Vampir 10.3

Vampir - Performance Optimization

Latest news

  • 12
    Nov
    2023

    Vampir meets SC23

    At booth 1553 at the SC23 conference in Denver, Colorado, US we will be showing new developments ...

Latest release

  • 05
    Jun
    2023

    Vampir 10.3.0 released

    We are pleased to announce the feature release of Vampir 10.3.0.

    This major release introduces ...


  1. Master Timeline Vampir 10.3: Master Timeline
  2. Find function in Master Timeline Search for MPI_Bcast in the Master Timeline
  3. Function summary Vampir 10.3: Function Summary
  4. Master Timeline highlighting 1 Master Timeline highlighting areas with low FLOP rate
  5. Master Timeline highlighting 2 Master Timeline highlighting areas with high FLOP rate
  6. Custom metrics editor showing the construction of a custom Wait Time metric. The metric is defined by the addition of the duration of MPI Irecv and MPI Wait functions. Vampir 10.3: Custom metrics editor
  7. Kiviat Chart Vampir 10.3: A kiviat chart mode in the process summary chart


Vampir 10.3 provides an easy-to-use framework that enables developers to quickly display and analyze arbitrary program behavior at any level of detail. The tool suite implements optimized event analysis algorithms and customizable displays that enable fast and interactive rendering of very complex performance monitoring data.

The combined handling and visualization of instrumented and sampled event traces generated by Score-P enables an outstanding performance analysis capability of highly-parallel applications. Current developments also include the analysis of memory and I/O behavior that often impacts an application's performance.

Score-P is the primary code instrumentation and run-time measurement framework for Vampir and supports various instrumentation methods, including instrumentation at source level and at compile/link time.

Vampir and Score-P provide a performance tool framework with special focus on highly-parallel applications. Performance data is collected from multi-process (MPI, SHMEM), thread-parallel (OpenMP, Pthreads), as well as accelerator-based paradigms (CUDA, HIP, OpenCL, OpenACC).