Scalable Communication Trace Compression

Characterizing the communication behavior of parallel programs through tracing can help understand an application's characteristics, model its performance, and predict behavior on future systems. However, lossless communication traces can get prohibitively large, causing programmers to resort t...

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Bibliographic Details
Published in:2010 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing pp. 408 - 417
Main Authors: Krishnamoorthy, Sriram, Agarwal, Khushbu
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2010
Subjects:
Cloud computing
communication trace compression
Compression algorithms
Concurrent computing
Grid computing
Laboratories
Mathematical model
Mathematics
mpi
performance analysis
Predictive models
Programming profession
Runtime
ISBN:1424469872, 9781424469871
Online Access:Get full text
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Summary:Characterizing the communication behavior of parallel programs through tracing can help understand an application's characteristics, model its performance, and predict behavior on future systems. However, lossless communication traces can get prohibitively large, causing programmers to resort to variety of other techniques. In this paper, we present a novel approach to lossless communication trace compression. We augment the sequitur compression algorithm to employ it in communication trace compression of parallel programs. We present optimizations to reduce the memory overhead, reduce size of the trace files generated, and enable compression across multiple processes in a parallel program. The evaluation shows improved compression and reduced overhead over other approaches, with up to 3 orders of magnitude improvement for the NAS MG benchmark. We also observe that, unlike existing schemes, the trace files sizes and the memory overhead incurred are less sensitive to, if not independent of, the problem size for the NAS benchmarks.
ISBN:1424469872
9781424469871
DOI:10.1109/CCGRID.2010.111