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Reynolds C.,AVX
Electronic Products (Garden City, New York) | Year: 2010

High CV, low direct current leakage (DCL), and low equivalent series resistance (ESR) are balanced in pulse applications while supporting duty cycles, voltage, and repetition rate. Wireless transmission, pulsed laser, and radar are characterized by a load that has a high instantaneous energy demand with only a finite amount of voltage drop in the circuit. In dynamic loads, two sources of drop include instantaneous voltage drop due to the internal resistance of the device, or ESR and the capacitive drop, as the charge of the holdup device decays over time. To achieve the necessary voltage holdup for the duration of the pulse, the instantaneous voltage drop should be minimized for long-term holdup and the capacitance should be maximized. AVX's ST Series (TurboCap) combines the ultralow ESR design of multilayer ceramic with a low ESL geometry to give the best overall combination of filtering and pulse holdup with 50-V capacitance ratings to 220 μF. Source


Billig N.R.,AVX
Electronic Products | Year: 2014

Experts suggest that emerged markets such as the 'downhole' oil and gas exploration sector have forced passive electronics suppliers to raise the temperature capabilities of stacked switch mode power supply (SMPS) multilayer ceramic capacitors (MLCC) and aluminum electrolytic capacitors. SMPS MLCCs tend to exhibit rugged, high temperature performance that is better suited to applications in the demanding oil exploration market than aluminum electrolytic capacitors. MLCC dielectrics capable of temperatures up to 250%C are also being developed. Stacked ceramic capacitors offer stable, reliable extended temperature characterizations across a range spanning from 55% to 200%C. Stacked ceramic capacitors also perform much better at high frequency. Source


News Article
Site: http://www.scientificcomputing.com/rss-feeds/all/rss.xml/all

When the National Science Foundation (NSF) announced the first-ever direct observation of gravitational waves, they confirmed a prediction Albert Einstein made when he published his General Theory of Relativity 100 years ago. Einstein never expected gravitational waves to be observed. These ripples in the fabric of spacetime are triggered by events occurring so far away and so long ago that, by the time they reached earth, he figured their signals would be too weak to measure. But Einstein didn’t foresee the development of the Laser Interferometer Gravity-Wave Observatory (LIGO) detectors — instruments so sensitive they can measure signals that change a fraction of the width of an atom’s nucleus over a distance of more than two miles. He didn’t anticipate Moore’s Law and the enormous computational resources that would enable scientists to extract and analyze these signals. With LIGO’s success, scientists have yet again confirmed Einstein’s genius. They’ve also opened a window on a whole new field of study — gravitational astrophysics — that will provide exciting new ways to light up the dark aspects of the universe. The LIGO team’s discovery and its ongoing work will deepen our understanding of phenomena such as black holes, neutron stars and, potentially, even the big bang. And it will lead to new insights we can’t yet imagine. “This discovery is the first time we’ve been able to hear the cosmos communicating to us using gravitational waves,” Dr. David Reitze, Executive Director of LIGO Laboratory told the U.S. House Committee on Science, Space and Technology.1 “I am quite confident that it won’t be the last time. Even more exciting, we will see, or rather hear, something completely unexpected from the universe that will revolutionize our understanding of the universe. We cannot wait to get there.” The LIGO experiment is awe-inspiring at both ends of the size spectrum. The gravitational waves captured by the LIGO detectors resulted from two black holes orbiting each other, colliding and merging approximately 1.3 billion years ago and 1.3 billion light-years away. The black holes, which were about 29 and 36 times the mass of our sun, collided at nearly half the speed of light. Their collision converted mass to the energy equivalent of three suns, producing the gravitational wave that LIGO detected. In the fraction of a second before the merger, they radiated with an energy 50 times greater than all the other stars in the visible universe. The LIGO equipment that observed the waves are the most sensitive measuring devices ever made, according to David Shoemaker, MIT LIGO Laboratory Director and Leader of the Advanced LIGO upgrade. Located at LIGO observatories in Louisiana and Washington State, each detector has an L-shaped interferometer that splits laser light, beams it in a vacuum down the 2.5 mile sides of the L, bounces it off mirrors at the ends of the L, measures how long each beam takes to travel and return, recombines it, and analyzes the “interference pattern.” Distances are measured over the 2.5 miles at a precision of 1/1019 of a meter, or one ten-thousandth the diameter of a proton. “The LIGO experiment gets to very fundamental physics,” says Stuart B. Anderson, Senior Research Scientist at Caltech and head of computing for LIGO. “When this gravitational wave passed through our solar system, it effectively changed the distance between the earth and the sun by the width of one atom. To be able to make this exquisitely precise measurement about something that’s so fundamental to our universe, in such a high-risk, high-impact area — it’s incredibly exciting.” The LIGO success is a lesson in visionary leadership, commitment and collaboration. “NSF showed foresight and determination in pursuing the goal of detecting gravitational waves and learning what these waves will be able to tell us,” Shoemaker says. “As with a lot of transformational science, you’re dealing with large-scale challenges. You have to develop new technologies and new workplace skills. You have to think in terms of longer time scales for development. NSF was far-sighted, and it made those commitments, including supporting non-teaching academic research scientists like Stuart and me.” International collaboration has been a central element. MIT and Caltech have been the pathfinders, beginning with NSF-funded projects since the 1970s to explore interferometer research. A broader, self-governing organization, the LIGO Scientific Collaboration (LSC), formed in 1997 to exploit the scientific potential of the LIGO instruments that were then under construction. Caltech and MIT play leading roles in the LSC, with responsibility for designing the equipment, creating the experiment, and operating the hardware. The LSC today includes more than 1,000 scientists at 90 universities and research institutions in 15 countries. “LSC is a big happy laboratory family where people work in collaboration and synergy across multiple labs and multiple universities,” says Shoemaker. “It’s a wonderfully distributed system with a great richness to the collaboration.” Massive computing to validate the signal and advance the science Recording a gravitational signal is one thing. LIGO teams also use sophisticated algorithms and massive computing resources to identify and validate the signal and explore its scientific significance. “Large-scale computing is absolutely key,” Anderson says. “We have very complicated workflows to execute, including one that has anywhere from 100,000 to 1 million compute tasks that need to be organized and executed. We ran something like 50 million core hours total to extract the signal and pull the science out of the data stream. We use a highly distributed computing environment with approximately 20,000 cores, and 90 percent are Intel processors.” The gravitational wave reading came on September 14, shortly after a major upgrade to the next-generation interferometer known as Advanced LIGO or ALIGO. “We had ramped up to take three months of readings and, almost immediately, we saw this very loud, very bright and highly statistically significant signal,” Anderson recalls. “It was a loud, unambiguous signal, but because it came so early in the run, it seemed almost too good to be true. For an extraordinary claim, there’s an extraordinary burden of proof, so we worked very hard and went through the data extremely carefully to try to prove ourselves wrong.” Between the September signal capture and NSF’s February 11, 2016, announcement, LSC researchers performed three major computational tasks. The first involves sifting the data from 100,000 other channels that are recorded simultaneously with gravitational wave channel. Scrutinizing these channels helps eliminate the effects of something like a minor earthquake or even a truck driving into a parking lot contaminating the data. In the second task, researchers conduct computationally intensive pseudo-experiments to evaluate the statistical significance of their signal capture. These experiments build confidence in the signal’s validity by allowing scientists to see how much louder it is compared to false alarms and noise fluctuations. With the signal validated, the third task focuses on determining what events produced the signal and how far it traveled. This work extracts the detailed parameters of the signal and matches them against models that have been developed based on relativity theory. The end result was the NSF announcement, a peer-reviewed paper in Physical Review Letters, celebrations by scientists and citizens around the world, and even appearances on popular television shows. Many observers say the team’s science leaders can also clear space on their shelves for a Nobel Prize. Reflecting the LSC’s broad network of participants, much of LIGO’s computing infrastructure is distributed around the world and performed by LSC members. Low-latency computing, which must process incoming data and keep pace as it is received, is performed on dedicated systems at the observatories, Caltech, and redundant sites in the US and Europe. Other workflows are less time-sensitive, and many are embarrassingly parallel and loosely coupled. This makes it possible to distribute the data, matching the workload to the most suitable platform and having different systems examine different portions of the parameter space. In addition to dedicated resources at Caltech and other LSC institutions, LIGO codes run on national multi-petaflops systems funded through NSF’s Extreme Science and Engineering Discovery Environment (XSEDE) program, such as the Stampede supercomputer at Texas Advanced Computing Center — a Dell PowerEdge cluster equipped with Intel Xeon Phi coprocessors — and the Comet system at the San Diego Supercomputer Center — a Dell-integrated cluster using the Intel Xeon processor E5-2600 v3 family  as well as 36 NVIDIA GPU nodes. LIGO also captures unused cycles from campus grids using NSF and the Department of Energy’s Open Science Grid (OSG), as well as from volunteer computing from the Einstein at Home project. Compute clusters access cached copies of the data over a variety of file systems. The central data archive, housed at Caltech, holds 5.3 PB of observational data and 1.1 PB of analysis results. The archive uses Oracle Hierarchical Storage Management (HSM) to manage storage tiering between disk and tape. The software infrastructure includes the HTCondor workload management system, Pegasus Workflow Management System, and Open Science Grid Software. The Python open source programming language, which Intel software experts have contributed to developing, is in heavy use. Even with such extensive resources, the LIGO workloads are compute-constrained — and rising. “Our science is computation-bound,” Anderson says. “If I had access to all the Intel computers around the world right now, I would use them. There are codes that would benefit from being run with greater sensitivity. As it is, we make compromises to get the most science we can out of the available computing, and we try to get as much computing as we can.” Much of the work involves single-precision, floating-point operations, with heavy use of numerical libraries including the Intel Math Kernel Library (Intel MKL) and the FFTW open source C subroutine library for computing discrete Fourier transforms. LIGO scientists gained a tenfold speedup for parameter extraction by optimizing LIGO codes for Intel MKL. They used Stampede for the optimization work, and Anderson says the improvements transferred well to other systems. “Because of those improvements, we could search all the parameter space we wanted to,” he comments. “Without that, we would not have been able to analyze all the data and keep up with it as it was coming in.” Moving to the Advanced LIGO instrument increased the project’s computational load, in both the number of tasks and total number of floating-point operations, by more than an order of magnitude. Among the contributing factors, the new instrument is more sensitive at lower frequencies, bringing potential events into the observable band for longer and providing a larger and more data-rich parameter space to search. Caltech upgraded its supercomputer to keep pace, adding Intel Xeon processor E3 and E5 v3 families and Intel Solid-State Drives (Intel SSDs). Workflows that need local temporary databases to manage their results got a particular boost from using the Intel SSDs as a local cache, according to Anderson. Caltech also uses some Intel Xeon Phi coprocessors, and Anderson is exploring using more of them going forward. “I’m excited that Intel Xeon Phi processors code name Knight’s Landing will be a cost-effective way to get access to lots of floating-point engines and run the same code as the Intel Xeon processor, so we don’t have to spend a lot of human resources to port it over and get it working,” Anderson says. “We’ll be well-positioned to take advantage of the Intel AVX-512 instruction set.” As work goes forward, LIGO’s need for compute cycles will climb even more sharply.  The instrumentation team will continue to tune the ALIGO detector for increased sensitivity, expanding the parameter space further. Additional gravitational wave detectors are coming on line around the world and in space, offering greater opportunities for collaboration and further insights into the workings of the universe. Astrophysicists have a long list of questions they want to answer and ways they want to tie the study of gravitational waves to other observational techniques and other fields of astronomy. Then there are the surprises. “When you open a brand new window, you see things you didn’t expect,” says Anderson. “That’s the really exciting aspect to this. We have to be prepared to discover things we don’t know about, and we really don’t know what their computational costs will be. From the perspective of software and middleware, we have to be agile enough to follow up on new things. We need to do it as efficiently as possible and at reasonable costs.” Programs such as the National Strategic Computing Initiative (NSCI), which aims to accelerate the development of exascale computers, may prove key to building on LIGO’s discoveries. “As the Advanced LIGO instruments are tuned up over the next few years to probe an order of magnitude larger volume of the universe, the corresponding computational challenge to search those data will also grow by more than an order of magnitude,” Anderson says. “In addition to the large high-throughput computing needs to search LIGO data for gravitational waves, high-performance computing is critical to solving Einstein’s equations numerically to provide information on both what LIGO ought to look for and in extracting the detailed information available in the waveforms recorded by LIGO. This HPC task will become significantly more challenging when LIGO detects other types of systems than the initial discovery of two merging black holes. For example, it will require world-class HPC resources to model the complicated physics of matter under extreme conditions, such as relativistically accurate simulations of merging neutron stars as they are ripped apart by gravitational forces, or to accurately simulate a three-dimensional supernova explosion.” At a total investment of $1.1 billion over the last three decades, LIGO is delivering benefits beyond the scientific impact. A major area of impact is in technology innovation and technology transfer to the private sector. “To make LIGO work, we had to develop the world’s most stable lasers, the world’s best mirrors, some of the world’s largest vacuum systems, as well as push the frontiers of quantum science and high performance computing,” Reitze told the Science, Space and Technology Committee. “LIGO advances the state-of-the-art in every technology it uses, and it uses lots of technology. We have partnered with many commercial technology firms in the U.S. and abroad to produce the incredible technology that LIGO uses.” LIGO is also an engine of workforce development, educating scientists who have either stayed with the program or carried their advanced skills to national laboratories, Silicon Valley, and companies in sectors such as biotechnology, aerospace and telecommunications. The excitement generated by the LIGO discoveries is helping to inspire a new generation of students, raise appreciation of science, and offer each of us a deeper sense of awe at the complexity of the universe. “Einstein was a fascinating figure,” Shoemaker says in conclusion. “The LIGO announcement once again shows that he was visionary in an almost unimaginable way. I hope taxpayers and organizations who helped support this process feel a sense of satisfaction from their participation in the endeavor. They’ve been part of something important.”


News Article
Site: http://www.scientificcomputing.com/rss-feeds/all/rss.xml/all

Molecular dynamics, quantum chemistry and quantum materials researchers use HPC resources to perform highly compute-intensive mathematical calculations and to simulate images of molecular structures. While HPC systems have been updated to increase processing speed and parallelization, many HPC codes are not optimized to take advantage of coming hardware advances, such as next-generation Intel Xeon Phi processors. This is the first in a series of articles, told from the perspective of the individuals who are updating the codes, that describes the changes various national laboratories and scientific research centers are making to improve code optimization and parallelization in order to reduce scientists’ time to discovery. 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. Both Pacific Northwest National Laboratory (PNNL) and Lawrence Berkeley National Laboratory (LBNL) are Intel Parallel Computing Centers (IPCCs) dedicated to optimizing NWChem code to effectively use the capability of the Intel Xeon Phi coprocessor. The main goal of the two teams is to optimize and modernize the NWChem code toward effective utilization of the emergent hybrid parallel computer systems based on the Intel Many Integrated Core Architecture (Intel MIC) technology to enable the scientific community to pursue new frontiers in the fields of chemistry and materials modeling. The aim of the LBNL and PNNL work is to create an optimized version of NWChem that enables the scientific community to pursue new frontiers in the fields of chemistry and materials and climate modeling. LBNL research and development focuses on implementing greater amounts of parallelism in the codes, starting with simple modifications such as adding/modifying OpenMP pragmas and refactoring to enable effective vectorization for performance improvement, all the way to exploring new algorithmic approaches that can better exploit manycore architectures. Catalytic materials used in conversion of cellulose to sugars and bio-oils In-silico design is critical in accelerating the development of new catalysts and chemical reaction and transformation processes that tackle key scientific and engineering challenges in the production of sustainable products in efficient, environmentally friendly and cost-effective ways at industrial scale. Heterogeneous catalysis has a rich history of facilitating energy-efficient selective chemical transformations and contributes to 90 percent of chemical manufacturing processes. Catalysts are central to overcoming the engineering and scientific barriers to economically feasible routes for the conversion of biomass-derived and solar-mediated fuel and chemicals into usable products. “An example is the conversion of cellulose into sugars and bio-oils, which through catalytic processes can be converted into biofuels or building blocks for industrial applications. Accurate simulations of the kinetics and thermodynamics of chemical transformations enable scientists to discover new and novel ways to predict, control and design optimal — industrially viable — catalytic activity and selectivity by rapidly scanning the large design space. It is crucial for the catalyst development process that compute-intensive computational chemistry simulations with NWChem run as efficiently on the next-generation computing platforms with the fastest time-to-solution possible,” states de Jong. Chem algorithms to take advantage of the new HPC architectures. Their work addresses the performance of several computational drivers in NWChem in these areas: Gaussian Density Functional Theory (DFT) methods, plane wave DFT formulations and multi-reference coupled cluster methods. The team is reworking codes with four major aims in mind: PNNL is rewriting NWChem algorithms to take advantage of the new HPC architectures. Their work addresses the performance of several computational drivers in NWChem in these areas: Gaussian Density Functional Theory (DFT) methods, plane wave DFT formulations and multi-reference coupled cluster methods. The team is reworking codes with four major aims in mind: In particular, the PNNL Intel PCC project focuses on the development of Intel MIC implementations of the so-called multi-reference coupled cluster (MRCC) methods, which are considered one of the most accurate methods in quantum chemistry. “These methods can be used to describe bond breaking processes, transition states in molecules, open-shell low-spin electronic states, and excited states.  Through combining novel algorithms for harnessing combined power of Intel Xeon processors and Intel Xeon Phi co-processors with several levels of parallelism, the PNNL team has achieved a considerable (3X) speedup of the MRCC codes. The achieved progress will allow us to apply expensive CC methodologies to model challenging problems in catalysis and to describe important enzymatic reactions,” indicates Kowalski. The PNNL team is also using a directive-based offload model and OpenMP 4.0 in their work. The team uses an offload model that keeps large parts of the existing Fortran code unmodified by inserting Intel Language Extensions for Offloading (LEO) constructs. This approach significantly reduces time needed to develop and validates the code and, at the same time, provides tangible performance speedups associated with the utilization of the Intel Xeon Phi coprocessors. While the Intel Xeon Phi coprocessor supports several offload programming models, each with unique properties, only the Intel LEO constructs — unfortunately a proprietary offload language that predates the availability of the target directives in OpenMP 4.0 — provide the needed flexibility of transfer of data and control, and required incremental changes to the existing Fortran source code. “Novel offloading algorithms developed in the last two years at PNNL not only helped to reduce time-to-solution of accurate methods, but also improved the scalability of the targeted implementations,” said Dr. Edoardo Aprà the lead author of the Supercomputing 2014 paper reporting on the first Intel MIC implementation of the ubiquitous CCSD(T) formalism. PNNL is currently using the OpenMP multithreading features for the parallelization of floating-point intensive computational kernels. They plan to convert the Intel LEO directives currently used for offloading to the target directives that are part of the OpenMP 4.0 standard. The LBNL team is modifying and optimizing the NWChem code to more fully utilize features of the Intel Xeon Phi processor. The team is focused on end-to-end treading of plane wave capability, the Hartree-Fock and Density Functional Theory (DFT) Fock build, and coupled cluster triples on Intel Xeon Phi processors. The work of the LBNL team is key to getting NWChem ready for the arrival of NERSC’s Cori supercomputer, which will feature the Knights Landing Xeon Phi processor code name Knight Landing. “Our goal is to at least double the performance of computational chemistry and materials software, specifically NWChem, to accelerate scientific discovery. We want to use the Intel processors as efficiently as possible to get the fastest time-to-solution for the science problems we are trying to simulate using Intel processors,” states de Jong. The LBNL team has found the integral computation to be poorly vectorizable and is having thread-safety issues with data structures present in the legacy code; they will be integrating other better vectorizable integral codes, and will explore implementing a new algorithm, using AVX-512 instructions, to improve their performance. They are also looking at using heterogeneous computing approaches, where different processors work on different operations, instead of simple OpenMP constructs to increase the amount of parallelism available to the processors. For the Fock matrix build in the Hartree-Fock or DFT algorithm, they achieved a 1.6x speedup running natively on the Intel Xeon Phi coprocessor. LBNL achieved a 3.3x speedup for the triples correction in the coupled cluster algorithm using both the Intel Xeon processor and the Intel Xeon Phi coprocessor. The key to achieving this speedup was to optimize not just the expensive loops (for which the team integrated advances made by the joint PNNL-Intel efforts), but integrating OpenMP directives in all parts of the triples code (such as those that set up data structures and the summations that occur as part of the post processing for the triples energy). “Our latest work is on the plane-wave algorithm where the most time-consuming parts are the FFT (Fast Fourier Transform) and matrix multiplications on tall and skinny matrices (200x10,000, for example). Working together with PNNL, we have transformed the complete plane-wave algorithm to utilize OpenMP. Our work at LBNL has focused on the tall and skinny matrices. We developed a reduce algorithm for these special matrix multiplications using OpenMP teams and we achieved a 5.5x speedup over Intel’s MKL library on the Intel Xeon processor E5-2680 v3, formerly known as Haswell. We’re currently implementing this in our plane-wave algorithm and will be doing benchmarking on the whole code to assess the overall speedup,” states de Jong. How HPC will aid chemistry and materials research The goal at PNNL is to optimize coding efforts in such a way that they will be easily available to integrate into other open-source computational chemistry software efforts. PNNL, LBNL and Georgia Tech are working together on the project to update NWChem code. NWChem code optimized to run on advanced hardware, such as the Intel Xeon Phi coprocessor, will be reviewed and released so that it can be used by all NWChem users and researchers. Scientists are currently restricted in what they can do in their research by limitations of the supercomputers or HPC software models. They often have to reduce their research models to make them fit in the HPC resources currently available. Researchers need to be able to use the next-generation computers to run large computations faster in research areas such as solar cells or to develop novel complex organic-inorganic materials for batteries, photovoltaic or thermo-electrics that can serve sustainable energy sources. “Many of our algorithms inherently do not have long vector loops that can be easily transformed using OpenMP. I am not sure OpenMP is the complete solution. We will have to develop models that utilize heterogeneous computing on nodes with different threads/cores working on different parts of the algorithm simultaneously,” states de Jong. Modernization of underlying codes for efficient use of manycore architectures is required for benefits to be realized. According to Kowalski, “The rapid development of computer technology and emergence of petascale hybrid architectures offer an enormous increase in computational power, which will have a transformative effect on the whole area of computational chemistry, including the shift in the system-size limit tractable by many-body methods and opportunity of using accurate many-body techniques, which are characterized by steep numerical scaling. The main goal is to enable accurate methods capable of describing complicated electron correlations effects, stemming from the instantaneous interactions between electrons in molecules. This includes two major classes of methodologies: Linda Barney is the founder and owner of Barney and Associates, a technical/marketing writing, training and web design firm in Beaverton, OR. R&D 100 AWARD ENTRIES NOW OPEN: Establish your company as a technology leader! For more than 50 years, the R&D 100 Awards have showcased new products of technological significance. 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White G.,AVX | Carastro L.,AVX
Electronic Products | Year: 2012

Most smart phones use multiple antennas to support each wireless frequency. Antennas add cost and provide interoperability challenges that can impact performance. To minimize the number of antennas for a multiband application, many designers incorporate low-loss high-Q RF components into their designs. These devices include capacitors for matching circuitry, inductors, and diplexers. Tight tolerance high-Q low-loss capacitors, inductors, and diplexers can be used to improve isolation between bands, which can minimize the number of antennas, while enhancing performance and providing savings in both cost and size. A recently patented process called Multilayer Organics (MLO) enables the development of RF components with considerably higher Q values at high frequencies than previously available. Source

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