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Visual Analysis of Inter-Process Communication for Large-Scale Parallel Computing
November/December 2009 (vol. 15 no. 6)
pp. 1129-1136
ASCII Text
x 
 
Chris Muelder, Francois Gygi, Kwan-Liu Ma, "Visual Analysis of Inter-Process Communication for Large-Scale Parallel Computing," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1129-1136, November/December, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2009.196,
author = {Chris Muelder and Francois Gygi and Kwan-Liu Ma},
title = {Visual Analysis of Inter-Process Communication for Large-Scale Parallel Computing},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {15},
number = {6},
issn = {1077-2626},
year = {2009},
pages = {1129-1136},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.196},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Visual Analysis of Inter-Process Communication for Large-Scale Parallel Computing
IS - 6
SN - 1077-2626
SP1129
EP1136
EPD - 1129-1136
A1 - Chris Muelder,
A1 - Francois Gygi,
A1 - Kwan-Liu Ma,
PY - 2009
KW - Information Visualization
KW - MPI Profiling
KW - Scalability
VL - 15
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Chris Muelder, University of California, Davis
Francois Gygi, University of California, Davis
Kwan-Liu Ma, University of California, Davis
In serial computation, program profiling is often helpful for optimization of key sections of code. When moving to parallel computation, not only does the code execution need to be considered but also communication between the different processes which can induce delays that are detrimental to performance. As the number of processes increases, so does the impact of the communication delays on performance. For large-scale parallel applications, it is critical to understand how the communication impacts performance in order to make the code more efficient. There are several tools available for visualizing program execution and communications on parallel systems. These tools generally provide either views which statistically summarize the entire program execution or process-centric views. However, process-centric visualizations do not scale well as the number of processes gets very large. In particular, the most common representation of parallel processes is a Gantt char t with a row for each process. As the number of processes increases, these charts can become difficult to work with and can even exceed screen resolution. We propose a new visualization approach that affords more scalability and then demonstrate it on systems running with up to 16,384 processes.

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Index Terms:
Information Visualization, MPI Profiling, Scalability
Citation:
Chris Muelder, Francois Gygi, Kwan-Liu Ma, "Visual Analysis of Inter-Process Communication for Large-Scale Parallel Computing," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1129-1136, Nov.-Dec. 2009, doi:10.1109/TVCG.2009.196
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