Lifeboat Foundation

Scientists at the University of Lund in Sweden have found a way to use “biological motors” for parallel computing. The findings could mean vastly more powerful and energy efficient computers in a decade’s time.

Nanotechnologists at Lund University in Sweden have discovered a way to miniaturize the processing power that is found today only in the largest and most unwieldy of supercomputers. Their findings, which were published in the Proceedings of the National Academy of Sciences, point the way to a future when our laptops and other personal, handheld computing devices pack the computational heft of a Cray Titan or IBM Blue Gene/Q.

But the solution may be a little surprising.

Continue reading “Researchers Found a Way to Shrink a Supercomputer to the Size of a Laptop” »

Today, Lawrence Livermore National Lab (LLNL) and IBM announced the development of a new Scale-up Synaptic Supercomputer (NS16e) that highly integrates 16 TrueNorth Chips in a 4×4 array to deliver 16 million neurons and 256 million synapses. LLNL will also receive an end-to-end software ecosystem that consists of a simulator; a programming language; an integrated programming environment; a library of algorithms as well as applications; firmware; tools for composing neural networks for deep learning; a teaching curriculum; and cloud enablement.

The $1 million computer has 16 IBM microprocessors designed to mimic the way the brain works.

IBM says it will be five to seven years before TrueNorth sees widespread commercial use, but the Lawrence Livermore test is a big step in that direction.

Continue reading “Neuromorphic supercomputer has 16 million neurons” »

Deep neural networks (DNNs) can be taught nearly anything, including how to beat us at our own games. The problem is that training AI systems ties up big-ticket supercomputers or data centers for days at a time. Scientists from IBM’s T.J. Watson Research Center think they can cut the horsepower and learning times drastically using “resistive processing units,” theoretical chips that combine CPU and non-volatile memory. Those could accelerate data speeds exponentially, resulting in systems that can do tasks like “natural speech recognition and translation between all world languages,” according to the team.

So why does it take so much computing power and time to teach AI? The problem is that modern neural networks like Google’s DeepMind or IBM Watson must perform billions of tasks in in parallel. That requires numerous CPU memory calls, which quickly adds up over billions of cycles. The researchers debated using new storage tech like resistive RAM that can permanently store data with DRAM-like speeds. However, they eventually came up with the idea for a new type of chip called a resistive processing unit (RPU) that puts large amounts of resistive RAM directly onto a CPU.

Using the Piz Daint supercomputer, cosmologists at the University of Geneva are the first to simulate the structure of the universe in a way that consistently accounts for the general theory of relativity.

Quicker time to discovery. That’s what scientists focused on quantum chemistry are looking for. According to Bert de Jong, Computational Chemistry, Materials and Climate Group Lead, Computational Research Division, Lawrence Berkeley National Lab (LBNL), “I’m a computational chemist working extensively with experimentalists doing interdisciplinary research. To shorten time to scientific discovery, I need to be able to run simulations at near-real-time, or at least overnight, to drive or guide the next experiments.” Changes must be made in the HPC software used in quantum chemistry research to take advantage of advanced HPC systems to meet the research needs of scientists both today and in the future.

NWChem is a widely used open source software computational chemistry package that includes both quantum chemical and molecular dynamics functionality. The NWChem project started around the mid-1990s, and the code was designed from the beginning to take advantage of parallel computer systems. NWChem is actively developed by a consortium of developers and maintained by the Environmental Molecular Sciences Laboratory (EMSL) located at the Pacific Northwest National Laboratory (PNNL) in Washington State. NWChem aims to provide its users with computational chemistry tools that are scalable both in their ability to treat large scientific computational chemistry problems efficiently, and in their use of available parallel computing resources from high-performance parallel supercomputers to conventional workstation clusters.

“Rapid evolution of the computational hardware also requires significant effort geared toward the modernization of the code to meet current research needs,” states Karol Kowalski, Capability Lead for NWChem Development at PNNL.

Continue reading “Modified NWChem Code Utilizes Supercomputer Parallelization” »

Physicists say a supercomputer simulation of blood flow around the entire human body is showing promise, based on an experimental test.

A few years ago, researchers from Germany and Japan were able to simulate one percent of human brain activity for a single second. It took the processing power of one of the world’s most powerful supercomputers to make that happen.

Hands down, the human brain is by far the most powerful, energy efficient computer ever created.

So what if we could harness the power of the human brain by using actual brain cells to power the next generation of computers?

Continue reading “This Amazing Computer Chip Is Made of Live Brain Cells” »

Australia’s more investment into Quantum technology.

The coming scientific revolution will make today’s supercomputers seem sluggish.

Solving the turbulence plasma mystery.

Cutting-edge simulations run at Lawrence Berkeley National Laboratory’s National Energy Research Scientific Computing Center (NERSC) over a two-year period are helping physicists better understand what influences the behavior of the plasma turbulence that is driven by the intense heating necessary to create fusion energy. This research has yielded exciting answers to long-standing questions about plasma heat loss that have previously stymied efforts to predict the performance of fusion reactors and could help pave the way for this alternative energy source.

The key to making fusion work is to maintain a sufficiently high temperature and density to enable the atoms in the reactor to overcome their mutual repulsion and bind to form helium. But one side effect of this process is turbulence, which can increase the rate of plasma heat loss, significantly limiting the resulting energy output. So researchers have been working to pinpoint both what causes the turbulence and how to control or possibly eliminate it.

Continue reading “Multi-scale simulations solve a plasma turbulence mystery” »