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Vampir
| The Vampir suite of tools offers scalable event analysis through a nice GUI which enables a fast and interactive rendering of very complex performance data. The suite consists of Vampirtrace, Vampir and Vampirserver). Ultra large data volumes can be analyzed with a parallel version of Vampirserver, loading and analysing the data on the compute nodes with the GUI of Vampir attaching to it.
Vampir is based on standard QT 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. |
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Usage
In order to use Vampir, You first need to generate a trace of Your application, preferrably using VampirTrace. This trace consists of a file for each MPI process and an OTF-file (Open Trace Format) describing the other files. Using environment variables (starting with VT_):
Please note, that being an MPI-tool, VampirTrace is dependent not only on the compiler being used, but also on the MPI-version.
You may recompile, specifying the amount of instrumentation You want to include, e.g. -vt:mpi for MPI instrumentation, -vt:compinst for compiler-based function instrumentation:
To analyse Your application, for small traces (<16 processes, only few GB of trace data), use vampir standalone
For large-scale traces (up to many thousand MPI processes), use the parallel VampirServer (on compute nodes allocated through the queuing system), and attach to it using vampir:
qsub: waiting for job 297851.intern2 to start qsub: job 297851.intern2 ready ... module load performance/vampirserver mpirun -np 256 vampirserver-core VampirServer 7.5.0 Licensed to HLRS Running 255 analysis processes... (abort with Ctrl-C or vampirserver-shutdown)
Server listens on: n010802:30000then on the head-node use the Vampir-GUI to "Remote open" the same file by attaching to the heavy-weight, compute nodes:
Please note, that vampirserver-core is memory-bound and may work best if started with only one MPI process per node, or one per socket, e.g. use Open MPI's option to mpirun -bysocket.