The Rails Toolkit &amp;#150; Enabling End-System Topology-Aware High End Computing

Vishwanath, Venkatram; Leigh, Jason; Nam, Sungwon; Renambot, Luc; Shimizu, Tatsuya; Takahashi, Haruhisa; Takizawa, Makoto; Kamatani, Osamu

doi:10.1109/escience.2008.83

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…TLAN is considered as "Terabit/s-scalable end-to-end parallel networking architecture for high-end scientific applications." TLAN also needs to cover the end-host configuration which supports multi-rail, multi-lane, parallel processing [5], and the network interfaces. In a simple case, multi-lanes are set up as parallel optical wavelength paths between end-hosts.…”

Section: A What Is Tlan?mentioning

confidence: 99%

Terabit/s-scalable end-to-end parallel networking architecture (TLAN) based on virtual optical resource control

Kamatani

Takahashi

Takizawa

et al. 2010

2010 IEEE Photonics Society Winter Topicals Meeting Series (WTM)

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Section: A What Is Tlan?mentioning

confidence: 99%

Terabit/s-scalable end-to-end parallel networking architecture (TLAN) based on virtual optical resource control

Kamatani

Takahashi

Takizawa

et al. 2010

2010 IEEE Photonics Society Winter Topicals Meeting Series (WTM)

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“…LambdaRAM employs novel latency mitigation heuristics based on the access patterns of an application, including presending, a push-based mechanism, prefetching, a pull-based mechanism, and hybrid combinations of them, proactively fetching data before an application needs it. It uses highperformance data transfer protocols, including Celeritas (Vishwanath, 2008b), designed to fully exploit the large available bandwidth of wide-area optical networks. LambdaRAM employs multi-dimensional distributed data structures, including multi-grids and octrees, to manage multidimensional datasets.…”

Section: Case Study : Lambdaram a Distributed Data Cachementioning

confidence: 99%

Visualization of Large-Scale Distributed Data

Leigh

Johnson

Renambot

et al.

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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An effective visualization is best achieved through the creation of a proper representation of data and the interactive manipulation and querying of the visualization. Large-scale data visualization is particularly challenging because the size of the data is several orders of magnitude larger than what can be managed on an average desktop computer. Large-scale data visualization therefore requires the use of distributed computing. By leveraging the widespread expansion of the Internet and other national and international high-speed network infrastructure such as the National LambdaRail, Internet-2, and the Global Lambda Integrated Facility, data and service providers began to migrate toward a model of widespread distribution of resources. This chapter introduces different instantiations of the visualization pipeline and the historic motivation for their creation. The authors examine individual components of the pipeline in detail to understand the technical challenges that must be solved in order to ensure continued scalability. They discuss distributed data management issues that are specifically relevant to large-scale visualization. They also introduce key data rendering techniques and explain through case studies approaches for scaling them by leveraging distributed computing. Lastly they describe advanced display technologies that are now considered the “lenses” for examining large-scale data.

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