FACT: fast communication trace collection for parallel applications through program slicing
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Triva: Interactive 3D visualization for performance analysis of parallel applications
Future Generation Computer Systems
ScalaExtrap: trace-based communication extrapolation for spmd programs
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Communication-centric optimizations by dynamically detecting collective operations
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
ScalaExtrap: Trace-based communication extrapolation for SPMD programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
Extracting the optimal sampling frequency of applications using spectral analysis
Concurrency and Computation: Practice & Experience
Runtime detection and optimization of collective communication patterns
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
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Since processor counts in supercomputers are increasing dramatically, efficient interprocessor communication is becoming even more important for the applications that run on them. A high level, abstract understanding of an application's communication behavior would not only simplify debugging of that communication but would also support more directed performance optimization. We explore automated identification of communication patterns to provide that high level abstraction. We introduce an algorithm to extract communication patterns from MPI traces automatically. Our algorithm first finds locally repeating sequences and then iteratively grows them into global patterns. We demonstrate our technique on three realistic codes using traces from up to 128 processors. Our results show that our approach detects the underlying communication pattern within reasonable time andmemory constraints, even for large trace sizes.