Federal Market Information Technology in the Post Flash Crash Era: Roles for Supercomputing

This paper describes collaborative work between active traders, regulators, economists, and supercomputing researchers to replicate and extend investigations of the Flash Crash and other market anomalies in a National Laboratory HPC environment. Our work suggests that supercomputing tools and methods will be valuable to market regulators in achieving the goal of market safety, stability, and security. Research results using high frequency data and analytics are described, and directions for future development are discussed. Currently the key mechanism for preventing catastrophic market action are “circuit breakers.” We believe a more graduated approach, similar to the “yellow light” approach in motorsports to slow down traffic, might be a better way to achieve the same goal. To enable this objective, we study a number of indicators that could foresee hazards in market conditions and explore options to confirm such predictions. Our tests confirm that Volume Synchronized Probability of Informed Trading (VPIN) and a version of volume Herfindahl-Hirschman Index (HHI) for measuring market fragmentation can indeed give strong signals ahead of the Flash Crash event on May 6 2010. This is a preliminary step toward a full-fledged early-warning system for unusual market conditions

Topography of Lipid Droplet-Associated Proteins: Insights from Freeze-Fracture Replica Immunogold Labeling

Lipid droplets are not merely storage depots for superfluous intracellular lipids in times of hyperlipidemic stress, but metabolically active organelles involved in cellular homeostasis. Our concepts on the metabolic functions of lipid droplets have come from studies on lipid droplet-associated proteins. This realization has made the study of proteins, such as PAT family proteins, caveolins, and several others that are targeted to lipid droplets, an intriguing and rapidly developing area of intensive inquiry. Our existing understanding of the structure, protein organization, and biogenesis of the lipid droplet has relied heavily on microscopical techniques that lack resolution and the ability to preserve native cellular and protein composition. Freeze-fracture replica immunogold labeling overcomes these disadvantages and can be used to define at high resolution the precise location of lipid droplet-associated proteins. In this paper illustrative examples of how freeze-fracture immunocytochemistry has contributed to our understanding of the spatial organization in the membrane plane and function of PAT family proteins and caveolin-1 are presented. By revisiting the lipid droplet with freeze-fracture immunocytochemistry, new perspectives have emerged which challenge prevailing concepts of lipid droplet biology and may hopefully provide a timely impulse for many ongoing studies

High-throughput Characterization of Porous Materials Using Graphics Processing Units

We have developed a high-throughput graphics processing units (GPU) code that can characterize a large database of crystalline porous materials. In our algorithm, the GPU is utilized to accelerate energy grid calculations where the grid values represent interactions (i.e., Lennard-Jones + Coulomb potentials) between gas molecules (i.e., CH$_{4}$ and CO$_{2}$) and material's framework atoms. Using a parallel flood fill CPU algorithm, inaccessible regions inside the framework structures are identified and blocked based on their energy profiles. Finally, we compute the Henry coefficients and heats of adsorption through statistical Widom insertion Monte Carlo moves in the domain restricted to the accessible space. The code offers significant speedup over a single core CPU code and allows us to characterize a set of porous materials at least an order of magnitude larger than ones considered in earlier studies. For structures selected from such a prescreening algorithm, full adsorption isotherms can be calculated by conducting multiple grand canonical Monte Carlo simulations concurrently within the GPU

PointCloudExplore 2: Visual exploration of 3D gene expression

To better understand how developmental regulatory networks are defined inthe genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP)has developed a suite of methods to describe 3D gene expression data, i.e.,the output of the network at cellular resolution for multiple time points. To allow researchersto explore these novel data sets we have developed PointCloudXplore (PCX).In PCX we have linked physical and information visualization views via the concept ofbrushing (cell selection). For each view dedicated operations for performing selectionof cells are available. In PCX, all cell selections are stored in a central managementsystem. Cells selected in one view can in this way be highlighted in any view allowingfurther cell subset properties to be determined. Complex cell queries can be definedby combining different cell selections using logical operations such as AND, OR, andNOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), thelatest publicly available version of PCX. PCX2 has shown to be an effective tool forvisual exploration of 3D gene expression data. We discuss (i) all views available inPCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture ofPCX2., and (iv) illustrate the usefulness of PCX2 using selected examples

Query-Driven Visualization and Analysis

This report focuses on an approach to high performance visualization and analysis, termed query-driven visualization and analysis (QDV). QDV aims to reduce the amount of data that needs to be processed by the visualization, analysis, and rendering pipelines. The goal of the data reduction process is to separate out data that is "scientifically interesting'' and to focus visualization, analysis, and rendering on that interesting subset. The premise is that for any given visualization or analysis task, the data subset of interest is much smaller than the larger, complete data set. This strategy---extracting smaller data subsets of interest and focusing of the visualization processing on these subsets---is complementary to the approach of increasing the capacity of the visualization, analysis, and rendering pipelines through parallelism. This report discusses the fundamental concepts in QDV, their relationship to different stages in the visualization and analysis pipelines, and presents QDV's application to problems in diverse areas, ranging from forensic cybersecurity to high energy physics

Linking Advanced Visualization and MATLAB for the Analysis of 3D Gene Expression Data

Three-dimensional gene expression PointCloud data generated by the Berkeley Drosophila Transcription Network Project (BDTNP) provides quantitative information about the spatial and temporal expression of genes in early Drosophila embryos at cellular resolution. The BDTNP team visualizes and analyzes Point-Cloud data using the software application PointCloudXplore (PCX). To maximize the impact of novel, complex data sets, such as PointClouds, the data needs to be accessible to biologists and comprehensible to developers of analysis functions. We address this challenge by linking PCX and Matlab via a dedicated interface, thereby providing biologists seamless access to advanced data analysis functions and giving bioinformatics researchers the opportunity to integrate their analysis directly into the visualization application. To demonstrate the usefulness of this approach, we computationally model parts of the expression pattern of the gene even skipped using a genetic algorithm implemented in Matlab and integrated into PCX via our Matlab interface

Feature-based Analysis of Plasma-based Particle Acceleration Data

Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. To facilitate scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs, we describe a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. The automatic feature detection in combination with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features enables an effective top-down data exploration process, starting from a high-level, feature-based view down to the level of individual particles. We describe the application of our analysis in practice to analyze simulations of single pulse and dual and triple colliding pulse accelerator designs, and to study the formation and evolution of particle beams, to compare substructures of a beam and to investigate transverse particle loss