The Impact of New EUV Diagnostics on CME-Related Kinematics

We present the application of novel diagnostics to the spectroscopic observation of a Coronal Mass Ejection (CME) on disk by the Extreme Ultraviolet Imaging Spectrometer (EIS) on the Hinode spacecraft. We apply a recently developed line profile asymmetry analysis to the spectroscopic observation of NOAA AR 10930 on 14-15 December 2006 to three raster observations before and during the eruption of a 1000km/s CME. We see the impact that the observer's line-of-sight and magnetic field geometry have on the diagnostics used. Further, and more importantly, we identify the on-disk signature of a high-speed outflow behind the CME in the dimming region arising as a result of the eruption. Supported by recent coronal observations of the STEREO spacecraft, we speculate about the momentum flux resulting from this outflow as a secondary momentum source to the CME. The results presented highlight the importance of spectroscopic measurements in relation to CME kinematics, and the need for full-disk synoptic spectroscopic observations of the coronal and chromospheric plasmas to capture the signature of such explosive energy release as a way of providing better constraints of CME propagation times to L1, or any other point of interest in the heliosphere.Comment: Accepted to appear in Solar Physics Topical Issue titled "Remote Sensing of the Inner Heliosphere". Manuscript has 14 pages, 5 color figures. Movies supporting the figures can be found in http://download.hao.ucar.edu/pub/mscott/papers/Weathe

Long Memory Persistence in the Factor of Implied Volatility Dynamics

The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied Volatility Surface (IV S). Practical applications require reducing the dimension and characterize its dynamics through a small number of factors. Such dimension reduction is summarized by a Dynamic Semiparametric Factor Model (DSFM) that characterizes the IV S itself and their movements across time by a multivariate time series of factor loadings. This paper focuses on investigating long range dependence in the factor loadings series. Our result reveals that shocks to volatility persist for a very long time, affecting significantly stock prices. For appropriate representation of the series dynamics and the possibility of improved forecasting, we model the long memory in levels and absolute returns using the class of fractional integrated volatility models that provide flexible structure to capture the slow decaying autocorrelation function reasonably well.Implied Volatility, Dynamic Semiparametric Factor Modeling, Long Memory, Fractional Integrated Volatility Models.

Speculative Approximations for Terascale Analytics

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quickly identify sub-optimal configurations are the principal causes. In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. We apply the proposed techniques to distributed gradient descent optimization -- batch and incremental -- for support vector machines and logistic regression models. We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big Data analytics system -- and evaluate their performance over terascale-size synthetic and real datasets. The results confirm that as many as 32 configurations can be evaluated concurrently almost as fast as one, while sub-optimal configurations are detected accurately in as little as a $1/20^{\text{th}}$ fraction of the time

Randomized cache placement for eliminating conflicts

Applications with regular patterns of memory access can experience high levels of cache conflict misses. In shared-memory multiprocessors conflict misses can be increased significantly by the data transpositions required for parallelization. Techniques such as blocking which are introduced within a single thread to improve locality, can result in yet more conflict misses. The tension between minimizing cache conflicts and the other transformations needed for efficient parallelization leads to complex optimization problems for parallelizing compilers. This paper shows how the introduction of a pseudorandom element into the cache index function can effectively eliminate repetitive conflict misses and produce a cache where miss ratio depends solely on working set behavior. We examine the impact of pseudorandom cache indexing on processor cycle times and present practical solutions to some of the major implementation issues for this type of cache. Our conclusions are supported by simulations of a superscalar out-of-order processor executing the SPEC95 benchmarks, as well as from cache simulations of individual loop kernels to illustrate specific effects. We present measurements of instructions committed per cycle (IPC) when comparing the performance of different cache architectures on whole-program benchmarks such as the SPEC95 suite.Peer ReviewedPostprint (published version