While the $35 @Pi is by no means a computing powerhouse, in recent years enthusiasts have begun harnessing the power of armies of the tiny boards. There's a wide range of Pi clusters out there, from modest five-board arrangements all the way up to sprawling 750-Pi machines. If you're curious to find out more, then here's five Pi clusters built in recent years, starting with some you can try yourself and moving on to the Pi-based supercomputers being built by research labs.
PALO ALTO, Calif., July 09, 2018 (GLOBE NEWSWIRE) -- @Hewlett Packard Enterprise (NYSE:HPE) today announced that the Ecole Polytechnique Fédérale de Lausanne’s (EPFL) @BlueBrainProject, a Swiss brain research initiative, selected #HPE to build a next-generation supercomputer for modeling and simulation of the mammalian brain. The new #supercomputer, called ‘Blue Brain 5’, will be dedicated to simulation neuroscience, in particular simulation-based research, analysis and visualization, to advance the understanding of the brain. Brain disorders are complex phenomena spanning from genes to circuits to the whole brain. Establishing an integrated view of how all these levels work together and are perturbed in brain diseases is the primary challenge in understanding the brain. The Blue Brain Project is pioneering the approach of reconstructing and simulating digital models of brains to address this challenge. Blue Brain systematically releases open-access data, models and open-source tools to help the community, such as the European Human Brain Project, understand the brain at its different levels of organization. Based on the supercomputing requirements derived from the project’s scientific roadmap, the Blue Brain Project followed a rigorous procurement process and after evaluation of the bids received, according to the published criteria, HPE was awarded a first contract at the end of 2017. Including a subsequent phase, the total value to provide state-of-the-art supercomputing technology and expertise can go up to CHF 18 million. The advanced supercomputer built by HPE is based on the HPE SGI 8600 System, providing tailored and scalable compute performance to enable the Blue Brain Project to pursue its scientific roadmap goal for 2020 to model entire regions of the mouse brain, in particular the thalamus and neocortex.
@Baidu, @Alibaba and @Tencent jockey for position in the development of #quantumcomputing, which delivers a faster and more efficient approach to processing information than today’s fastest computers
Long considered the Holy Grail for nations, quantum computing is poised to move on the fast track in China as the mainland’s internet giants pour new investment into this field, boosting efforts by the country to become a hi-tech innovation powerhouse.
Baidu, Alibaba Group Holding and Tencent Holdings – the Chinese internet triumvirate known by the acronym BAT – are now competing head-to-head in quantum computing research, funding projects that could give them a toehold stake in the commercial development of the mother of all supercomputers.
Quantum computers, which take a new approach to processing information, are theoretically capable of making calculations that are orders of magnitude faster than what the world’s most powerful supercomputers can do.
New silicon chip for helping build #quantumcomputers and securing our information Bristol, UK | Posted on February 8th, 2018 Scientific effort worldwide is focused on attempting to use #siliconphotonics to realise #quantumtechnologies, such as super-secure communications, quantum #supercomputers and new ways build increased sensitivity sensors. Silicon photonic chips process information made of light using an area millions of times smaller than if you were to try make the equivalent device using individual lenses, mirrors and other optics. Now, researchers at the University of Bristol have made a breakthrough for silicon quantum photonics — they have developed new type of on-chip detector capable of measuring quantum mechanical behavior within the integrated chip architecture. This is a new tool for making sure silicon photonic processors work the way they are designed and can themselves be used for other tasks, such as generating random numbers for cryptography, vital for the security industry, and as an important part of new types of optical sensor. PhD student Giacomo Ferranti explained, “The great thing about the detector is that it works at room temperature. A lot of single photon detection requires cryogenics at ~4 Kelvin” (minus 270 degrees centigrade) “while those cold detectors have their own amazing benefits, they are currently expensive and require large cryogenic fridges. Our detector is both small enough to sit on a human hair and can work in normal room temperature conditions” One of the key applications that the detector has already been used for by the researchers is to generate random numbers. “The ability to generate truly random numbers with a machine, without any bias, is actually a very difficult task” explains Francesco Raffaelli, another PhD student responsible for the project. “Random numbers have all sorts of applications, but the one that interests me the most is its use for cryptography and quantum cryptography. One day soon, I imagine these devices will be routinely part of the micro-processor on your desktop PC and in your mobile phone to keep them secure".
http://www.nanotech-now.com/news.cgi?story_id=54974
@Dell will be the vendor behind the latest upgrade to the US National Oceanic and Atmospheric Administration ( @NOAA ), part of the US Department of Commerce. The upgrade will deliver additional computational power when two @Dell systems are added to systems from @IBM and Cray at datacenters in Reston, Virginia, and Orlando, Florida, later this month. Once the upgrade is finalised NOAA’s combined weather and climate #supercomputing system will be among the 30 fastest in the world, with the ability to process 8 quadrillion calculations per second. ‘NOAA’s supercomputers play a vital role in monitoring numerous weather events from blizzards to hurricanes,’ said Secretary of Commerce Wilbur Ross. ‘These latest updates will further enhance NOAA’s abilities to predict and warn American communities of destructive weather.’ This upgrade completes phase three of a multiyear effort to build more powerful supercomputers that make complex calculations faster to improve weather, water and climate forecast models. It adds 2.8 petaflops of speed at both data centres combined, increasing NOAA’s total operational computing speed to 8.4 petaflops — or 4.2 petaflops per site. Dell is the latest addition to NOAA's weather and climate operational supercomputing system. This powerful Dell hums alongside NOAA's IBM and Cray computers at a data centre in Orlando, Florida. The three systems combined in Florida and Virginia give NOAA 8.4 petaflops of total processing speed and pave the way for improved weather models and forecasts. (NOAA ) The upgrade also adds 60 per cent more storage capacity, allowing NOAA to collect and process more weather, water and climate observations used by all the models than ever before. ‘NOAA’s supercomputers ingest and analyse billions of data points taken from satellites, weather balloons, airplanes, buoys and ground observing stations around the world each day,’ said retired Navy Rear Admiral Dr Timothy Gallaudet, acting NOAA administrator. ‘Having more computing speed and capacity positions us to collect and process even more data from our newest satellites — GOES-East, NOAA-20 and GOES-S — to meet the growing information and decision-support needs of our emergency management partners, the weather industry and the public’ added Gallaudet. With this upgrade US weather supercomputing paves the way for NOAA’s National Weather Service to implement the next generation Global Forecast System, known as the ‘American Model’, next year. The new GFS will have significant upgrades in 2019, including increased resolution to allow NOAA to run the model at 9 kilometres and 128 levels out to 16 days, compared to the current run of 13 kilometres and 64 levels out to 10 days. The revamped GFS will run in research mode on the new supercomputers during this year’s hurricane season. ‘As we look toward launching the next generation GFS in 2019, we’re taking a “community modelling approach” and working with the best and brightest model developers in this country and abroad to ensure the new U.S. model is the most accurate and reliable in the world,’ said National Weather Service Director Dr Louis Uccellini.
https://www.scientific-computing.com/news/noaa-turns-dell-supercomputer-upgrade
 The (noisy) 50-100 qubit #quantumcomputer is coming soon. ( #NISQ = noisy intermediate-scale quantum computer). NISQ devices cannot be simulated by brute force using the most powerful currently existing #supercomputers. NISQ will be an interesting tool for exploring physics. It might also have useful applications. But we’re not sure about that. NISQ will not change the world by itself. Rather it is a step toward more powerful quantum technologies of the future. Potentially transformative scalable quantum computers may still be decades away. We’re not sure how long it will take. Qubit “quality” The number of qubits is an important metric, but it is not the only thing that matters. The quality of the qubits, and of the “quantum gates” that process the qubits, is also very important. All quantum gates today are noisy, but some are better than others. Qubit measurements are also noisy. For today’s best hardware (superconducting circuits or trapped ions), the probability of error per (two-qubit) gate is about 1 per 1000, and the probability of error per measurement is about 1 per 100 (or better for trapped ions). We don’t yet know whether systems with many qubits will perform that well. Naively, we cannot do many more than 1000 gates (and perhaps not even that many) without being overwhelmed by the noise. Actually, that may be too naïve, but anyway the noise limits the computational power of NISQ technology. Eventually we’ll do much better, either by improving (logical) gate accuracy using quantum error correction (at a hefty overhead cost) or building much more accurate physical gates, or both. But that probably won’t happen very soon. Other important features: The time needed to execute a gate (or a measurement). E.g., the two-qubit gate time is about 40 ns for superconducting qubits, 100 µs for trapped ions, a significant difference. Also qubit connectivity, fabrication yield, …  Quantum Speedups? When will quantum computers solve important problems that are beyond the reach of the post powerful classical supercomputers? We should compare with post-exascale classical hardware, e.g. 10 years from now, or more (over 10^18 FLOPS). We should compare with the best classical algorithms for the same tasks. Note that, for problems outside NP (e.g typical quantum simulation tasks), validating the performance of the quantum computer may be difficult. Even if classical supercomputers can compete, the quantum computer might have advantages, e.g. lower cost and/or lower power consumption. Quantum optimizers Eddie Farhi: “Try it and see if it works!” We don’t expect a quantum computer to solve worst case instances of NP-hard problems, but it might find better approximate solutions, or find them faster. Hybrid quantum/classical algorithms. Combine quantum evaluation of an expectation value with a classical feedback loop for seeking a quantum state with a lower value. Quantum approximate optimization algorithm (QAOA). In effect, seek low-energy states of a classical spin glass. Variational quantum eigensolvers (VQE). Seek low energy states of a quantum many-body system with a local Hamiltonian H. (Much easier than algorithms which require simulation of time evolution governed by H.) Classical optimization algorithms (for both classical and quantum problems) are sophisticated and well-honed after decades of hard work. Will NISQ be able to do better? How quantum testbeds might help Peter Shor: “You don’t need them [testbeds] to be big enough to solve useful problems, just big enough to tell whether you can solve useful problems.” Classical examples: Simplex method for linear programming: experiments showed it’s fast long before theorists could prove that it’s fast. Metropolis algorithm: experiments showed it’s useful for solving statistical physics problems before theory established criteria for rapid convergence. Deep learning. Mostly tinkering so far, without much theory input. Possible quantum examples: Quantum annealers, approximate optimizers, variational eigensolvers, … playing around may give us new ideas. But in the NISQ era, imperfect gates will place severe limits on circuit size. In the long run, quantum error correction will be needed for scalability. In the near term, better gates might help a lot! What can we do with, say, less than 100 qubits, depth less than 100? We need a dialog between quantum algorithm experts and application users. Quantum annealing The D-Wave machine is a (very noisy) 2000-qubit quantum annealer (QA), which solves optimization problems. It might be useful. But we have no convincing theoretical argument that QAs are useful, nor have QA speedups been demonstrated experimentally. QA is a noisy version of adiabatic quantum computing (AQC), and we believe AQC is powerful. Any problem that can be solved efficiently by noiseless quantum computers can also be solved efficiently by noiseless AQC, using a “circuit-to-Hamiltonian map.” But in contrast to the quantum circuit model, we don’t know whether noisy AQC is scalable. Furthermore, the circuit-to-Hamiltonian map has high overhead: Many more qubits are needed by the (noiseless) AQC algorithm than by the corresponding quantum circuit algorithm which solves the same problem. Theorists are more hopeful that a QA can achieve speedups if the Hamiltonian has a “sign problem” (is “non-stoquastic”). Present day QAs are stoquastic, but non-stoquastic versions are coming soon. Assessing the performance of QA may already be beyond the reach of classical simulation, and theoretical analysis has not achieved much progress. Further experimentation should clarify whether QAs actually achieve speedups relative to the best classical algorithms. QAs can also be used for solving quantum simulation problems rather than classical optimization problems (D-Wave, unpublished). Quantum speedups in the NISQ era and beyond Can noisy intermediate-scale quantum computing (NISQ) surpass exascale classical hardware running the best classical algorithms? Near-term quantum advantage for useful applications is possible, but not guaranteed. Hybrid quantum/classical algorithms (like QAOA and VQE) can be tested. Near-term algorithms should be designed with noise resilience in mind. Quantum dynamics of highly entangled systems is especially hard to simulate, and is therefore an especially promising arena for quantum advantage. Experimentation with quantum testbeds may hasten progress and inspire new algorithms. NISQ will not change the world by itself. Realistically, the goal for near-term quantum platforms should be to pave the way for bigger payoffs using future devices. Lower quantum gate error rates will lower the overhead cost of quantum error correction, and also extend the reach of quantum algorithms which do not use error correction. Truly transformative quantum computing technology may need to be fault tolerant, and so may still be far off. But we don’t know for sure how long it will take. Progress toward fault tolerant QC must continue to be a high priority for quantum technologists. Quantum hardware: state of the art IBM Quantum Experience in the cloud: now 16 qubits (superconducting circuit). 20 qubits by end of 2017, 50-qubit device “built and measured.” Google 22-qubit device (superconducting circuit), 49 qubits next year. Harvard 51-qubit quantum simulator (Rydberg atoms in optical tweezers). Dynamical phase transition in Ising-like systems; puzzles in defect (domain wall) density. UMd 53-qubit quantum simulator (trapped ions). Dynamical phase transition in Ising-like systems; high-efficiency single-shot readout of many-body correlators. ionQ: 32-qubit processor planned (trapped ions), with all-to-all connectivity. Microsoft: is 2018 the year of the Majorana qubit? And many other interesting platforms … spin qubits, defects in diamond (and other materials), photonic systems, … There are other important metrics besides the number of qubits; in particular, the two-qubit gate error rate (currently over 1 per 1000) determines how large a quantum circuit can be executed with reasonable signal-to-noise
RESEARCH TRIANGLE PARK, N.C., Dec. 14, 2017 — @Lenovo (SEHK:0992) (Pink Sheets: #LNVGY ) Data Center Group and @Intel will deliver a next-generation #supercomputer to @Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences in Munich, Germany. One of the foremost European computing centers for professionals in the scientific, research and academic communities, LRZ is tasked with managing not only exponential amounts of big data, but processing and analyzing that data quickly to accelerate research initiatives around the world. For example, the LRZ recently completed the world’s largest simulation of earthquakes and resulting tsunami’s, such as the Sumatra-Andaman Earthquake. This research enables real-time scenarios planning that can help predict aftershocks and other seismic hazards. Upon its completion in late 2018, the new supercomputer (called SuperMUC-NG) will support LRZ in its groundbreaking research across a variety of complex scientific disciplines, such as astrophysics, fluid dynamics and life sciences, by offering highly available, secure and energy-efficient high-performance computing (HPC) services that leverage industry-leading technology optimized to address the a broad range of scientific computing applications. The LRZ installation will also feature the 20-millionth server shipped by Lenovo, a significant milestone in the company’s data center history. “Lenovo is committed to providing research institutions like LRZ with not only sheer computing power, but a true, end-to-end solution that can help effectively and efficiently solve critical humanitarian challenges. We’re pleased to be working on this next-generation project in partnership with Intel,” said Scott Tease, Executive Director, HPC and AI, Lenovo Data Center Group. “The new SuperMUC-NG installation will provide LRZ with greater compute power in a smaller data center footprint with drastically reduced energy usage through innovative water-cooling technology, offering researchers a comprehensive supercomputing solution that packs more performance than ever to accelerate critical research projects.” The SuperMUC-NG will deliver a staggering 26.7 petaflop compute capacity powered by nearly 6,500 nodes of Lenovo’s recently-announced, next-generation ThinkSystem SD650 servers, featuring Intel Xeon Platinum processors with Intel Advanced Vector Extensions (Intel AVX 512), and interconnected with Intel Omni-Path Architecture. The new system will also include the integration of Lenovo Intelligent Computing Orchestrator (LiCO), a powerful management suite with an intuitive GUI that helps accelerate development of HPC and AI applications, as well as cloud-based components to empower LRZ researchers with the freedom to virtualize, process the vast amount of data sets and expediently share results with colleagues. To address the often-astronomical operational expenses generated by high-performance computing (HPC) infrastructure, the new SuperMUC-NG supercomputer will benefit from Intel technical optimizations and also feature cutting-edge water cooling technology from Lenovo. In combination with Lenovo Energy Aware Run-Time (EAR) software, a technology that dynamically controls system infrastructure power while applications are still running, Lenovo’s comprehensive water-cooling technology delivers 45 percent greater electricity savings to LRZ as compared to a similar, standard air-cooled system. Together, these energy efficiency innovations will help further reduce the research center’s carbon footprint and total cost of ownership. “Global research leaders like LRZ are driving insights that address not only some of the most complex problems we face, but that also make meaningful improvements in all of our lives,” said Trish Damkroger, Vice President of Technical Computing at Intel. “Intel offers the technical foundation that, when combined with the solution expertise of Lenovo, delivers the efficient performance and ease of programing to help LRZ’s researchers drive more discoveries with deeper analytics than have ever been possible before.” Once operational, the LRZ SuperMUC-NG system is expected to place on the industry-wide TOP500 list.
In publicly and privately funded computational science research, dollars (or Euros in this case) follow FLOPS. And when you’re one of the leading computing centers in Europe with a reputation around the world of highly reliable, leading edge technology resources, you look for the best in supercomputing in order to continue supporting breakthrough research. Thus, Barcelona Supercomputing Center (BSC) is driven to build leading supercomputing clusters for its research clients in the public and private sectors.  MareNostrum 4 is nestled within the Torre Girona chapel “We have the privilege of users coming back to us each year to run their projects,” said Sergio Girona, BSC’s Operations Department Director. “They return because we reliably provide the technology and services they need year after year, and because our systems are of the highest level.” Supported by the Spanish and Catalan governments and funded by the Ministry of Economy and Competitiveness with €34 million in 2015, BSC sought to take its MareNostrum 3 system to the next-generation of computing capabilities. It specified multiple clusters for both general computational needs of ongoing research, and for development of next-generation codes based on emerging supercomputing technologies and tools for the Exascale computing era. It fell to IBM, who partnered with Fujitsu and Lenovo, to design and build MareNostrum 4. MareNostrum 4 is a multi-cluster system which main cluster and data storage are interconnected by the Intel® Omni-Path Architecture (Intel® OPA) fabric. A general-purpose compute cluster, with 3,456 nodes of Intel® Xeon® Scalable Processor Product Family will provide up to 11.1 petaFLOPS of computational capacity. A smaller cluster delivering up to 0.5 petaFLOPS is built on the Intel® Xeon Phi™ Processor 7250. A third small cluster up to 1.5 petaFLOPS will include Power9* and Nvidia GPUs, and a fourth one, made of ARM v8 processors, will provide other 0.5 petaFLOPS of performance. And an IBM storage array will round out the system. All systems are interconnected with the storage subsystem. MareNostrum 4 is designed to be twelve times faster than its predecessor. Spain’s 13.7 petaFLOPS supercomputer contributes to the Partnership for Advanced Computing in Europe (PRACE) and supports the Spanish Supercomputing Network (RES). “From my point of view,” stated Girona, “Intel had, at the time of the procurement, the best processor for general purpose systems. Intel is very good on specific domains, and they continue to innovate in other domains. That is why we chose Intel processors for the general-purpose cluster and Intel Xeon Phi Processor for one of the emerging technology clusters, on which we can explore new code development.” The system was in production by July 2017 and placed at number 13 in the June 2017 Top500 list and number 16 on the November 2017 list. “The Barcelona Supercomputing Center team is committed to maximizing MareNostrum in any way we can,” concluded Girona. “But MareNostrum is not about us. Our purpose at BSC is to help others. We are successful when the scientists and engineers using MareNostrum’s computing power get all the data they need to further their discoveries. It is always rewarding to know we help others to further cutting-edge scientific exploration.”
Volta-Powered GPU Delivers 110 Teraflops of Deep Learning Horsepower — 9x Its Predecessor — to Researchers and Scientists Conference on Neural Information Processing Systems -- @NVIDIA today introduced #TITANV, the world’s most powerful #GPU for the PC, driven by the world’s most advanced GPU architecture, NVIDIA #Volta. Announced by NVIDIA founder and CEO @JensenHuang at the annual NIPS conference, TITAN V excels at computational processing for scientific simulation. Its 21.1 billion transistors deliver 110 teraflops of raw horsepower, 9x that of its predecessor, and extreme energy efficiency. “Our vision for Volta was to push the outer limits of high performance computing and AI. We broke new ground with its new processor architecture, instructions, numerical formats, memory architecture and processor links,” said Huang. “With TITAN V, we are putting Volta into the hands of researchers and scientists all over the world. I can’t wait to see their breakthrough discoveries.” NVIDIA Supercomputing GPU Architecture, Now for the PC TITAN V’s Volta architecture features a major redesign of the streaming multiprocessor that is at the center of the GPU. It doubles the energy efficiency of the previous generation Pascal™ design, enabling dramatic boosts in performance in the same power envelope. New Tensor Cores designed specifically for deep learning deliver up to 9x higher peak teraflops. With independent parallel integer and floating-point data paths, Volta is also much more efficient on workloads with a mix of computation and addressing calculations. Its new combined L1 data cache and shared memory unit significantly improve performance while also simplifying programming. Fabricated on a new TSMC 12-nanometer FFN high-performance manufacturing process customized for NVIDIA, TITAN V also incorporates Volta’s highly tuned 12GB HBM2 memory subsystem for advanced memory bandwidth utilization. Free AI Software on NVIDIA GPU Cloud TITAN V’s incredible power is ideal for developers who want to use their PCs to do work in AI, deep learning and high performance computing. Users of TITAN V can gain immediate access to the latest GPU-optimized AI, deep learning and HPC software by signing up at no charge for an NVIDIA GPU Cloud account. This container registry includes NVIDIA-optimized deep learning frameworks, third-party managed HPC applications, NVIDIA HPC visualization tools and the NVIDIA TensorRT™ inferencing optimizer. Immediate Availability TITAN V is available to purchase today for $2,999 from the NVIDIA store in participating countries
https://nvidianews.nvidia.com/news/nvidia-titan-v-transforms-the-pc-into-ai-supercomputer
#Supercomputers are typically huge and expensive, and they take an awful lot of power to run. Looking for a cheaper way allow developers to build and test high performance computer system software, the Los Alamos National Laboratory turned to the ubiquitous #RaspberryPi and Australia's BitScope. The Los Alamos National Laboratory is already home to one of the top 10 supercomputers in the world, the Trinity, but the lab needed a relatively inexpensive and scalable way to accommodate developers and researchers who couldn't get access to large systems for research and development in system software. "The Raspberry Pi modules let developers figure out how to write this software and get it to work reliably without having a dedicated testbed of the same size, which would cost a quarter billion dollars and use 25 megawatts of electricity," said LANL's Gary Glider.  The BitScope solution was designed and built in under 3 months and features 750 nodes made up of five BitScope rack mount modules. Each Pi rocks a 64-bit, 1.2 GHz quad-core ARM processor, and the 150 mini computers in each of the modules combine to offer up to 3,000 available cores as a parallel testbed for developers and researchers to design and test system software before launching it on larger systems like Trinity. The setup estimated to cost less than US$150 per node and runs at under 5 W per node. As well as system software development, the cluster could also find use in sensor network simulations, high performance networking research and the internet of things. The BitScope Cluster Modules are expected to go on sale early next year.
#Quantumcomputing is poised to change our digital world. Our most sophisticated #supercomputers will dwarf in power compared to computers wielding the power of #quantummechanics. Processes that take an immeasurable amount of time to complete, say thousands of years, could be done almost instantaneously on a quantum computer. How do we harness the power of atoms in computation — and what is it that makes them so powerful? With quantum computers, bits are no longer restrained to binary states. In classical computers, we can have bits that are either 0 or 1. These numbers are arranged into a string of bits, creating a byte. Bytes form larger structures, such as a megabyte or kilobyte and provide instructions to your computer. An example of a string of bits may be 00100110, or 01101101, and so on and so forth. In #quantumcomputers, a bit can be 0 or 1, or a combination of these states. These malleable bits are called quantum bits or “ #qubits ,” and are extremely powerful and highly volatile. Michelle Simmons, research head at the Centre for Quantum Computation and Communication Technology at the University of New South Wales, illustrated the potential of quantum computers in a tangible and visceral way in a recent talk. “Every time I add a quantum bit to a quantum computer, I double the computational power,” she explains. “If I could have 300 qubits, that would be more powerful than all the computers in the world connected together.” Researchers are racing to add more qubits to powerful processors. At the moment, however, a 300-qubit chip resides in a future far ahead. IBM has crafted a 16-qubit processor, Google, trailing close behind, a 6-qubit one. Both IBM and Google are utilizing superconductors to get the job done. They submerge their superconductor systems in sub zero temperatures to allow these machines to work without producing too much resistance. While new technologies are emerging to improve sophisticated superconductors, their use in computing is nothing new. In fact, the tech startup D-Wave secured massive funding early this decade, and has been engineering expensive quantum annealing systems since 2010. Groundbreaking research has lead to another way to quantum computing, however. The use of time crystals could prove to outperform traditional superconductor systems. Time crystals were proposed by Nobel laureate Frank Wilczek in 2012. His idea, in essence, was that since we can see spacial symmetries spontaneously break, as is the case with spatial crystals, we should be able to see this in time as well. That is, if we can have an arrangement of atoms in a certain sequence to create a form of matter, we could have an arrangement in time as well. In the context of quantum mechanics, time crystals could act as a sort of perpetual motion machine. Elizabeth Gibney explains in her article The Quest to Crystallize Time in Nature as as a “collection of quantum particles that constantly changes, and never reaches a steady state.” The idea of synthesizing time crystals in a lab seemed theoretically impossible when Wilczek proposed the idea. However, two separate research teams — one from University of Maryland, the other, Harvard University — have successfully created and observed the new type of matter in their own labs, utilizing vastly different approaches. “In a delicate balance between strong interactions, weak disorder, and periodic driving force,” begins Observations of a Time Crystal on University of Maryland research team’s site, “a collection of trapped ion qubits has been made to pulsate with a period that is relatively insensitive to the drive.” Meaning, a certain sect of time crystals, discrete time crystals, can exist. “This is a time crystal,” the article continues, “where the stable pulses emerge and break time symmetry — just like freezing liquid breaks spatial symmetry and forms a spatial crystal.” University of Maryland’s team has tested their quantum computer, built on their trapped-ion system, against IBM’s superconducting system, simulating 5-qubit machines. According to the research lead, using a simulator, “the performance is seen to mirror the connectivity of the systems, with the ion trap system out-performing the superconducting system on all results.” Breaking into the quantum age will require innovation, to be sure. The ability to create discrete time crystals may be the answer to creating more powerful quantum computing machines. At present, low-qubit chips aren’t equipped to accomplish as much as pioneers in the emerging technology would like. The discovery of discrete time crystals could be the infrastructure this new type of computing is built upon, and may just be what drives quantum computing forward.
http://gisuser.com/2017/11/quantum-computing-superconductors-vs-time-crystals/
The most powerful computer ever sent into space proved its mettle this month by registering a processing speed in excess of a trillion floating-point calculations per second, a measure that’s known as a teraflop. “ #HPE ’s #SpaceborneComputer is the first high-performance commercial off-the-shelf computer system to run one #teraflop at the #InternationalSpaceStation,” Mark Fernandez, Americas #HPCtechnology officer at #HewlettPackard Enterprise and co-principal investigator for the project, said today in a blog post.  The Spaceborne Computer project, pioneered by NASA and HPE, will put off-the-shelf equipment through its paces over the next year to study how well it stands up to the space environment. Most of the computers used in space have to incorporate hardware-based modifications to ensure reliability amid the radiation storms that periodically sweep through outer space. In contrast, the Spaceborne Computer relies on a software approach that incorporates real-time throttling of system operations, based on current conditions.
Unbelievably Fast The two fastest #supercomputers in the world right now are in China, and they possess 93 and 33 petaflops of processing power. A single petaflop equates to 1,000,000,000,000,000 calculations per second. That’s a lot of zeros, but even China’s 93 petaflop machine will seem pretty slow in comparison to the world’s first exascale computer — once someone finally builds it. An exascale computer would possess 1,000 petaflops of computing power. That would make it about a trillion times faster than your gigascale consumer laptop, which is capable of 1 billion calculations per second. Building such a powerful computer would be far too much for a single company to take on, but various nations think they may be up to the task. Right now, the fastest computer in the U.S. is Titan, which runs at about 18 petaflops per second. The government plans to unveil a new supercomputer by next year at the Oak Ridge National Laboratory in Tennessee, and at peak performance, that machine should be capable of around 200 petaflops per second. However, both will seem downright slow if the nation’s plans for a exascale computer come to fruition. This summer, the U.S. Department of Energy ( #DOE ) gave $258 million to six different companies — #AdvancedMicroDevices, #Cray, #HewlettPackard, #IBM, #Intel, and #Nvidia — as part of their #Exascale Computing Project. The hope is that these individual companies will be able to develop the various components necessary for an exascale system that would be operational by 2021. The U.S. isn’t the only nation aggressively pursuing this next level in processing, however. According to Meng Xiangfei, a director at China’s National Supercomputer Center in Tianjin, their Tianhe-3 supercomputer will be able to hit the exascale milestone sometime in 2018. Meanwhile, Japan believes its first exascale machine, Post-K, will be ready by early 2022. Predicting a Brighter Future So, what’s the point of having a supercomputer that fast? Computing power is directly correlated with realistic modeling, and the more powerful a computer, the better its predictive power. Exascale systems should have sufficient computing power to model even complex biological systems with enough precision to make massive logistical endeavors feasible. The DOE’s exascale initiative includes a project to develop predictive wind energy models that will be ready to work on an exascale system by 2022. Right now, wind energy meets only about 5 percent of the nation’s energy needs, mostly because wind farms aren’t consistently more cost effective when factoring out subsidies. Exascale supercomputers can change this by more accurately modeling how wind flows through plants and pouring that data into better industrial designs and cost reduction strategies for sustainable energy systems.
https://futurism.com/nations-race-to-build-a-computer-with-the-power-to-predict-the-future/
The Quantum Supremacy Threshold #Qubits allow the development of new computational algorithms, which are much more productive than silicon-based iterations. The more qubits a #quantumcomputer uses, the more processing power it has. But most advanced #quantumcomputationalsystems available today are still far behind #supercomputers in terms of their practical applications–although the situation is changing very fast indeed. There’s a theoretical threshold after which quantum computers would surpass most powerful classical supercomputers. Scientists believe it should happen somewhere around 50 qubits. Currently, the most advanced quantum chips are below 20 qubits, such as the #IBMQ that uses 17 qubits. #Google also is no stranger to the quantum race, as it’s working on a 49-qubit 14-meter machine using #superconductingcircuits. 51 “ #ColdAtoms ” to Make the World’s Most Advanced #QuantumComputer Google’s 49 qubit computer was supposed to be the highlight of the ICQT 2017 (The International Conference on #QuantumTechnologies, held July 12th–16th in Moscow). Designed by John Martinis, a professor at University of California at Santa Barbara, Google’s computer will use a chip embedded with 49 qubits (0.6 cm by 0.6 cm). But as groundbreaking Google’s machine might be, it was another machine that stole the show. During the same day of the ICQT 2017 that Martinis was supposed to give a lecture about his quantum device, Mikhail Lukin, the co-founder of RQC, made his own announcement. Mikhail’s team, including Russian and American scientists, have built the world’s most powerful functional quantum computing system, running on 51 qubits. The new quantum system uses an array of 51 “cold atoms” in lieu of qubits. Locked up on “laser cells”, these atoms should be kept at extremely low temperatures. “… we observe a novel type of robust many-body dynamics corresponding to persistent oscillations of crystalline order after a sudden quantum quench,” said researchers in a paper available at arXiv.org. “These observations enable new approaches for exploring many-body phenomena and open the door for realizations of novel quantum algorithms.” The model was successfully tested in the labs of Harvard University, solving physics problems that silicon chip-based supercomputers would have a hard time replicating.
https://edgylabs.com/russia-51-qubit-quantum-computer-icqt/
Today #DellEMC announced it will build a supercomputer to power #SwinburneUniversity of Technology’s groundbreaking research into #astrophysics and #gravitationalwaves. Called #OzStar, the new supercomputer will be used by the #ARC Centre of Excellence for Gravitational Wave Discovery ( #OzGRav ) to understand the extreme physics of #blackholes and warped space-time.
n of stars, intergalactic gases and more,” said Professor Bailes. “It’s exciting to think that we as OzGRav could make the next landmark discovery in gravitational wave astrophysics – and the Dell EMC supercomputer will allow us to capture, visualize and process the data to make those discoveries.”
The supercomputer will process incredibly large volumes of data coming from giant telescopes, searching for insights that could unlock answers about the Universe, reaffirming Swinburne’s position as a global leader in physics research.
The OzStar supercomputer will feature the new Dell EMC 14th Generation PowerEdge Servers, Dell EMC H-Series Networking Fabric and Dell EMC HPC Storage with Intel Lustre filesystem, delivering more than 1 Petaflop of performance capability – or 31 million years of calculations in a single second.
Building on Einstein’s theory, 100 years on
Albert Einstein first predicted gravitational waves in 1915 in his theory of General Relativity. Based on theoretical physics alone, Einstein described how gravity warps and distorts space-time. It took another 100 years and huge advances in technology before gravitational waves were physically detected in 2015.
In this landmark discovery, scientists detected the ripples in space time caused by gravitational waves; these waves resulted from the collision of two black holes over a billion light years away. This confirmed Einstein’s theory in a very direct way – something even he doubted would ever be physically possible. It also demonstrated that time and space can be stretched and compressed by the passing of gravitational waves, which inspired excited discussion about other predictions of General Relativity, such as the nature of space and time in the vicinity of (and within) black holes.
While Einstein predicted the existence of gravitational waves, it took one hundred years for technology to advance to the point they could be detected,” said Professor Matthew Bailes, director of OzGRav, Swinburne University of Technology. “Discoveries this significant don’t occur every day and we have now opened a new window on the Universe. This machine will be a tremendous boost to our brand-new field of science and will be used by astrophysicists at our partner nodes as well as internationally.”
The AU$31.1 million ARC Centre of Excellence for Gravitational Wave Discovery was announced in September 2016 with the mission to capitalize on the historic first detections of gravitational waves. Led by Swinburne, it will use state-of-the-art technology to research and understand the extreme physics of black holes and warped space-time.
Powering a new field of gravitational wave astronomy
Swinburne selected Dell EMC technology for the AU$4 million supercomputer at the heart of this research. The system will be used to sift through the reams of data from Advanced LIGO, the observatory that first detected gravitational waves, to search for new examples of merger, explosions and even evidence of 1 cm “mountains” on nearby neutron stars that cause deviations in space-time.
The supercomputer will also serve our other astronomers both at Swinburne and nationally who calculate theoretical models and crunch data,” said Professor Jared Hurley, Centre for Astrophysics and Supercomputing, Swinburne University of Technology.
Up to 35% of the supercomputer’s time will be spent on OzGrav research related to gravitational waves. The supercomputer will also continue to incorporate the GPU Supercomputer for Theoretical Astrophysics Research (gSTAR), operating as a national facility for the astronomy community funded under the federal National Collaborative Research Infrastructure Scheme (NCRIS) in cooperation with Astronomy Australia Limited (AAL). In addition, the supercomputer will underpin the research goals of Swinburne staff and students across multiple disciplines, including molecular dynamics, nanophotonics, advanced chemistry and atomic optics.
Supercomputing power with scale and flexibility
Dell EMC has designed the supercomputer using its 14th Generation PowerEdge R740 server technology that scales from traditional enterprise deployment through to high performance computing and machine learning. The platform is used as a building block to both the management and computing nodes, while the server is based on the next-generation Intel Xeon scalable processors.
OzStar replaces the “green” machines that have served Swinburne for the last decade and seeks to further reduce Swinburne’s carbon footprint by minimising CO2 emissions by carefully considering heating, cooling and a very high performance per watt ration of power consumption. The modular and scalable design means the system can be simply managed to meet workload demands.
This combination of Dell EMC technologies will deliver the incredibly high computing power required to move and analyse data sets that are literally astronomical in size,” said Andrew Underwood, Dell EMC’s ANZ high performance computing lead, who collaborated with Swinburne on the supercomputer design. “Our 14th Generation PowerEdge server and H-Series networking will form the bedrock of this modern supercomputer, enabling Swinburne University of Technology to push the limits of compute and data-intensive research workloads on a single platform and drive faster research breakthroughs.”
The OzStar supercomputer will be built using three core building blocks:
Dell EMC PowerEdge R740 compute and data-crunching nodesContaining Intel Xeon V5 processors and NVIDIA Tesla P100sFeaturing the unique Dell EMC HPC BIOS setting to increase performance of the compute nodeDell EMC H-Series Networking FabricDesigned to meet Swinburne’s performance, throughput and scalability requirementsPowered by Intel’s Omni-Path Architecture (OPA) to provide 86.4 Terebits per second of aggregate network bandwidth at .9 µs (microsecond) latencyDell EMC HPC Storage with Intel Lustre filesystemBuilt on the tried and tested PowerEdge and PowerVault Modular Disk reference architecture for optimised capacity, throughput and reliability
Along with these ingredients, Dell EMC will deliver ProSupport engineers to provide 24/7 expert level support on each of the building blocks in the OzStar supercomputer.
Together, these building blocks will deliver Swinburne University of Technology with:
4,410 x86 cores at 2.3Ghz across 107 standard compute and eight data crunching nodes230 x NVIDIA Tesla P100 12GB GPUs (one per CPU socket)272 Intel Xeon Phi cores at 1.6Ghz across 4 C6320p KNL nodesA high-speed, low latency network fabric able to move data across each building block at over 100Gbps with various features to ensure reliability and traffic flow (9 Dell EMC H1048 switches), as well as GPU-Direct capability5PiB of usable storage via the Lustre file system at 60GB/s throughput, on the Dell EMC PowerVault MD3060e storage with R740 controllers (10 Dell EMC PowerEdge R740 + 16 MD3060e and 1 MD3240)
Driving a new era of research
After confirming the project in mid-August, the Dell EMC supercomputer is expected to take four weeks to install and will be operational before the end of September.
Brilliant researchers are often only limited by advances in technology,” said Chris Kelly, vice president and general manager, Compute and Networking, Dell EMC Australia and New Zealand. “With this new supercomputer, Swinburne and OzGrav will be able to embark on a new era of astronomy that could unlock answers to questions mankind has pondered for centuries. It’s an incredibly exciting time for astronomical research and we at Dell EMC are proud to power the discoveries of tomorrow.”
https://insidehpc.com/2017/08/dell-emc-supercomputer-power-ozgrav-studies-black-holes/
Technology company #IBM is developing a new supercomputer powered by 64 #TrueNorth chips for use by the #USAirForceResearchLab, as part of a collaboration announced in June. The new ‘brain-like’ #supercomputer will be used for analytics involving pattern and #objectrecognition, as well as ‘ #sensoryprocessing ’. The TrueNorth #neurosynaptic system has been designed to imitate human neurons and can convert audio, video and other forms of data received by sensors into symbols, which the computer can then process. It is capable of performing advanced computations using less energy than conventional chips. Air Force Research Laboratory principal electronics engineer Qing Wu said: "This is about building more intelligent machines that will work with humans to make human operators and analysts more effective and efficient when dealing with data. "The major advantage of this chip is it runs machine-learning algorithms, the same ones as we run, the same functionality, same accuracy, but with much less power dissipation." The chip features a million neurons and was developed under the US Defense Advanced Research Projects Agency’s (DARPA) systems of neuromorphic adaptive plastic scalable electronics (SyNAPSE) programme.
#HewlettPackardEnterprise is teaming up with #NASA to launch a #supercomputer into space on Monday, with the ultimate aim of building computing resources that could serve on board a mission to Mars. The supercomputer, called the #SpaceborneComputer, will launch from the Kennedy Space Center in Florida on board the #SpaceX #CRS12 rocket, developed by @ElonMusk 's SpaceX. The rocket will send the SpaceX Dragon Spacecraft -- and the supercomputer along with it -- to the International Space Station ( #ISS ) National Lab. The goal of the joint experiment is to have the Spaceborne Computer operate smoothly in space for on year, which is roughly how long it would take to travel to Mars.
http://www.zdnet.com/article/hpe-nasa-to-launch-a-supercomputer-into-space/
Building on the successes of the Stampede1 supercomputer, the Texas Advanced Computing Center ( #TACC ) has rolled out its next-generation #HPC system, #Stampede2. Over the course of 2017, Stampede2 will undergo further optimization phases with the support of a $30 million grant from the National Science Foundation ( #NSF ). With the latest Xeon and Skylake processors, and enhanced networking provided by the Omni-Path architecture, the new flagship system is expected to deliver approximately 18 petaflops, nearly doubling Stampede1’s performance. Stampede2 continues Stampede1’s mission: enabling thousands of scientists and researchers across the United States to deliver breakthrough scientific discoveries in science, engineering, artificial intelligence, the humanities, and more. Dr. Dan Stanzione, Executive Director, at TACC, describes the breadth of their work. “TACC provides large-scale support for our users who make significant engineering and research discoveries. As research goals advance, the problems supercomputers help solve are getting bigger and more complicated every year. For our team at TACC, delivering greater speed and capacity with Stampede2 are top concerns.” A faster system means TACC can help scientists gain insights from their data more quickly. It also means Stampede2 can offer support for many more projects. In 2016, TACC received five times more requests to use Stampede than its capacity could handle. Unfortunately, that means many important scientific projects did not have access to the resources they needed. “Stampede2 will be a big step forward, since we can accommodate more projects and complete them more quickly,” Stanzione continued. TACC’s Stampede2 is deployed with support from vendor partners #Dell, #Intel, and #Seagate Technology, and operated by a team of cyberinfrastructure experts at TACC, UT Austin, Clemson University, Cornell University, the University of Colorado at Boulder, Indiana University, and Ohio State University. “An HPC system the scope of Stampede represents an incredible team effort,” expresses Stanzione. “We are thankful for the many partner organizations and experts across the country who supported us.” Asked about the challenges of rolling out such a major HPC system upgrade, Stanzione describes key considerations. “Much of the work in science and engineering is computation-focused, especially in fields such as weather, astrophysics, and aerodynamics. However, we are seeing exciting growth in demand from fields like biology and the humanities, and these new communities bring different needs. Today we are providing familiar web and application interfaces to supercomputing systems that naturally fit into established workflows. And we are building systems from the ground up with big-data in mind. Stampede has grown from being a big engine to being an end-to-end solution.” Several other benefits give Stampede2 a huge advantage over earlier systems. New applications require greater computational throughput. Stampede2 supports more than 40 major science and engineering applications at any given time, alongside thousands of applications used by small groups or individual researchers. Stampede2 is a heterogeneous system: one third of its processors are Intel Xeon processors, and the rest are Intel Xeon Phi processors. This combination of processors allows a broader range of applications to take advantage of the improved system configuration, and speeds up parallel workloads. For intensive computing applications such as visualization and ray tracing, Intel processors can also replace GPUs when tapping OSPRey interactive rendering capabilities. Given the scope of the design, Stampede2 is being deployed in three phases. Phase 1, based on the Intel Scalable System Framework, is already in place. Each node consists of Dell PowerEdge C6320P servers with Intel Xeon Phi processors 7250 (code name Knights Landing). As with Stampede1.5, Intel Omni-Path Architecture provides 100Gbps bandwidth in a low-latency network fabric. Despite having deployed only part of the system, phase one of the Stampede2 system ranks at #12 on the June 2017 Top500.org rankings of the fastest supercomputers worldwide. Comprising 4,200 nodes – each with 68 cores, 96GB of DDR RAM, and 16GB of high-speed MCDRAM – Stampede2 achieved a peak performance of nearly 13 petaFLOPS. Phase two will add 1,736 of the newest Intel Xeon processors (formerly code named Skylake). As part of the Intel Xeon processor scalable family (formerly code named Purley), these new processors with 28 cores offer significantly faster performance for compute and data-intensive workloads. Faster performance is due also to significant increases in memory and I/O bandwidth. Additional efforts will take the system’s capability even further. Incorporation of 3D XPoint non-volatile memory will provide an important opportunity to evaluate the impact of this new approach to memory on a large scale, and is expected to nudge performance even higher. When complete, Stampede2 is expected to deliver about 18 petaFLOPS of computing capacity for open science, nearly doubling the maximum performance of Stampede1. Researchers have used the system since May, conducting diverse research on Stampede2 including automated tumor identification from MRI data (The University of Texas at Austin), simulations supporting the LIGO gravitational wave discovery (Cambridge University), and real-time weather forecasting to direct storm chaser trucks (University of Oklahoma). When asked about the planning and rollout of Stampede2, Stanzione replies, “Stampede2 has been TACC’s smoothest deployment so far. Our team has already accomplished many system upgrades, and we are working hard on further optimizations. One element that makes the deployment process easier is our early collaboration with Intel. We always want to be among Intel’s earliest adopters so we can realize the benefits of their new technologies right away. In turn, we can also offer feedback to Intel when we encounter unexpected challenges.” Intel, working with TACC and other early adopters, uses this feedback to ensure products have been tested thoroughly in production systems before broad release. While Stanzione and his team strive for the greatest possible performance, they must also ground their ambitions with the reality of a fixed budget. “We are very fortunate to have the financial support of the NSF, and we want to use their grant very wisely,” reflects Stanzione. “By experimenting with the latest Intel technologies, we can better understand the price-performance impact of each, consider bottlenecks, and determine where Stampede will benefit most from upgrades. Working early with Intel helps us identify and adopt technologies that maximize our system’s capability while remaining cost effective. The latest Intel Xeon processors will give us quite a boost in performance, and we are excited to implement phase two very soon.” Once phase two is complete, Stanzione expects a much greater number of applicants seeking time on the supercomputer. “Our team here at TACC is very excited to get our work done so we can better serve engineers and researchers who require the system’s full potential,” he reflects. “We are the people behind the scenes; our reward comes when scientists achieve breakthrough discoveries.” While the team at TACC cannot anticipate the multitude and diversity of scientific projects which will ultimately run on Stampede2, one thing is certain: HPC systems like Stampede2 serve as foundations for innovation, taking science forward into a very optimistic future.
https://www.nextplatform.com/2017/07/25/texas-advanced-supercomputing-center-taps-latest-hpc-tech/
Six leading US technology companies will receive $258 million in funding from the Department of Energy’s #Exascale Computing Project (ECP) as part of its new #PathForward program. This money is to accelerate the research necessary to deploy the nation’s first exascale #supercomputers in about 2021.
The $258 million in funding will be allocated over a three-year contract period, with companies providing additional funding amounting to at least 40 percent of their total project cost, bringing the total investment to at least $430 million.
The award recipients:
· Advanced Micro Devices ( #AMD )
· Cray Inc. ( #CRAY )
· Hewlett Packard Enterprise ( #HPE )
· International Business Machines ( #IBM )
· Intel Corp. ( #Intel )
· NVIDIA Corp. ( #NVIDIA )
The Department’s funding for this program is supporting R and D in three areas – hardware technology, software technology, and application development – with the intention of delivering at least one exascale-capable system by 2021.
Hewlett Packard Enterprise recently revealed a prototype of its memory centric system called “The Machine”. It has 160 terabytes of memory.
Nvidia is continuing to have success scaling the performance and energy efficiency of their GPGPUs.
China currently has the two fastest supercomputers. Earlier in 2017, China said they were on track to the world’s first exascale machine,the Tianhe-3, by the year 2020.
US supercomputer got bumped down the list of top supercomputers when a Swiss computer took the third place spot.
A Tianhe-3 prototype is to be ready by 2018.
https://www.nextbigfuture.com/2017/07/update-on-the-race-to-the-exaflop-supercomputer.html
