Preserving time in large-scale communication traces
Proceedings of the 22nd annual international conference on Supercomputing
ScalaTrace: Scalable compression and replay of communication traces for high-performance computing
Journal of Parallel and Distributed Computing
Recording the control flow of parallel applications to determine iterative and phase-based behavior
Future Generation Computer Systems
Space-efficient time-series call-path profiling of parallel applications
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
A parallel trace-data interface for scalable performance analysis
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Visualization of repetitive patterns in event traces
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Automatic generation of executable communication specifications from parallel applications
Proceedings of the international conference on Supercomputing
Scalable fine-grained call path tracing
Proceedings of the international conference on Supercomputing
Scalable parallel trace-based performance analysis
EuroPVM/MPI'06 Proceedings of the 13th European PVM/MPI User's Group conference on Recent advances in parallel virtual machine and message passing interface
Auto-generation of communication benchmark traces
ACM SIGMETRICS Performance Evaluation Review
Elastic and scalable tracing and accurate replay of non-deterministic events
Proceedings of the 27th international ACM conference on International conference on supercomputing
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Compressed Complete Call Graphs (cCCGs) are a newly developed memory data structure for event based program traces. The most important advantage over linear lists or arrays traditionally used is the ability to apply lossy or lossless data compression. The compression scheme is completely transparent with respect to read access, decompression is not required. This approach is a new way to cope with todays challenges when analyzing enormous amounts of trace data. The article focuses on CCG construction and compression, querying and evaluation are briefly covered.