Using  extremely large number of processing elements in computing systems leads to unexpected phenomena, such as different efficiencies of the same system for different tasks, that cannot be explained in the frame of the classical computing paradigm. The introduced simple non-technical model enables to set up a frame and formalism  needed to explain the unexpected experiences around supercomputing.  The paper shows that the degradation of the efficiency of the parallelized sequential system is a natural consequence of the  computing paradigm, rather than an engineering imperfectness. The workload is greatly responsible for wasting the energy as well as limiting the size and the type of tasks the supercomputers can run . Case studies provide insight how the different contributions compete for dominating the resulting payload performance of the computing system, and how enhancing the technology made the computing+communication the dominating contribution in defining the efficiency of supercomputers. The model also enables to derive predictions about the supercomputer performance limitations for the near future  as well as  provides hints for enhancing the supercomputer components. The phenomena show interesting parallels with the phenomena experienced in science  more than a century ago  and through their studying a modern science was developed.
Using an extremely large number of processing elements in computing systems leads to unexpected phenomena, such as different efficiencies of the same system for different tasks, that cannot be explained in the frame of classical computing paradigm. The simple non-technical (but considering the temporal behavior of the components) model, introduced here, enables us to set up a frame and formalism , needed to explain those unexpected experiences around supercomputing. Introducing temporal behavior into computer science also explains why only the extreme scale computing enabled us to reveal the experienced limitations. The paper shows , that degradation of efficiency of parallelized sequential systems is a natural consequence of the classical computing paradigm, instead of being an engineering imperfectness. The workload , that supercomputers run, is much responsible for wasting energy, as well as limiting the size and type of tasks . Case studies provide insight , how different contributions compete for dominating the resulting payload performance of a computing system, and how enhancing the interconnection technology made computing+communication to dominate in defining the efficiency of supercomputers. Our model also enables to derive predictions about supercomputer performance limitations for the near future , as well as it provides hints for enhancing supercomputer components. Phenomena experienced in large-scale computing show interesting parallels with phenomena experienced in science , more than a century ago , and through their studying a modern science was developed.
A
an
D
the
D
introduced
R
model enables
(but considering the temporal behavior of the components) model, introduced here, enables us
A
,
R
the
those
A
Introducing temporal behavior into computer science also explains why only the extreme scale computing enabled us to reveal the experienced limitations.
R
that the degradation of the efficiency of the parallelized sequential system
, that degradation of efficiency of parallelized sequential systems
A
classical
R
rather than
instead of being
R
is greatly
, that supercomputers run, is much
R
the energy
energy,
D
the
D
the supercomputers can run
R
how the
, how
R
the
a
R
technology made the
interconnection technology made
R
the dominating contribution
to dominate
R
The
Our
D
the
A
,
A
it
D
the
R
The phenomena
Phenomena experienced in large-scale computing
D
the
A
,
A
,