Through the availability of increasingly powerful computers
with increasing amounts of internal and external memory, it is
possible to investigate incredibly complex dynamics by means of
ever more realistic simulations. However, this brings with it
vast amounts of data . To analyze these data it is imperative to
have software tools which can visualize these multi-dimensional
data sets. Comparing this with experiment and theory it becomes
clear that visualization of scientific data is useful yet
difficult. For complicated, time-dependent simulations, the
running of the simulation may involve the calculation of many
time steps, which requires a substantial amount of CPU time , and
memory resources are still limited, one cannot save the results
of every time step. Hence, it will be necessary to visualize and
store the results selectively in `real time' so that we do not
have to recompute the dynamics if we want to see the same scene
again. `Real time' means that the selected time step will be
visualized as soon as it has been calculated.
The main reasons for scientific visualization are the following
ones : it will compress a lot of data into one picture (data
browsing), it can reveal correlations between different
quantities both in space and time, it can furnish new space-like
structures beside the ones which are already known from previous
calculations, and it opens up the possibility to view the data
selectively and interactively in `real time'. By following the
formation and the deformation as well as the motions of these
structures in time, one will gain insight into the complicated
dynamics. As was mentioned before, we also want to integrate our
simulation codes into a visualization environment in order to
analyze the data 'real time' and to by-pass the need to store
every intermediate result for later analysis. This is possible by
means of processing in which the simulation is
distributed over a set of high-performance computers and the
actual visualization is done on a graphical distributive workstation.
It is also very useful to have the possibility to interactively
change the simulation parameters and immediately see the effect
of this change through the new data. This process is called computational
steering and it will increase the effective use of CPU time.
