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Performance Analysis

The Jülich Supercomputing Centre has a long tradition in the development of performance tools for parallel programs. The current focus is on the automation of the performance analysis process. With the KOJAK toolset, we aimed at the development of a genericautomatic performance analysis environment for parallel programs. Performance problems are specified in terms of execution patterns that represent situations of inefficient behavior. These patterns are input for an analysis process that recoganizes and quantifies the inefficient behavior in event traces. Mechanisms that hide the complex relationships within event pattern specifications allow a simple description of complex inefficient behavior on a high level of abstraction. With the Scalasca toolset, a successor to KOJAK, the main focus is on scalability in order to support analysis of parallel applicationsrunning on today's supercomputer consisting of many thousand processor cores. The latest versions of Scalasca are based on the community-maintained instrumentation and run-time measurement infrastructure Score-P.


  • The Scalasca toolset is developed in collaboration with Laboratory for Parallel Programming of Technische Universität Darmstadt
  • VI-HPS: Virtual Institute - High Productivity Supercomputing (HGF)
  • EIC: Programming methods and performance tools for future IBM architectures
  • ECL: Performance modeling and tools for future cluster systems
  • BDEC/IESP: Big Data and Extreme Computing / International Exascale Software Project
  • EESI: European Exascale Software Initative (EU FP7)

Current research projects:

  • POP: Performance Optimisation and Productivity (EU H2020)
  • SCIPHI: Score-P and Cube extensions for Intel PHI (Intel Corporation)

Concluded research projects:

  • RAPID: Runtime Analysis of Parallel applications for Industrial software Development (Siemens AG)
  • Score-E: Scalable Tools for the Analysis and Optimization of Energy Consumption in HPC (BMBF)
  • CATWALK: A Quick Development Path for Performance Models" (DFG SPPEXA)
  • DEEP: Scalasca support for OmpSs and the DEEP architecture/Intel MIC (EU FP7)
  • Mont-Blanc: Scalasca support for OmpSs and the Mont-Blanc architecture/ARM (EU FP7)
  • LMAC: Performance Dynamics of Massively Parallel Codes (BMBF)
  • H4H: Hybrid programming for heterogeneous architectures (EU ITEA2)
  • PRIMA: Performance Refactoring on Instrumentation, Measurement and
    Analysis Technologies for Petascale Computing (US DOE)
  • HOPSA: Integration of system and application monitoring (EU RU FP7)
  • TEXT: Tool support for MPI/SMPSs programming model (EU FP7)
  • eeClust: Energy-efficient cluster computing (BMBF)
  • SILC: Scalable Performance-Analysis Infrastructure (BMBF)
  • ParMA: Parallel Programming for Multi-core Architectures (EU ITEA2)