Parallel ocean general circulation modeling
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Predictive performance and scalability modeling of a large-scale application
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
An overview of the BlueGene/L Supercomputer
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Blue Gene/L, a System-On-A-Chip
CLUSTER '02 Proceedings of the IEEE International Conference on Cluster Computing
Initial Design of a Test Suite for Automatic Performance Analysis Tools
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Advances in the TAU performance system
Performance analysis and grid computing
An API for Runtime Code Patching
International Journal of High Performance Computing Applications
Scaling physics and material science applications on a massively parallel Blue Gene/L system
Proceedings of the 19th annual international conference on Supercomputing
Blue Gene/L torus interconnection network
IBM Journal of Research and Development
Design and implementation of message-passing services for the Blue Gene/L supercomputer
IBM Journal of Research and Development
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Applications on todays massively parallel supercomputers rely on performance analysis tools to guide them toward scalable performance on thousands of processors. However, conventional tools for parallel performance analysis have serious problems due to the large data volume that may be required. In this paper, we discuss the scalability issue for MPI performance analysis on Blue Gene/L, the worlds fastest supercomputing platform. We present an experimental study of existing MPI performance tools that were ported to BG/L from other platforms. These tools can be classified into two categories: profiling tools that collect timing summaries, and tracing tools that collect a sequence of time-stamped events. Profiling tools produce small data volumes and can scale well, but tracing tools tend to scale poorly. The experimental study discusses the advantages and disadvantages for the tools in the two categories and will be helpful in the future performance tools design.