Modeling and Implementation of an Asynchronous Approach to Integrating HPC and Big Data Analysis

With the emergence of exascale computing and big data analytics, many important scientific applications require the integration of computationally intensive modeling and simulation with data-intensive analysis to accelerate scientific discovery. In this paper, we create an analytical model to steer the optimization of the end-to-end time-to-solution for the integrated computation and data analysis. We also design and develop an intelligent data broker to efficiently intertwine the computation stage and the analysis stage to practically achieve the optimal time-to-solution predicted by the analytical model. We perform experiments on both synthetic applications and real-world computational fluid dynamics (CFD) applications. The experiments show that the analytic model exhibits an average relative error of less than 10%, and the application performance can be improved by up to 131% for the synthetic programs and by up to 78% for the real-world CFD application

Designing a Parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems

Lattice Boltzmann method (LBM) is an important computational fluid dynamics (CFD) approach to solving the Naiver-Stokes equations and simulating complex fluid flows. LBM is also well known as a memory bound problem and its performance is limited by the memory access time on modern computer systems. In this paper, we design and develop both sequential and parallel memory-aware algorithms to optimize the performance of LBM. The new memory-aware algorithms can enhance data reuses across multiple time steps to further improve the performance of the original and fused LBM. We theoretically analyze the algorithms to provide an insight into how data reuses occur in each algorithm. Finally, we conduct experiments and detailed performance analysis on two different manycore systems. Based on the experimental results, the parallel memory-aware LBM algorithm can outperform the fused LBM by up to 292% on the Intel Haswell system when using 28 cores, and by 302 % on the Intel Skylake system when using 48 cores

A Hybrid Wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series

Intratumoral Genetic and Functional Heterogeneity in Pediatric Glioblastoma.

Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. To understand the mechanisms of tumor evolution in this cancer, we performed whole-genome sequencing with linked reads on longitudinally resected pGBM samples. Our analyses showed that all diagnostic and recurrent samples were collections of genetically diverse subclones. Clonal composition rapidly evolved at recurrence, with less than 8% of nonsynonymous single-nucleotide variants being shared in diagnostic-recurrent pairs. To track the origins of the mutational events observed in pGBM, we generated whole-genome datasets for two patients and their parents. These trios showed that genetic variants could be (i) somatic, (ii) inherited from a healthy parent, or (iii) de novo in the germlines of pGBM patients. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to nondividing cells. Interestingly, radiation and antimitotic chemotherapeutics did not increase overall tumor burden upon recurrence. These findings support an important role for slow-cycling stem cell populations in contributing to recurrences, because slow-cycling cell populations are expected to be less prone to genotoxic stress induced by these treatments and therefore would accumulate few mutations. Our results highlight the need for new targeted treatments that account for the complex functional hierarchies and genomic heterogeneity of pGBM. SIGNIFICANCE: This work challenges several assumptions regarding the genetic organization of pediatric GBM and highlights mutagenic programs that start during early prenatal development.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/9/2111/F1.large.jpg.Wellcome Trust Royal Societ

Transparent and conductive indium doped cadmium oxide thin films prepared by pulsed filtered cathodic arc deposition

Indium doped cadmium oxide (CdO:In) films with different In concentrations were prepared on low-cost glass substrates by pulsed filtered cathodic arc deposition (PFCAD). It is shown that polycrystalline CdO:In films with smooth surface and dense structure are obtained. In-doping introduces extra electrons leading to remarkable improvements of electron mobility and conductivity, as well as improvement in the optical transmittance due to the Burstein Moss effect. CdO:In films on glass substrates with thickness near 230 nm show low resistivity of 7.23 10-5 cm, high electron mobility of 142 cm2/Vs, and mean transmittance over 80percent from 500-1250 nm (including the glass substrate). These high quality pulsed arc-grown CdO:In films are potentially suitable for high efficiency multi-junction solar cells that harvest a broad range of the solar spectrum