A step towards making local and remote desktop applications interoperable with high-resolution tiled display walls

The visual output from a personal desktop application is limited to the resolution of the local desktop and display. This prevents the desktop application from utilizing the resolution provided by high-resolution tiled display walls. Additionally, most desktop applications are not designed for the distributed and parallel architecture of display walls, limiting the availability of such applications in these kinds of environments. This paper proposes the Network Accessible Compute (NAC) model, transforming personal computers into compute services for a set of display-side visualization clients. The clients request output from the compute services, which in turn start the relevant personal desktop applications and use them to produce output that can be transferred into display-side compatible formats by the NAC service. NAC services are available to the visualization clients through a live data set, which receives requests from visualization nodes, translates these to compute messages and forwards them to available compute services. Compute services return output to visualization nodes for rendering. Experiments conducted on a 28-node, 22-megapixel, display wall show that the time used to rasterize a 350-page PDF document into 550 megapixels of image tiles and display these image tiles on the display wall is 74.7 seconds (PNG) and 20.7 seconds (JPG) using a single computer with a quad-core CPU as a NAC service. When increasing this into 28 quad-core CPU computers, this time is reduced to 4.2 seconds (PNG) and 2.4 seconds (JPG). This shows that the application output from personal desktop computers can be made interoperable with high-resolution tiled display walls, with good performance and independent of the resolution of the local desktop and display.

[1]  Gilles Fedak,et al.  XtremWeb: a generic global computing system , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[2]  Joel H. Saltz,et al.  Active semantic caching to optimize multidimensional data analysis in parallel and distributed environments , 2007, Parallel Comput..

[3]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[4]  Wagner Toledo Corrêa,et al.  SPVN: a new application framework for interactive visualization of large datasets , 2007, SIGGRAPH Courses.

[5]  Michael R. Shirts,et al.  Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing. , 2003, Biopolymers.

[6]  Joel H. Saltz,et al.  Distributed processing of very large datasets with DataCutter , 2001, Parallel Comput..

[7]  Angela C. Sodan,et al.  Parallelism via Multithreaded and Multicore CPUs , 2010, Computer.

[8]  David P. Anderson,et al.  BOINC: a system for public-resource computing and storage , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[9]  Andrew E. Johnson,et al.  High-Performance Dynamic Graphics Streaming for Scalable Adaptive Graphics Environment , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[10]  William H. Duquette,et al.  RIVA: A Versatile Parallel Rendering System for Interactive Scientific Visualization , 1996, IEEE Trans. Vis. Comput. Graph..

[11]  Naga K. Govindaraju,et al.  Challenges and Opportunities in Many-Core Computing , 2008, Proceedings of the IEEE.

[12]  Joel H. Saltz,et al.  Visualization of Large Data Sets with the Active Data Repository , 2001, IEEE Computer Graphics and Applications.

[13]  Jason Leigh,et al.  Optistore: an on-demand data processing middleware for very large scale interactive visualization , 2007 .

[14]  Yong Liu,et al.  Improving the performance of VNC for high-resolution display walls , 2009, 2009 International Symposium on Collaborative Technologies and Systems.

[15]  Brian Vinter.  The Architecture of the Minimum intrusion Grid (MiG) , 2005, CPA.

[16]  Nabil Abdennadher,et al.  Towards a peer-to-peer platform for high performance computing , 2005, Eighth International Conference on High-Performance Computing in Asia-Pacific Region (HPCASIA'05).

[17]  Robert L. Grossman,et al.  TeraScope: distributed visual data mining of terascale data sets over photonic networks , 2003, Future Gener. Comput. Syst..

[18]  Otto J. Anshus,et al.  On-demand High-performance Visualization of Spatial Data on High-resolution Tiled Display Walls , 2016, IMAGAPP/IVAPP.

[19]  Daniel S. Katz,et al.  Accessing and visualizing scientific spatiotemporal data , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[20]  Luc Renambot,et al.  TeraVision: a distributed, scalable, high resolution graphics streaming system , 2004, 2004 IEEE International Conference on Cluster Computing (IEEE Cat. No.04EX935).

[21]  P. Peggy Li.  Supercomputing Visualization for Earth Science Datasets , 2002 .

[22]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[23]  Andrew A. Chien,et al.  The OptIPuter , 2003, CACM.

[24]  Dave Stainforth,et al.  Climateprediction.net: Design Principles for Publicresource Modeling Research , 2002, IASTED PDCS.