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TorusVis ND : Unraveling High- Dimensional Torus Networks for Network Traffic Visualizations Shenghui Cheng, Pradipta De, Shaofeng H.-C. Jiang* and Klaus.

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Presentation on theme: "TorusVis ND : Unraveling High- Dimensional Torus Networks for Network Traffic Visualizations Shenghui Cheng, Pradipta De, Shaofeng H.-C. Jiang* and Klaus."— Presentation transcript:

1 TorusVis ND : Unraveling High- Dimensional Torus Networks for Network Traffic Visualizations Shenghui Cheng, Pradipta De, Shaofeng H.-C. Jiang* and Klaus Mueller Visual Analytics and Imaging Lab Computer Science Department Stony Brook University and SUNY Korea * Computer Science Dept. The University of Hong Kong

2 Motivation Measuring performance on large scale computers is important Visualizing these measurements can boost understanding Exploration is best done in the context of the network Complexity of the interconnections makes exploration difficult

3 Obstacles Would like to visually explore  processor state and occupancy  communications between processors  do this over time Would be fairly easy for a 2D mesh network Unfortunately we have a mesh with D>2

4 How to Visualize an ND Mesh Provide a large number of projections  this can be overwhelming to the user Create an optimized 2D layout of the nodes  could use MDS  but the interconnections would clutter the display Need a display that separates the nodes from the links

5 Our Strategy Basic concept  form a circle  equally space nodes on it  interconnect in circle interior Problem is line clutter in interior Overcome with edge bundling

6 Mapping the Nodes onto the Circle Need a node serialization scheme Naïve serialization  sequential numbering/indexing  increment node indices in modulus order  has an uneven degree of locality How can locality be improved?  space-filling curve  Hilbert curve  proven to have the best locality  generate for any D  self-similar fractal structure

7 Locality Locality metric  sequential L = 1,822  Hilbert L = 1,414 (20% better)  for N= 4 5 = Average distance of neighbors sequential Hilbert

8 Zooming In Looking at a local group of 6 processors  sequential and Hilbert indexing clearly expressed  Hilbert appears more local than sequential sequential Hilbert

9 Interaction Key to deal with data deluge  number of processors  number if interconnections  types of performance metrics  time We allow user to select  processor groups of interest  time slices of interest  performance metric selector not yet implemented

10 Node Selection Interface Based on parallel coordinates  all nodes  selecting a single node and showing its links in context of others

11 Node Filtering and Bracketing Isolate a group of processors in a certain address range Certain processors and links might be more important  for example: a larger number of messages in a certain time interval  other importance metric

12 Use Case Simulated the wake-up of a processor network  random processor wakes up and sends a message to a random neighbor  neighbor sends the message to its won random neighbor  all processors awake after half the simulation time First visualization  track a single message over time  lighter color is older

13 heavier traffic more emphasized

14 Time Slice Selection Features  width of stream maps to number of messages in a time interval Observations  each node has times of no messages sent, but also burst periods  there are also quiet and burst times overall time selected time slice

15 Selected Time Interval Time slicer, node selector and network display are tightly coupled links with messages links with and without messages

16 Conclusions TorusVis ND follows one of the classic paradigms of information visualization  overview, filter, and detail on demand  the “Visual Information Seeking Mantra”  puts the user into the loop of steering the data exploration  operations like selection, filtering, and brushing Brushing tools can be used in two ways  selection and filtering  visualization of network performance  allow users to interact with large and complex data TorusVis ND not restricted to torus networks  any highly-connected network can benefit in principle

17 Future Work Make interface more scalable  introduce multi-resolution capabilities into the network display to allow it to handle larger numbers of network nodes  introduce multi-perspective lenses to the network display interior to allow users to zoom into multiple areas of interest. Work with domain experts and real data  this will truly optimize our framework and system  make it more practical  inspire new work

18 Questions? Funding:  NSF grant IIS  MSIP (Ministry of Science, ICT and Future Planning), Korea, under the "IT Consilience Creative Program (ITCCP)" (NIPA-2013-H ) supervised by NIPA (National IT Industry Promotion Agency)


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