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HXPY: A High-Performance data Processing Package for Financial time-series data

Journal of Computer Science & Technology 2023年第1期38卷 3-24页

作者：郭家栋彭靖姝苑航倪明选 The Hong Kong University of Science and Technology Hong KongChina International Digital Economy Academy Shenzhen 518048China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511455China

A tremendous amount of data has been generated by global financial markets everyday,and such time-series data needs to be analyzed in real time to explore its potential *** recent years,we have witnessed the successful adoption of machine learning models on financial data,where the importance of accuracy and timeliness demands highly effective computing ***,traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues,such as the outlier handling with stock suspension in Pandas and *** this paper,we propose HXPY,a high-performance data processing package with a C++/Python interface for financial time-series *** supports miscellaneous acceleration techniques such as the streaming algorithm,the vectorization instruction set,and memory optimization,together with various functions such as time window functions,group operations,down-sampling operations,cross-section operations,row-wise or column-wise operations,shape transformations,and alignment *** results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its *** MiBs to GiBs data,HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.

关键词： dataframe time-series data SIMD(single instruction multiple data) CUDA(Compute Unified Device Architecture)

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Classification of Vegetation in North Tibet Plateau Based on MODIS time-series data

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Wuhan University Journal of Natural Sciences 2008年第3期13卷 273-278页

作者： LU Yuan YAN Yan TAO Heping Institute of Mountain Hazards and Environment ChineseAcademy of Sciences Chengdu 610041 Sichuan China School of Resource and Environmental Sciences GuangxiTeachers Education University Nanning 530001 Guangxi China

Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.

关键词： vegetation classification moderate resolution imaging spectroradiometer normalized difference vegetation index time-series data North Tibet Plateau

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A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems

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Visual Informatics 2018年第1期2卷 98-110页

作者： Takanori Fujiwara Jianping Kelvin Li Misbah Mubarak Caitlin Ross Christopher D.Carothers Robert B.Ross Kwan-Liu Ma University of California DavisUnited States Argonne National Laboratory United States Rensselaer Polytechnic Institute United States

The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network *** of the state of the art supercomputers use networks based on the increasingly popular Dragonfly *** is crucial to study the behavior and performance of different parallel applications running on Dragonfly networks in order to make optimal system configurations and design choices,such as job scheduling and routing ***,in order to study these temporal network behavior,we would need a tool to analyze and correlate numerous sets of multivariate time-series data collected from the Dragonfly's multi-level *** paper presents such a tool-a visual analytics system-that uses the Dragonfly network to investigate the temporal behavior and optimize the communication performance of a *** coupled interactive visualization with time-series analysis methods to help reveal hidden patterns in the network behavior with respect to different parallel applications and system *** system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies,which effectively helps visual analysis *** demonstrate the effectiveness of the system with a set of case *** system and findings can not only help improve the communication performance of supercomputing applications,but also the network performance of next-generation supercomputers.

关键词： Supercomputing Parallel communication network Dragonfly networks time-series data Performance analysis Visual analytics

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