Georgia Tech is taking on the challenge of moving computing past the end of Moore’s Law by standing up a new interdisciplinary research center, which is known as CRNCH. “We knew that at some point physics would come into play. We hit that wall around 2005,” said Tom Conte, inaugural director of CRNCH and professor in Georgia Tech’s schools of Computer Science and Electrical and Computer Engineering.
In this video from the HPC Advisory Council Spain Conference, Addison Snell from Intersect360 Research looks back over the past 10 years of HPC and provides predictions for the next 10 years. Intersect360 Research just released their Worldwide HPC 2015 Total Market Model and 2016–2020 Forecast.
From Megaflops to Gigaflops to Teraflops to Petaflops and soon to be Exaflops, the march in HPC is always on and moving ahead. This whitepaper details some of the technical challenges that will need to be addressed in the coming years in order to get to exascale computing.
Today D-Wave Systems announced details of its most advanced quantum computing system, featuring a new 2000-qubit processor. The announcement is being made at the company’s inaugural users group conference in Santa Fe, New Mexico. The new processor doubles the number of qubits over the previous generation D-Wave 2X system, enabling larger problems to be solved and extending D-Wave’s significant lead over all quantum computing competitors. The new system also introduces control features that allow users to tune the quantum computational process to solve problems faster and find more diverse solutions when they exist. In early tests these new features have yielded performance improvements of up to 1000 times over the D-Wave 2X system.
The big data analytics market has seen rapid growth in recent years. Part of this trend includes the increased use of machine learning (Deep Learning) technologies. Indeed, machine learning speed has been drastically increased though the use of GPU accelerators. The issues facing the HPC market are similar to the analytics market — efficient use of the underlying hardware. A position paper from the third annual Big Data and Extreme Computing conference (2015) illustrates the power of co-design in the analytics market.
Achieving better scalability and performance at Exascale will require full data reach. Without this capability, onload architectures force all data to move to the CPU before allowing any analysis. The ability to analyze data everywhere means that every active component in the cluster will contribute to the computing capabilities and boost performance. In effect, the interconnect will become its own “CPU” and provide in-network computing capabilities.
Scientists at the Energy Department’s National Renewable Energy Laboratory (NREL) discovered a use for perovskites that could propel the development of quantum computing. “Considerable research at NREL and elsewhere has been conducted into the use of organic-inorganic hybrid perovskites as a solar cell. Perovskite systems have been shown to be highly efficient at converting sunlight to electricity. Experimenting on a lead-halide perovskite, NREL researchers found evidence the material could have great potential for optoelectronic applications beyond photovoltaics, including in the field of quantum computers.”
The move to network offloading is the first step in co-designed systems. A large amount of overhead is required to service the huge number of packets required for modern data rates. This amount of overhead can significantly reduce network performance. Offloading network processing to the network interface card helped solve this bottleneck as well as some others.
In this podcast, the Radio Free HPC team looks at why it’s so difficult for new processor architectures to gain traction in HPC and the datacenter. Plus, we introduce a new regular feature for our show: The Catch of the Week.
IDC has announced the featured speakers for the next international HPC User Forum. The event will take place Sept. 22 in Beijing, China.