research-article

A new multi-core pipelined architecture for executing sequential programs for parallel geospatial computing

Authors:

Simon Y. BerkovichAuthors Info & Claims

COM.Geo '10: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application

Article No.: 23, Pages 1 - 8

https://doi.org/10.1145/1823854.1823881

Published: 21 June 2010 Publication History

Abstract

Parallel programming on multi-core processors has become the industry's biggest software challenge. This paper proposes a novel parallel architecture for executing sequential programs using multi-core pipelining based on program slicing by a new memory/cache dynamic management technology. The new architecture is very suitable for processing large geospatial data in parallel without parallel programming. This paper presents a new architecture for parallel computation that addresses the problem of needing to relocate data from one memory hierarchy to another in a multi-core environment. A new memory management technology inserts a layer of abstraction between the processor and the memory hierarchy, allowing the data to stay in one place while the processor effectively migrates as tasks change. The new architecture can make full use of the pipeline and automatically partition data then schedule them onto multi-cores through the pipeline. The most important advantage of this architecture is that most existing sequential programs can be directly used with nearly no change, unlike conventional parallel programming which has to take into account scheduling, load balancing, and data distribution. The new parallel architecture can also be successfully applied to other multi-core/many-core architectures or heterogeneous systems. In this paper, the design of the new multi-core architecture is described in detail. The time complexity and performance analysis are discussed in depth. The experimental results and performance comparison with existing multi-core architectures demonstrate the effectiveness, flexibility, and diversity of the new architecture, in particular, for large geospatial data parallel processing with the examples of Digital Elevation Model (DEM) generation from Light Detection And Ranging (LIDAR) dataset.

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Digital Library

Cited By

Liao DYammahi MAlhudhaif AAlsaby FAlGemili UBerkoich S(2016)A Qualitatively Different Principle for the Organization of Big Data ProcessingBig Data10.1201/b19694-9(171-198)Online publication date: 28-Apr-2016
https://doi.org/10.1201/b19694-9
Mani GBari NLiao DBerkovich S(2014)Organization of Knowledge Extraction from Big Data SystemsProceedings of the 2014 Fifth International Conference on Computing for Geospatial Research and Application10.1109/COM.Geo.2014.6(63-69)Online publication date: 4-Aug-2014
https://dl.acm.org/doi/10.1109/COM.Geo.2014.6
Alhudhaif AYan TBerkovich S(2014)On the Organization of Cluster Voting with Massive Distributed StreamsProceedings of the 2014 Fifth International Conference on Computing for Geospatial Research and Application10.1109/COM.Geo.2014.3(55-62)Online publication date: 4-Aug-2014
https://dl.acm.org/doi/10.1109/COM.Geo.2014.3
Show More Cited By

Index Terms

A new multi-core pipelined architecture for executing sequential programs for parallel geospatial computing
1. Computer systems organization
  1. Architectures
    1. Other architectures
    2. Parallel architectures
      1. Multiple instruction, multiple data

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cover image ACM Other conferences

COM.Geo '10: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application

June 2010

274 pages

ISBN:9781450300315

DOI:10.1145/1823854

General Chair:
Lindi Liao

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 June 2010

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COM.Geo '10

COM.Geo '10: 1st International Conference on Computing for Geospatial Research and Application

June 21 - 23, 2010

Washington, D.C., USA

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Cited By

Liao DYammahi MAlhudhaif AAlsaby FAlGemili UBerkoich S(2016)A Qualitatively Different Principle for the Organization of Big Data ProcessingBig Data10.1201/b19694-9(171-198)Online publication date: 28-Apr-2016
https://doi.org/10.1201/b19694-9
Mani GBari NLiao DBerkovich S(2014)Organization of Knowledge Extraction from Big Data SystemsProceedings of the 2014 Fifth International Conference on Computing for Geospatial Research and Application10.1109/COM.Geo.2014.6(63-69)Online publication date: 4-Aug-2014
https://dl.acm.org/doi/10.1109/COM.Geo.2014.6
Alhudhaif AYan TBerkovich S(2014)On the Organization of Cluster Voting with Massive Distributed StreamsProceedings of the 2014 Fifth International Conference on Computing for Geospatial Research and Application10.1109/COM.Geo.2014.3(55-62)Online publication date: 4-Aug-2014
https://dl.acm.org/doi/10.1109/COM.Geo.2014.3
Berkovich SLiao DLiao L(2012)On clusterization of "big data" streamsProceedings of the 3rd International Conference on Computing for Geospatial Research and Applications10.1145/2345316.2345347(1-6)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.1145/2345316.2345347

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