Vampir provides an easy to use analysis framework which enables developers to quickly display program behavior at any level of detail:
- It converts performance data obtained from a program into different performance views.
- It supports navigation and zooming within these displays.
- It helps to identify inefficient parts of code.
- It leads to more efficient programs.
Vampir implements optimized event analysis algorithms and customizable displays which enables a fast and interactive rendering of very complex performance monitoring data. Ultra large data volumes can be analyzed with a parallel version of Vampir which is available on request.
Vampir is based on standard X-Windows and works on desktop Unix workstations as well as on parallel production systems. The program is available for nearly all platforms like Linux-based PCs and Clusters, IBM, SGI, SUN, NEC, HP, and Apple.
Vampir is available in three editions to meet the needs of different business sizes and application development goals. The editions provide different feature sets to support application performance optimization in workstation-type scenarios as well as in extreme scale scenarios.
Scalable Trace Analysis
Performance data originating from large-scale parallel applications can easily reach volumes in the order of hundreds of gigabytes. Interactive visualization and analysis of such data volumes requires a parallel and highly scalable analysis engine. The Vampir tool chain addresses this issue with a parallel analysis component called VampirServer. Developers can interact with VampirServer from their desktop using the Vampir GUI as a performance data browser. Download the brochure to learn more.
Hardware accelerators are becoming increasingly important in the HPC community. Our performance monitor VampirTrace provides CUDA and OpenCL support to give detailed insight into the runtime behavior of hardware accelerators. This enables extensive performance analysis and optimization of hybrid programs. Download the brochure to learn more.
Vampir's visual performance analysis provides a convient way to gain insight into parallel program behavior. To generate its input event tracies, it relies on the powerful open source Score-P toolkit for code instrumentation and runtime monitoring. Download the brochure to learn more.
Monitoring with Score-P
Analysis of event data with Vampir is an effective approach to optimizing the performance of parallel applications. Collecting the performance data in a scalable and efficient fashion is a highly challenging task. With the new Score-P scalable performance monitor, we support a convenient measurement infrastructure for recording fine-grained performance events with special focus on parallel applications. Download the brochure to learn more.