Vampir meets SC18
Vampir comes to SC18 in Dallas! Our performance visualizer for parallel programs provides graphical visualization of massively parallel applications. Learn how we walk the road to exascale at our booth #827 from November 12 to November 15. Additionally, you can engage with our experts in various activities of the SC18 technical program. We showcase the latest version of our tool suite and introduce a sampling technology that drastically simplifies performance optimization.
At SC18 we unveil Vampir 9.6 that provides new performance charts, customizable performance metrics, extended grouping and filtering, and new support for sampling traces.
Today, 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. Event and sampling-based statistics or counter metrics and a set of continuously extended and improved interactive visualizations in Vampir allow a detailed understanding of the complex application behavior. Current developments also include the analysis of memory and I/O behavior that often impacts an application's performance. In the near future, Vampir will introduce support for the in-situ analysis of running applications.
Vampir Website
Score-P is the primary code instrumentation and run-time measurement framework for Vampir 9, and also works natively with Scalasca, TAU, and Periscope.
It supports an extensive set of events such as function and library calls, communication events, and hardware counters. Score-P supports various instrumentation methods, including instrumentation at source level and at compile/link time. Recently, experimental support for sampling has been added. 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, OpenCL, OpenACC).
Score-P Website
At SC18 we unveil Vampir 9.6 that provides new performance charts, customizable performance metrics, extended grouping and filtering, and new support for sampling traces.
Today, 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. Event and sampling-based statistics or counter metrics and a set of continuously extended and improved interactive visualizations in Vampir allow a detailed understanding of the complex application behavior. Current developments also include the analysis of memory and I/O behavior that often impacts an application's performance. In the near future, Vampir will introduce support for the in-situ analysis of running applications.
Vampir Website
Score-P is the primary code instrumentation and run-time measurement framework for Vampir 9, and also works natively with Scalasca, TAU, and Periscope.
It supports an extensive set of events such as function and library calls, communication events, and hardware counters. Score-P supports various instrumentation methods, including instrumentation at source level and at compile/link time. Recently, experimental support for sampling has been added. 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, OpenCL, OpenACC).
Score-P Website
Additional information material
Vampir
Vampir
Vampir Editions
Vampir Scalable
MonitoringOur events on SuperComputing 2018
| Time | Room | Type | Title |
|---|---|---|---|
| Mon, 12th November 09:00 AM - 05:30 PM | D221 | Workshop | Ninth Annual Workshop for the Energy Efficient HPC Working Group (EE HPC WG) |
| Tue, 13th November 08:30 AM - 05:00 PM | C2/3/4 Ballroom | Poster | Top-Down Performance Analysis of HPC Workflows |
| Tue, 13th November 05:15 PM - 07:00 PM | C2/3/4 Ballroom | Poster Reception | Top-Down Performance Analysis of HPC Workflows |
