Acceleration of Computational Geometry Algorithms for High Performance Computing Based Geo-Spatial Big Data Analysis

Geo-Spatial computing and data analysis is the branch of computer science that deals with real world location-based data. Computational geometry algorithms are algorithms that process geometry/shapes and is one of the pillars of geo-spatial computing. Real world map and location-based data can be huge in size and the data structures used to process them extremely big leading to huge computational costs. Furthermore, Geo-Spatial datasets are growing on all V’s (Volume, Variety, Value, etc.) and are becoming larger and more complex to process in-turn demanding more computational resources. High Performance Computing is a way to breakdown the problem in ways that it can run in parallel on big computers with massive processing power and hence reduce the computing time delivering the same results but much faster.This dissertation explores different techniques to accelerate the processing of computational geometry algorithms and geo-spatial computing like using Many-core Graphics Processing Units (GPU), Multi-core Central Processing Units (CPU), Multi-node setup with Message Passing Interface (MPI), Cache optimizations, Memory and Communication optimizations, load balancing, Algorithmic Modifications, Directive based parallelization with OpenMP or OpenACC and Vectorization with compiler intrinsic (AVX). This dissertation has applied at least one of the mentioned techniques to the following problems. Novel method to parallelize plane sweep based geometric intersection for GPU with directives is presented. Parallelization of plane sweep based Voronoi construction, parallelization of Segment tree construction, Segment tree queries and Segment tree-based operations has been presented. Spatial autocorrelation, computation of getis-ord hotspots are also presented. Acceleration performance and speedup results are presented in each corresponding chapter

MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data

In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms

Parallelization of Plane Sweep Based Voronoi Construction with Compiler Directives

Voronoi diagram construction is a common and fundamental problem in computational geometry and spatial computing. Numerous sequential and parallel algorithms for Voronoi diagram construction exists in literature. This paper presents a multi-threaded approach where we augment an existing sequential implementation of Fortune\u27s planesweep algorithm with compiler directives. The novelty of our fine-grained parallel algorithm lies in exploiting the concurrency available at each event point encountered during the algorithm. On the Intel Xeon E5 CPU, our shared-memory parallelization with OpenMP achieves around 2x speedup compared to the sequential implementation using datasets containing 2k-128k sites

Spatial Data Decomposition and Load Balancing on HPC Platforms

We are in the era of Spatial Big Data. Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity and heterogeneity. For example, OpenStreetMap data for the whole world is about 1 TB and NASA world climate datasets are about 17 TB. Spatial data volume and variety makes spatial computations both data-intensive and compute-intensive. Due to the irregular distribution of spatial data, domain decomposition becomes challenging. In this work, we present spatial data partitioning technique that takes into account spatial join cost. In addition, we present spatial join computation using Asynchronous Dynamic Load Balancing (ADLB) library. ADLB is a software library designed to help rapidly build scalable parallel programs using MPI. We evaluated the performance of ADLB-based MPI-GIS implementation. In our existing work, spatial data movement cost from ADLB server to worker MPI processes limited the scalability of MPI-GIS

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Metadata record for: HIT-COVID, a global database tracking public health interventions to COVID-19

This dataset contains key characteristics about the data described in the Data Descriptor HIT-COVID, a global database tracking public health interventions to COVID-19. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON forma