Automating computational experiments data post-processing

• D.V. Leontiev,IAC PFEBRAS Vladivostok. Russia

• Kharitonov D.I.

  D.I. Kharitonov. IAC PFEBRAS Vladivostok. Russia

• Odyakova D.S.

  D.S. Odyakova. IAC PFEBRAS. Vladivostok. Russia

• Parahin R.V.

  R.V. Parakhin IAC PFEBRAS. Vladivostok. Russia

This article considers the principles of the event control system for processing data from computational  experiments.  An  approach  to  construct  a  data  processing  models  of  a computational experiments is considered. To make models the Petri nets are used. The
model  of  computational  experiment  consists  of  computational  and  control  processes models. The models are built separately. The computational process model is built in a two  stages.  On  the  first  stage  the  user  generates  the  event  tree  of  computational  ex-
periment. On the second stage the computational process model is automatically built from the event tree. The model of the control process is built from a reaction patterns. The following three reaction patterns are developed: reaction on a previous event, reac-
tion on each N-th event, reaction on a next event. The reaction pattern is configured on the  triggered  event.  The  approach  allows  users  with  minimal  skills  to  make  the  data processing models. The architecture of the event control subsystem of a computational
experiment is considered. A description of the tools used (Slurm and Audit), which are the basis for the functioning of the event management system, is given. Event control is performed  using  the  Audit  subsystem,  which  collects  events  and  sends  them  to  the
processing node. A control process is located on the processing node, which track events on the model and starts the execution of the corresponding reactions. A description of the starting process computational experiment with event control is given. The sequence
of events convolution algorithm is described, which is designed to search for inconsistencies between model and a real computational experiment. The main feature of the developed approach is that there is no need to reprogram an original computational task.  
Keywords: high performance computing, big data processing, multiprocessor computing systems, scientific data visualization, Petri nets.