Chromium

Humphreys, Greg; Houston, Michael J.; Ng, Ren; Frank, Randall; Ahern, Sean; Kirchner, P. D.; Klosowski, James T.

doi:10.1145/566654.566639

Cited by 255 publications

(212 citation statements)

References 24 publications

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“…Tasks will be accomplished in multiple passes through a highly parallel stream processing machine with certain limitations on what instructions can be performed on each item at each pixel in each pass. See [132] for an example and [123] for stream-related results. Generic graphics hardware may not be suitable for processing data streams coming in at a high rate, but stream algorithms may find applications in using graphics hardware as a computing platform for solving problems.…”

Section: Problem 24mentioning

confidence: 99%

Data Streams: Algorithms and Applications

Muthukrishnan

2005

FNT in Theoretical Computer Science

768

541

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In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of [175].

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Section: Problem 24mentioning

confidence: 99%

Data Streams: Algorithms and Applications

Muthukrishnan

2005

FNT in Theoretical Computer Science

768

541

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“…When a client's status is changed, consequently, the client node will send the changes to the server making the others clients fetch these new data modified by any client. OptiPlanet [31], Equalizer [7], Chromium [14] and CGLX [6] are examples of interactive applications to be executed in a cluster display wall environment. Non-interactive applications do not usually allow any changes by the user, although, in the case that the user could make some, these would not affect either the server or the other clients.…”

Section: Background Of Cluster Visualization Systemsmentioning

confidence: 99%

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Arroyo

Giné

Roig

et al. 2015

Multimed Tools Appl

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Nowadays, clusters of interconnected workstations have become a common solution for powering large composite displays, or "cluster display walls", to visualize high resolution images. Our paper is focused on analyzing a specific cluster display wall developed by Google, named Liquid Galaxy, made up of heterogeneous commodity hardware with different degrees of heterogeneity, running master-slave (Google Earth) and clientserver (Quake III Arena) multimedia applications. With this in mind, we define and test different scenarios, representing the behavior of many kinds of users. Our results show that the CPU, memory and network are good enough to execute the client-server application, while, depending on the user behavior, the external network constitutes the bottleneck of the system in Google Earth. So, the master-slave application has focused our attention. Likewise, in order to analyze the users' point of view when interacting with Google Earth in the Liquid Galaxy, we define a new metric, named Visualization Rate (VR), which enables a relationship to be established between the user experience and the platform performance. In order to set the minimum acceptable value of the VR parameter according to users perception, we carried out different tests with real users. Then, this minimum threshold was compared with the VR value obtained from the automated benchmarking performed afterwards on clusters with different heterogeneity degrees. Finally, we analyzed the VR trend when the Liquid Galaxy is scaled from 3 up to 8 nodes in both the homogeneous and heterogeneous architectures to study the scalability of the system.

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“…Chromium is based on WireGL [9] and provides a scalable display technology. The latest Chromium paper [10] reports on the implementation of components (stream processing units, or SPUs) that can be used in sort-last parallel graphics applications. More on the sort-last parallel rendering approach can be found in [13] and [23].…”

Section: Introductionmentioning

confidence: 99%

Application of interactive parallel visualization for commodity-based clusters using visualization APIs

Tomov

Bennett

McGuigan

et al. 2004

Computers & Graphics

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We present an efficient and inexpensive to develop application for interactive highperformance parallel visualization. We extend popular APIs such as Open Inventor and VTK to support commodity-based cluster visualization. Our implementation follows a standard master/slave concept: the general idea is to have a "Master" node, which will intercept a sequential graphical user interface (GUI) and broadcast it to the "Slave" nodes. The interactions between the nodes are implemented using MPI. The parallel remote rendering uses Chromium. This paper is mainly the report of our implementation experiences. We present in detail the proposed model and key aspects of its implementation. Also, we present performance measurements, we benchmark and quantitatively demonstrate the dependence of the visualization speed on the data size and the network bandwidth, and we identify the singularities and draw conclusions on Chromium's sort-first rendering architecture. The most original part of this work is the combined use of Open Inventor and Chromium.

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Chromium

Cited by 255 publications

References 24 publications

Data Streams: Algorithms and Applications

Data Streams: Algorithms and Applications

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Application of interactive parallel visualization for commodity-based clusters using visualization APIs

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