Visualizing large-scale parallel communication traces using a particle animation technique

Question

1. What have the authors contributed in "Visualizing large-scale parallel communication traces using a particle animation technique" ?

2. What have the authors stated for future works in "Visualizing large-scale parallel communication traces using a particle animation technique" ?

3. What is the background of the animation window?

4. What is the term used to describe the MPI virtual topology?

Accepted Answer

The authors introduce an animation-based interactive visualization technique for the analysis of communication patterns occurring in parallel application execution.. The authors demonstrate the effectiveness of their approach scaling up to 16 thousand processes using a series of trace data sets of ScaLAPACK matrix operations functions.

Accepted Answer

Thus, the authors plan to imple- c© 2013 The Author ( s ) c© 2013 The Eurographics Association and Blackwell Publishing Ltd. ment zooming or filtering features in the current visualization.. Further studies can also instrument and trace the computational activities of other libraries or actual simulations to evaluate their performance.

Accepted Answer

The background of the animation window employs a grid, with the horizontal lines delimiting event durations on a logarithmic scale and the vertical lines serving to separate the segments, which makes clarifies which segment a particle belongs to.

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The authors use the term “process grid" to describe the MPI virtual topology used by the matrix operations under study, which is the Cartesian (grid) topology.

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The Jumpshot toolkits are also responsible for creating the MPE tracing libraries and some scalable binary trace formats, such as CLOG2 and SLOG2 [CGL08].

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When applied to parallel system performance analysis or parallel software visualizations, animation is often applied to existing visualizations, such as Gantt charts or communication matrices, to show changes in the system during execution.

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The authors chose to analyze a set of ScaLAPACK distributed matrix operations for their case studies primarily because it is a building block for many scientific simulations.

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The number of processes per segment is calculated based on the total width available to the animation window and on a specified segment minimum length.

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Another fact that became apparent is that MPI function calls tend to have a very wide distribution of durations, for which a logarithmic scale is only partially appropriate.

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Studies have shown that the upper limit on the number of colors readily distinguishable is around 25 [GA10], so the authors also employ a strategy to assign similar colors to operations which are similar in nature or which can be easily associated, such as the MPI_Comm set of functions or the blocking and non-blocking variants of MPI Send and Receive.

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In the animation window, particles travel upwards such that their position on the y axis always corresponds to the amount of time their associated events have been in-flight.

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Other visualizations [SML∗12, LLB∗12] focus on the communication topology, and hence use network representations such as node-link diagrams or matrices.

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CLOG2 files store start and stop times of MPI events as separate entries, which the authors match up when the authors convert the data to their database format.

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Their method is not designed to show trends or rates of change, but to support the tasks of comprehending the complex communication required by large parallel applications and identifying slowdowns due to communication overhead.

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The event’s type is the actual MPI function called (such as MPI_Send or MPI_Broadcast). • A process is an initiator of MPI function calls.

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The label under each segment indicates the ID of the first process in the set and since these IDs are in sequential order, it is very easy to deduce how many processes correspond to each segment.

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At any point, the user can also manually clear the background buffer or the currently fading particles, allowing for the reset of the history if it gets too cluttered.

Accepted Answer

A study by Robertson et. al [RFF∗08] indicates that animation is particularly ill-suited to trend visualization and has found that participants interpreted trend data with higher accuracy when they were shown static depictions of the data rather than animations.

Visualizing large-scale parallel communication traces using a particle animation technique

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Most frequently asked questions

1. What have the authors contributed in "Visualizing large-scale parallel communication traces using a particle animation technique" ?

2. What have the authors stated for future works in "Visualizing large-scale parallel communication traces using a particle animation technique" ?

3. What is the background of the animation window?

4. What is the term used to describe the MPI virtual topology?

5. What are the other tools responsible for creating the MPE tracing libraries?

6. What is the purpose of animation in parallel systems?

7. Why did the authors choose to analyze a set of distributed matrix operations for their case studies?

8. How many processes are calculated in the animation window?

9. What is the important fact that became apparent in the example?

10. What is the upper limit on the number of colors readily distinguishable?

11. What is the simplest way to describe the animation window?

12. What are other visualizations for parallel tracing?

13. What format is used to store start and stop times of MPI events?

14. Why is the method designed to show trends or rates of change?

15. What is the type of MPI function called?

16. How many processes correspond to each segment?

17. What is the way to clear the background buffer?

18. What is the way to visualize a trend?

Figures

Citations

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A Visual Analytics System for Optimizing Communications in Massively Parallel Applications

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References

Animation: can it facilitate?

The Tau Parallel Performance System

Visualizing the performance of parallel programs

Visualizing the performance of parallel programs

Effectiveness of Animation in Trend Visualization

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