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Bulk synchronous visualization

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PMAM '13: Proceedings of the 2013 International Workshop on Programming Models and Applications for Multicores and Manycores

Pages 21 - 30

https://doi.org/10.1145/2442992.2442995

Published: 23 February 2013 Publication History

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Abstract

Many visual analytics applications require computationally expensive high resolution visualizations. Large desktop displays and display walls may provide the required resolution, and current multi- and many-core processors often have the required computational resources. However, it is still challenging to write programs that can utilize high resolution displays and multi-core processors. We describe the bulk synchronous visualization (BSV) model that makes it easier to write high resolution parallel visualizations. The dataset to be visualized is decomposed into thousands of tasks that are assigned to sequential processes. These are then run in parallel by the BSV system which provides efficient process and window management. BSV takes advantage of the large DRAM size and multiple cores of current computers, and the copy-on-write and low overhead fork mechanisms provided by current operating systems. We have implemented three BSV applications and used these to identify advantages and limitations of BSV on Windows, Linux and OS X. The results demonstrate that BSV makes it easy to implement visualization applications that utilize high resolution displays and multi-core processors.

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Index Terms

Bulk synchronous visualization
1. Computing methodologies
  1. Distributed computing methodologies
    1. Distributed programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Distributed programming languages
    2. Software libraries and repositories

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Reviews

Reviewer: Jason H Moore

Visualization is becoming an increasingly critical component of research studies that generate and analyze big data in biology, economics, physics, and many other disciplines. Visualization methods are sometimes categorized as "information visualization, which focuses on methods such as heat maps for showing high-dimensional research results, and scientific visualization, which focuses on the mathematics and physics of visualizing complex objects" [1]. There is a third emerging discipline called visual analytics, which integrates data analysis and human-computer interaction (HCI) with both information and scientific visualization methods. Data analysis methods such as those from machine learning and data mining have become much more powerful in recent years. HCI tools such as touch and gesture computing make it effortless to interact with visualization systems. These technologies are complemented by inexpensive 3D televisions and immersive display walls. Visual analytics is expected to take visualization to the next level by allowing researchers to explore their data in an interactive manner, while at the same time launching analyses from the visualization. The ability to deliver visual analytics technology to researchers with big data will depend critically on the computational infrastructure that will support the visualization hardware. This paper presents a bulk synchronous visualization (BSV) method for interactive parallel computation. This is accomplished by breaking a big dataset into many small pieces that can be processed for visualization in parallel. Methods such as this will be critical if we are to deliver user-friendly visual analytics tools to big data researchers. Visualization will only be effective when it is fast and seamless. Even small delays in rendering time can greatly impact the user experience and thus limit scientific discovery. This author provides one of many new methods that will be needed to fully exploit the patterns hidden in big data. Online Computing Reviews Service

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Published In

PMAM '13: Proceedings of the 2013 International Workshop on Programming Models and Applications for Multicores and Manycores

February 2013

134 pages

ISBN:9781450319089

DOI:10.1145/2442992

Editors:
Pavan Balaji
Argonne National Laboratory
,
Minyi Guo
Shanghai Jiao Tong University, China
,
Zhiyi Huang
University of Otago, New Zealand

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: 23 February 2013

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PPoPP '13: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 23, 2013

Guangdong, Shenzhen, China

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Hybrid bulk synchronous parallelism library for clustered smp architectures

Exploiting task and data parallelism in ILUPACK's preconditioned CG solver on NUMA architectures and many-core accelerators

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Hybrid bulk synchronous parallelism library for clustered smp architectures

Exploiting task and data parallelism in ILUPACK's preconditioned CG solver on NUMA architectures and many-core accelerators

Fine-grain parallelism using multi-core, Cell/BE, and GPU Systems

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