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High-performance online spatial and temporal aggregations on multi-core CPUs and many-core GPUs

Jianting Zhang, Simin You, Le Gruenwald
2012 Proceedings of the fifteenth international workshop on Data warehousing and OLAP - DOLAP '12  
Performance comparison on spatial association Experiment Results for Different Aggregations on Multi-Core CPUs (in Seconds) Cloud computing+MapReduce+Hadoop Multicore CPUs GPGPU Computing: From Fermi  ...  ")Finding the nearest tax blocks for 170 million taxi pickup locations (to aggregate based on tax block types) -Using open source libspatiaindex+GDAL (to avoid database overhead) Table 2 . 2 and Motivation  ...  in about 15 seconds and achieved 13X speedup over optimized serial CPU implementation. • Spatial, temporal and spatiotemporal aggregations can be processed in the order of a fraction of a second on GPUs  ... 
doi:10.1145/2390045.2390060 dblp:conf/dolap/ZhangYG12 fatcat:sj4qc7iw75dlfmuduc22y5vofq

Parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUs

Jianting Zhang, Simin You, Le Gruenwald
2014 Information Systems  
on multi-core CPUs and many-core Graphics Processing Units (GPUs).  ...  on both GPUs and multi-core CPUs.  ...  Camille Kamga at CUNY City College for providing the NYC taxi trip data and Dr. Hongmian Gong at CUNY Hunter College for insightful discussions on urban applications of GPS data.  ... 
doi:10.1016/j.is.2014.01.005 fatcat:y43k2qlcqrg6lnt5ryitfnpahy

High-Performance Spatial Query Processing on Big Taxi Trip Data Using GPGPUs

Jianting Zhang, Simin You, Le Gruenwald
2014 2014 IEEE International Congress on Big Data  
GPUs.  ...  Experiments on nearly 170 million taxi trips in the New York City (NYC) in 2009 and 735,488 tax lot polygons with 4,698,986 vertices have demonstrated the efficiency of the proposed techniques: the GPU  ...  While we have addressed point-to-polyline distance type of queries on both multi-core CPUs and many-core GPUs in [1] , we will focus on Nearest Neighbor (NN) type of queries between points and polygons  ... 
doi:10.1109/bigdata.congress.2014.20 dblp:conf/bigdata/ZhangYG14 fatcat:gvakasmfmfdazhxiaqwredb73a

Mining Massive-Scale Spatiotemporal Trajectories in Parallel: A Survey [chapter]

Pengtao Huang, Bo Yuan
2015 Lecture Notes in Computer Science  
Unit (GPU), MapReduce and Field Programmable Gate Array (FPGA).  ...  We also give an in-depth analysis of the related techniques and compare them according to their principles and performance.  ...  and many-core GPUs to associate taxi pickup location points with their nearest street segments and then reduce online spatial, temporal and spatiotemporal aggregations to relational aggregations, which  ... 
doi:10.1007/978-3-319-25660-3_4 fatcat:qdkslwlgjzesdfnzvkbrpsgmti

U2SOD-DB

Jianting Zhang, Camille Kamga, Hongmian Gong, Le Gruenwald
2012 Proceedings of the ACM SIGKDD International Workshop on Urban Computing - UrbComp '12  
Spatial and temporal aggregations on 150 million pickup locations and times in middle-town and downtown Manhattan areas in the New York City (NYC) can be completed in a fraction of a second.  ...  (100X) speedup using a hybrid CPU-GPU approach.  ...  CPUs and many-core GPUs.  ... 
doi:10.1145/2346496.2346522 dblp:conf/kdd/ZhangKGG12 fatcat:aqxz5s23jjblziwllw6oc64eje

Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data [article]

Andrew J. Holbrook, Charles E. Loeffler, Seth R. Flaxman, Marc A. Suchard
2020 arXiv   pre-print
We show that, with care, one may parallelize these calculations using both central and graphics processing unit implementations to achieve over 100-fold speedups over single-core processing.  ...  Using a simple adaptive Metropolis-Hastings scheme, we apply our high-performance computing framework to a Bayesian analysis of big gunshot data generated in Washington D.C. between the years of 2006 and  ...  Acknowledgments The research leading to these results has received funding through National Institutes of Health grant U19 AI135995 and National Science Foundation grant DMS1264153.  ... 
arXiv:2005.10123v1 fatcat:tl5375ttvrey5kfyh7g5tmtwg4

A vision for GPU-accelerated parallel computation on geo-spatial datasets

Sushil K. Prasad, Michael McDermott, Satish Puri, Dhara Shah, Danial Aghajarian, Shashi Shekhar, Xun Zhou
2015 SIGSPATIAL Special  
A GPU can yield one-to-two orders of magnitude speedups and will become increasingly more affordable and energy efficient due to mass marketing for gaming.  ...  We also survey the current landscape of representative geo-spatial problems and their parallel, GPU-based solutions. 1  ...  The individual compute nodes of these devices (as of clusters) consist of multi-core CPUs containing tens of processing cores and many-core GPUs containing hundreds to thousands of cores, both with shared-memory  ... 
doi:10.1145/2766196.2766200 fatcat:ayy3ozgvxvccxirmi3er65no54

U2STRA

Jianting Zhang, Simin You, Le Gruenwald
2012 Proceedings of the 2012 ACM workshop on City data management workshop - CDMW '12  
An impressive 87X speedup for spatial aggregations of GPS point locations and 25-40X speedups for trajectory queries over serial CPU implementations have been achieved.  ...  Many trajectory queries are both I/O and computing intensive.  ...  This is a natural choice before multi-core CPUs and many-core GPUs become the mainstream commodity processors.  ... 
doi:10.1145/2390226.2390229 fatcat:7v2spz6lazdgpbcbynxpvxu6xe

Approximate similarity search for online multimedia services on distributed CPU–GPU platforms

George Teodoro, Eduardo Valle, Nathan Mariano, Ricardo Torres, Wagner Meira, Joel H. Saltz
2013 The VLDB journal  
Hypercurves executes in hybrid CPU-GPU environments and is  ...  In addition, the nature of the interactions between users and online services creates fluctuating query request rates throughout execution, which requires a similarity search engine to adapt to better  ...  Acknowledgements We would like to express our gratitude to the reviewers for their valuable comments, which helped us to improve our work both in terms of content and presentation. E. Valle  ... 
doi:10.1007/s00778-013-0329-7 fatcat:rpcxwmr5hvdwll73wvvnuxirke

Prototyping A Web-based High-Performance Visual Analytics Platform for Origin-Destination Data

Jianting Zhang, Simin You, Yinglong Xia
2015 Proceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics - UrbanGIS'15  
OD polygons by data parallel scanning OD point locations for cache efficiency and easy parallelization on conventional multi-core hardware for high efficiency and performance.  ...  In this study, by leveraging our experiences in Web-GIS and parallel spatial data processing and learning from successful OD data visualization case studies, we have developed a Web-based high-performance  ...  The simplified technique is easy to implement and deploy on conventional multi-core CPUs and does not require a GPU.  ... 
doi:10.1145/2835022.2835025 dblp:conf/gis/ZhangYX15 fatcat:26fsyntjdnbdpgrdp3ehwlk3hm

GPU rasterization for real-time spatial aggregation over arbitrary polygons

Eleni Tzirita Zacharatou, Harish Doraiswamy, Anastasia Ailamaki, Cláudio T. Silva, Juliana Freiref
2017 Proceedings of the VLDB Endowment  
Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions.  ...  In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times  ...  Given that our test system has a quad core processor (with a total of 8 threads), the multi-core CPU implementation provides a 5× speedup over the single-core CPU implementation.  ... 
doi:10.14778/3157794.3157803 fatcat:be7fa2eijbczzglvrfpa5uu4zi

Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics [article]

Milad Makkie, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu
2018 arXiv   pre-print
To implement such a model, we have created an enhanced processing pipeline on the top of Apache Spark and Tensorflow library, leveraging from a very large cluster of GPU machines.  ...  This model aims to both learn the complex hierarchical structure of the tfMRI data and to leverage the processing power of multiple GPUs in a distributed fashion.  ...  ACKNOWLEDGMENT This work was supported by National Institutes of Health (DA033393, AG042599) and National Science Foundation (IIS-1149260, CBET-1302089, and BCS-1439051).  ... 
arXiv:1710.08961v3 fatcat:saxu4iobqrakdg6borq3lygjcu

Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics

Milad Makkie, Heng Huang, Yu Zhao, Athanasios V. Vasilakos, Tianming Liu
2019 Neurocomputing  
To deploy such a model, we have created an enhanced processing pipeline on the top of Apache Spark and Tensorflow, leveraging from a large cluster of GPU nodes over cloud.  ...  This model aims to both learn the complex hierarchical structures of the tfMRI big data and to leverage the processing power of multiple GPUs in a distributed fashion.  ...  Acknowledgments This work was supported by National Institutes of Health (DA033393, AG042599) and National Science Foundation (IIS-1149260, CBET-1302089, and BCS-1439051). Makkie et al.  ... 
doi:10.1016/j.neucom.2018.09.066 pmid:31354187 pmcid:PMC6660166 fatcat:cssvsm4255gsfmcgqh3ica7nwa

MAP-Vis: A Distributed Spatio-Temporal Big Data Visualization Framework Based on a Multi-Dimensional Aggregation Pyramid Model

Guan, Xie, Han, Zeng, Shen, Xing
2020 Applied Sciences  
Firstly, we propose a generic multi-dimensional aggregation pyramid (MAP) model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid.  ...  During the exploration and visualization of big spatio-temporal data, massive volume poses a number of challenges to the achievement of interactive visualization, including large memory consumption, high  ...  Acknowledgments: The authors are grateful to the editor and reviewers for their careful and valuable suggestions. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10020598 fatcat:wmwsr2rfvvde3nyjt7oflvisji

High performance FPGA and GPU complex pattern matching over spatio-temporal streams

Roger Moussalli, Ildar Absalyamov, Marcos R. Vieira, Walid Najjar, Vassilis J. Tsotras
2014 Geoinformatica  
In this paper, we present a study on FPGA-and GPU-based architectures processing complex patterns on streams of spatio-temporal data.  ...  We show an extensive performance evaluation where FPGA and GPU setups outperform the current state-of-the-art (single-threaded) CPU-based approaches, by over three orders of magnitude for FPGAs (for expressive  ...  Acknowledgments This work has been partially supported by National Science Foundation awards: CCF-1219180, IIS-1161997 and IIS-1305253.  ... 
doi:10.1007/s10707-014-0217-3 fatcat:hqzepvsoencf3fqojtcwdgcdle
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