A Study of MPI Performance Analysis Tools on Blue Gene/L

Applications on todays massively parallel supercom-puters rely on performance analysis tools to guide them toward scalable performance on thousands of proces-sors. However, conventional tools for parallel perfor-mance 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 anal-ysis 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 disad-vantages for the tools in the two categories and will be helpful in the future performance tools design.