Tools for scalable performance analysis on Petascale systems

Tools are becoming increasingly important to efficiently utilize the computing power available in contemporary large scale systems. The drastic increase in the size and the complexity of systems require tools to be scalable while producing meaning full and easily digestible information that may help the user pin-point problems at scale. The goal of this tutorial is to introduce some state-of-the-art performance tools from three different organizations to a diverse audience group. Together these tools provide a broad spectrum of capabilities necessary to analyze the performance of scientific and engineering applications on a variety of large and small scale systems. These tools include: * IBM High Performance Computing Toolkit: The IBM High Performance Computing Toolkit is a suite of performance-related tools and libraries to assist in application tuning. This toolkit is an integrated environment for performance analysis of sequential and parallel applications using the MPI and OpenMP paradigms. Scientists can collect rich performance data from selected parts of an execution, digest the data at a very high level, and plan for improvements within a single unified interface. It provides a common framework for IBM's mid-range server offerings, including pSeries and eSeries servers and Blue Gene systems, on both AIX and Linux. More information cab be found here: http://domino.research.ibm.com/comm/research_projects.nsf/pages/hpct.index.html * Scalable Performance Analysis of Large-Scale Applications (SCALASCA) Toolset: Scalasca is an open-source toolset that can be used to analyze the performance behavior of parallel applications and to identify opportunities for optimization. It has been specifically designed for use on large-scale systems including BlueGene and Cray XT, but is also well-suited for small- and medium-scale HPC platforms. Scalasca supports an incremental performance-analysis procedure that integrates runtime summaries with in-depth studies of concurrent behavior via event tracing, adopting a strategy of successively refined measurement configurations. A distinctive feature is the ability to identify wait states that occur, for example, as a result of unevenly distributed workloads. Especially when trying to scale communication-intensive applications to large processor counts, such wait states can present severe challenges to achieving good performance. Scalasca is developed by the Julich Supercomputing Centre and available under the New BSD open