Current and future surveys of large-scale cosmic structure are associated with a massive and complex datastream to study, characterize, and ultimately understand the physics behind the two major components of the 'Dark Universe', dark energy and dark matter. In addition, the surveys also probe primordial perturbations and carry out fundamental measurements, such as determining the sum of neutrino masses. Large-scale simulations of structure formation in the Universe play a critical role in the interpretation of the data and extraction of the physics of interest. Just as survey instruments continue to grow in size and complexity, so do the supercomputers that enable these simulations. Here we report on HACC (Hardware/Hybrid Accelerated Cosmology Code), a recently developed and evolving cosmology N-body code framework, designed to run efficiently on diverse computing architectures and to scale to millions of cores and beyond. HACC can run on all current supercomputer architectures and supports a variety of programming models and algorithms. It has been demonstrated at scale on Cell-and GPU-accelerated systems, standard multicore node clusters, and Blue Gene systems. HACC's design allows for ease of portability, and at the same time, high levels of sustained performance on the fastest supercomputers available. We present a description of the design philosophy of HACC, the underlying algorithms and code structure, and outline implementation details for several specific architectures. We show selected accuracy and performance results from some of the largest high resolution cosmological simulations so far performed, including benchmarks evolving more than 3.6 trillion particles.
Trace metals play important roles in normal and in disease-causing biological functions. X-ray fluorescence microscopy reveals trace elements with no dependence on binding affinities (unlike with visible light fluorophores) and with improved sensitivity relative to electron probes. However, X-ray fluorescence is not very sensitive for showing the light elements that comprise the majority of cellular material. Here we show that X-ray ptychography can be combined with fluorescence to image both cellular structure and trace element distribution in frozen-hydrated cells at cryogenic temperatures, with high structural and chemical fidelity. Ptychographic reconstruction algorithms deliver phase and absorption contrast images at a resolution beyond that of the illuminating lens or beam size. Using 5.2-keV X-rays, we have obtained sub-30-nm resolution structural images and ∼90-nm-resolution fluorescence images of several elements in frozen-hydrated green algae. This combined approach offers a way to study the role of trace elements in their structural context. ptychography | X-ray fluorescence microscopy | cryogenic biological samples X -ray fluorescence microscopy (XFM) offers unparalleled sensitivity for quantitative mapping of elements, especially trace metals which play a critical role in many biological processes (1-3). It is complementary to light microscopy, which can study some elemental content in live cells (with superresolution techniques possible) but which is more difficult to quantitate because it depends on the binding affinities of fluorophores. However, XFM does not usually show much cellular ultrastructure, because the light elements (such as H, C, N, and O, which are the main constituents of biological materials) have low fluorescence yield (4). At the multi-keV X-ray energies needed to excite most X-ray fluorescence lines of interest, these light elements show little absorption contrast, but phase contrast can be used to image cellular structure (5, 6) and this can be combined with scanned-beam XFM (7-11).One can also acquire phase-contrast X-ray images with a resolution beyond X-ray lens limits by recording the diffraction pattern from a coherently illuminated, noncrystalline sample in an approach called coherent diffraction imaging (CDI) (12). This approach has been used to image isolated dried cells (13-15), and 3-nm resolution has been achieved when imaging silver nanocubes (16). The traditional CDI approach requires that samples meet a so-called "finite support" (17) requirement with no observable scattering outside of a defined region; although some limited success has been obtained (18,19), this finite support condition has proven difficult to achieve with single cells surrounded by ice layers. Ptychography (20-22) is a recently realized CDI method [with an older history (23)] that circumvents this isolated cell requirement by instead scanning a limitedsize coherent illumination spot across the sample. Ptychography has been used to image freeze-dried diatoms at 30-nm resolution (24) and bacter...
Abstract:Ptychography is an imaging method whereby a coherent beam is scanned across an object, and an image is obtained by iterative phasing of the set of diffraction patterns. It is able to be used to image extended objects at a resolution limited by scattering strength of the object and detector geometry, rather than at an optics-imposed limit. As technical advances allow larger fields to be imaged, computational challenges arise for reconstructing the correspondingly larger data volumes, yet at the same time there is also a need to deliver reconstructed images immediately so that one can evaluate the next steps to take in an experiment. Here we present a parallel method for real-time ptychographic phase retrieval. It uses a hybrid parallel strategy to divide the computation between multiple graphics processing units (GPUs) and then employs novel techniques to merge sub-datasets into a single complex phase and amplitude image. Results are shown on a simulated specimen and a real dataset from an X-ray experiment conducted at a synchrotron light source.
Today's computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE's science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. This article is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science.
Ptychography is a coherent diffraction imaging (CDI) method for extended objects in which diffraction patterns are acquired sequentially from overlapping coherent illumination spots. The object's complex transmission function can be reconstructed from those diffraction patterns at a spatial resolution limited only by the scattering strength of the object and the detector geometry. Most experiments to date have positioned the illumination spots on the sample using a move-settle-measure sequence in which the move and settle steps can take longer to complete than the measure step. We describe here the use of a continuous "fly-scan" mode for ptychographic data collection in which the sample is moved continuously, so that the experiment resembles one of integrating the diffraction patterns from multiple probe positions. This allows one to use multiple probe mode reconstruction methods to obtain an image of the object and also of the illumination function. We show in simulations, and in x-ray imaging experiments, some of the characteristics of fly-scan ptychography, including a factor of 25 reduction in the data acquisition time. This approach will become increasingly important as brighter x-ray sources are developed, such as diffraction limited storage rings.
X-ray microscopy can be used to image whole, unsectioned cells in their native hydrated state. It complements the higher resolution of electron microscopy for submicrometer thick specimens, and the molecule-specific imaging capabilites of fluorescence light microscopy. We describe here the first use of fast, continuous x-ray scanning of frozen hydrated cells for simultaneous sub-20 nm resolution ptychographic transmission imaging with high contrast, and sub-100 nm resolution deconvolved x-ray fluorescence imaging of diffusible and bound ions at native concentrations, without the need to add specific labels. By working with cells that have been rapidly frozen without the use of chemical fixatives, and imaging them under cryogenic conditions, we are able to obtain images with well preserved structural and chemical composition, and sufficient stability against radiation damage to allow for multiple images to be obtained with no observable change.
We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.Index Terms-In situ visualization, ensemble visualization, parameter space exploration, deep learning, image synthesis.
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