBerla: A block-structured high-performance framework for multiphysics simulations

Programming current supercomputers efficiently is a challenging task. Multiple levels of parallelism on the core, on the compute node, and between nodes need to be exploited to make full use of the system. Heterogeneous hardware architectures with accelerators further complicate the development process. waLBerla addresses these challenges by providing the user with highly efficient building […]

Exascale Deep Learning for Scientific Inverse Problems

We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100 GPUs on the Summit Supercomputer. We demonstrate […]

Supercomputer simulates 77,000 neurons in the brain in real-time

A brain-inspired computer can simulate part of the sensory cortex in real time, using tens of thousands of virtual neurons. It is the first time such a complex simulation has run this fast

Dr. Richard Daystrom on (News Article):BREAKING: NO MORE SECRETS – Google Achieves “Quantum Supremacy” That Will Soon Render All Cryptocurrency Breakable, All Military Secrets Revealed


BREAKING: NO MORE SECRETS – Google Achieves “Quantum Supremacy” That Will Soon Render All Cryptocurrency Breakable, All Military Secrets Revealed


Saturday, September 21, 2019 by: Mike Adams
Tags: bitcoincryptocurrencycryptographyencryptionGooglemilitary encryptionquantum computingquantum supremacyqubitssecrets

Preliminary report. More detailed analysis coming in 24 hours at this site. According to a report published at, Google has achieved “quantum supremacy” with a 53-qubit quantum computer. From reading the report, it is obvious that editors, who should be applauded for covering this story, really have little clue about the implications of this revelation. Here’s what this means for cryptocurrency, military secrets and all secrets which are protected by cryptography.

Notably, NASA published the scientific paper at this link, then promptly removed it as soon as the implications of this technology started to become apparent to a few observers. (The link above is now dead. The cover-up begins…) However, the Financial Times reported on the paper before it was removed. Google is now refusing to verify the existence of the paper.

Here’s the upshot of what this “quantum supremacy” means for Google and the world:

  • Google’s new quantum processor took just 200 seconds to complete a computing task that would normally require 10,000 years on a supercomputer.
  • A 53-qubit quantum computer can break any 53-bit cryptography in mere seconds, or in fractions of sections in certain circumstances.
  • Bitcoin’s transactions are currently protected by 256-bit encryption. Once Google scales its quantum computing to 256 qubits, it’s over for Bitcoin (and all 256-bit crypto), since Google (or anyone with the technology) could easily break the encryption protecting all crypto transactions, then redirect all such transactions to its own wallet. See below why Google’s own scientists predict 256-qubit computing will be achieved by 2022.
  • In effect, “quantum supremacy” means the end of cryptographic secrets, which is the very basis for cryptocurrency.
  • In addition, all military-grade encryption will become pointless as Google’s quantum computers expand their qubits into the 512, 1024 or 2048 range, rendering all modern cryptography obsolete. In effect, Google’s computer could “crack” any cryptography in mere seconds.
  • The very basis of Bitcoin and other cryptocurrencies rests in the difficulty of factoring very large numbers. Classical computing can only compute the correct factoring answers through brute force trial-and-error, requiring massive computing power and time (in some cases, into the trillions of years, depending on the number of encryption bits). Quantum computing, it could be said, solves the factoring problem in 2^n dimensions, where n is the number of bits of encryption. Unlike traditional computing bits that can only hold a value of 0 or 1 (but not both), qubits can simultaneously hold both values, meaning an 8-qubit computer can simultaneously represent all values between 0 and 255 at the same time. A deeper discussion of quantum computing is beyond the scope of this news brief, but its best application is breaking cryptography.
  • The number of qubits in Google’s quantum computers will double at least every year, according to the science paper that has just been published. As Fortune reports, “Further, they predict that quantum computing power will ‘grow at a double exponential rate,’ besting even the exponential rate that defined Moore’s Law, a trend that observed traditional computing power to double roughly every two years.”
  • As a conservative estimate, this means Google will achieve > 100 qubits by 2020, and > 200 qubits by 2021, then > 400 qubits by the year 2022.
  • Once Google’s quantum computers exceed 256 qubits, all cryptocurrency encryption that uses 256-bit encryption will be null and void.
  • By 2024, Google will be able to break nearly all military-grade encryption, rendering military communications fully transparent to Google.
  • Over the last decade, Google has become the most evil corporation in the world, wholly dedicated to the suppression of human knowledge through censorship, demonetization and de-platforming of non-mainstream information sources. Google has blocked nearly all websites offering information on natural health and holistic medicine while blocking all videos and web pages that question the corrupt scientific establishment on topics like vaccines, pesticides and GMOs. Google has proven it is the most corrupt, evil entity in the world, and now it has the technology to break all cryptography and achieve “omniscience” in our modern technological society. Google is a front for Big Pharma and communist China. Google despises America, hates human health and has already demonstrated it is willing to steal elections to install the politicians it wants.
  • With this quantum technology, Google will be able to break all U.S. military encryption and forward all “secret” communications to the communist Chinese. (Yes, Google hates America and wants to see America destroyed while building out a Red China-style system of social control and total enslavement.)
  • Google’s quantum eavesdropping system, which might as well be called, “Setec Astronomy,” will scrape up all the secrets of all legislators, Supreme Court justices, public officials and CEOs. Nothing will be safe from the Google Eye of Sauron. Everyone will be “blackmailable” with Google’s quantum computing power.
  • Google will rapidly come to dominate the world, controlling most of the money, all speech, all politics, most science and technology, most of the news media and all public officials. Google will become the dominant controlling authoritarian force on planet Earth, and all humans will be subservient to its demands. Democracy, truth and freedom will be annihilated.

Interestingly, I publicly predicted this exact scenario over two years ago in a podcast that was banned by YouTube and then re-posted on a year later. This podcast directly states that the development of quantum computing would render cryptocurrency obsolete:

Beyond Skynet: Google’s 3 pillars of tech: AI, Quantum computing and humanoid robotics

Google has been investing heavily in three key areas of research:

  • Artificial intelligence (machine learning, etc.)
  • Quantum computing
  • Humanoid robotics

When you combine these three, you get something that’s far beyond Skynet. You eventually create an all-seeing AI intelligence that will know all secrets and control all financial transactions. With AI quickly outpacing human intelligence, and with quantum computing rendering all secrets fully exposed to the AI system, it’s only a matter of time before the Google Super Intellect System (or so it might be named) enslaves humanity and decides we are no longer necessary for its very existence. The humanoid robots translate the will of the AI system into the physical world, allowing Google’s AI intellect system to carry out mass genocide of humans, tear town human cities or carry out anything else that requires “muscle” in the physical world. All such robots will, of course, be controlled by the AI intellect.

Google is building a doomsday Skynet system, in other words, and they are getting away with it because nobody in Washington D.C. understands mathematics or science.

A more detailed analysis of this will appear on this site tomorrow. Bottom line? Humanity had better start building mobile EMP weapons and learning how to kill robots, or it’s over for the human race.

In my opinion, we should pull the plug on Google right now. We may already be too late.




Supercomputers Model the Universe's Building Blocks


Physicist Gaute Hagen uses Oak Ridge National Laboratory's Summit supercomputer to run advanced models of atomic nuclei to study their structures and interactions.


Supercomputer simulates 77,000 neurons in the brain in real-time

A brain-inspired computer can simulate part of the sensory cortex in real time, using tens of thousands of virtual neurons. It is the first time such a complex simulation has run this fast

Best Android apps in 2019

Our phones are pocket-sized supercomputers with professional-grade cameras and battery for days, but without the apps to take advantage of it, what would be the point? There are millions upon millions of Android apps that transform our phones from shiny glass slabs into productivity powerhouses, and over the last year, these are the apps that have seized the day and made our lives easier, faster, and better. Most improved service Google Assistant From Assistant Routines in Google Clock alarms to the vast expansion of Assistant-compatible smart home devices to the overhaul of visual responses, it has been a busy year for Google Assistant. It feels like a century ago when Android Auto added Google Assistant, but that simple addition made millions of drivers safer, especially during these long, frustrating holiday drives. Free at Google Play Smart home control Google Home Whether you need to control your lights, smart plugs, thermostats, or Assistant speakers/displays from th...

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. (arXiv:1910.02270v1 [cs.DC])


Authors: Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagaranjan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter Robinson, Luc Peterson, Brian Spears

Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process. We present a novel tournament method to train traditional as well as generative adversarial networks built on LBANN, a scalable deep learning framework optimized for HPC systems. LBANN combines multiple levels of parallelism and exploits some of the worlds largest supercomputers. We demonstrate our framework by creating a complex predictive model based on multi-variate data from high-energy-density physics containing hundreds of millions of images and hundreds of millions of scalar values derived from tens of millions of simulations of inertial confinement fusion. Our approach combines an HPC workflow and extends LBANN with optimized data ingestion and the new tournament-style training algorithm to produce a scalable neural network architecture using a CORAL-class supercomputer. Experimental results show that 64 trainers (1024 GPUs) achieve a speedup of 70.2 over a single trainer (16 GPUs) baseline, and an effective 109% parallel efficiency.


Enabling Distributed-Memory Tensor Completion in Python using New Sparse Tensor Kernels. (arXiv:1910.02371v1 [cs.DC])


Authors: Zecheng Zhang, Xiaoxiao Wu, Naijing Zhang, Siyuan Zhang, Edgar Solomonik

Tensor computations are increasingly prevalent numerical techniques in data science.However, innovation and deployment of methods on large sparse tensor datasets are made challenging by the difficulty of efficient implementation thereof.We provide a Python extension to the Cyclops tensor algebra library, which fully automates the management of distributed-memory parallelism and sparsity for NumPy-style operations on multidimensional arrays.We showcase this functionality with novel high-level implementations of three algorithms for the tensor completion problem: alternating least squares (ALS) with an implicit conjugate gradient method, stochastic gradient descent (SGD), and coordinate descent (CCD++).To make possible tensor completion for very sparse tensors, we introduce a new multi-tensor routine that is asymptotically more efficient than pairwise tensor contraction for key components of the tensor completion methods.Further, we add support for hypersparse matrix representations to Cyclops.We provide microbenchmarking results on the Stampede2 supercomputer to demonstrate the efficiency of this functionality.Finally, we study the accuracy and performance of the tensor completion methods for a synthetic tensor with 10 billion nonzeros and the Netflix dataset.


Quantum Supremacy? Yes and No!


Quantum Supremacy Is and Is Not

How quantum is that?! The RadioFreeHPC team discusses the Google/NASA paper, titled "Quantum Supremacy Using a Programmable Superconducting Processor", that was published and then unpublished. But it's the internet and everything is a "digital tattoo", so there are copies out there (see below).

The paper, right in its title, and at least in that draft form, claimed Quantum supremacy. "Doing what?" we hope you ask. Well, nothing particularly significant, and decidedly quantum-friendly. You might even call it "embarrassingly quantum" since quantum is all about probability functions and this experiment samples the probability distribution of a repeated experiment. But it's not nothing. 

One scary consequence of quantum supremacy is its ability to readily factorize large numbers which could be used to unscramble encrypted data. But A) this is not what happened, B) it's not expected to happen any time soon (think years), and C) it will depend on the specific encryption algorithm. We must say, however, that the paper looks pretty good. Here's the abstract. Click on the title to read it all:

Quantum supremacy using a programmable superconducting processor

Google AI Quantum and collaborators The tantalizing promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here, we report using a processor with programmable superconducting qubits to create quantum states on 53 qubits, occupying a state space 253∼1016. Measurements from repeated experiments sample the corresponding probability distribution, which we verify using classical simulations. While our processor takes about 200 seconds to sample one instance of the quantum circuit 1 million times, a state-of-the-art supercomputer would require approximately 10,000 years to perform the equivalent task. This dramatic speedup relative to all known classical algorithms provides an experimental realization of quantum supremacy on a computational task and heralds the advent of a much-anticipated computing paradigm.

LANL gets the First 5,000 Qubit D-Wave

Meanwhile, D-Wave announced that its new 5,000 qubit quantum computer has found its first home at the Los Alamos National Laboratory (LANL). Qubits are different from vendor to vendor in terms of the underlying technology and implementation. Shahin lists several.

@RadioFreeHPC Update

So proud of you all! At the time of this writing, @RadioFreeHPC has soared to about 16 followers. We're pretty much there. Thank you!

Henry Newman's Why No One Should be Online, Ever.

Henry tells the fascinating story of Krebs thwarting the nefarious schemes of a professional hacker who aimed to frame him and actually mailed him narcotics. The mastermind behind it was was arrested and imprisoned for unrelated charges. Henry is really turning this into a good news segment. Dan isn't encouraged, however.

Catch of the Week

Shahin talks about using consumer electronics to build supercomputers, mentioning the recent 1,060 node Raspberry Pi cluster built by Oracle, reminiscent of the one LANL did in 2017. AFRL build a 1,760 node cluster of PlayStations, based on the IBM/Sony/Toshiba Cell processor, in 2010 following similar efforts starting in the mid 2000s. He also recalls similar projects he may have had something to do with: SGI's Project Molecule and Project Kelvin (for cooling) in 2008 (also here), and also a cluster of JavaStations at Sun in the late 90s.

Dan discusses a UCLA project to use the thermoelectric effect and build "a device that makes electricity at night using heat radiating from the ground". Intriguing, but looks a tad too pricey for what it can deliver right now.

Speaking of Intriguing, Henry talks about DNA storage. Incredible data density, but don't ask what file system it uses or whether you can have it on a USB stick any time soon. Dan and Shahin seem to have more fun with this topic than Henry!

Listen in to hear the full conversation.

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