Evaluate solution options.
Learning Objective:
For sequential, parallel, and
distributed computing:
a. Compare problem solutions.
b. Determine the efficiency of solutions.
CSN-2.A.1
Sequential computing is a computational
model in which operations are performed in
order one at a time.
CSN-2.A.2
Parallel computing is a computational model
where the program is broken into multiple
smaller sequential computing operations,
some of which are performed simultaneously.
CSN-2.A.3
Distributed computing is a computational
model in which multiple devices are used to run
a program.
CSN-2.A.4
Comparing efficiency of solutions can be done
by comparing the time it takes them to perform
the same task.
CSN-2.A.5
A sequential solution takes as long as the sum
of all of its steps.
CSN-2.A.6
A parallel computing solution takes as long
as its sequential tasks plus the longest of its
parallel tasks.
The “speedup” of a parallel solution is
measured in the time it took to complete the
task sequentially divided by the time it took to
complete the task when done in parallel longer meaningfully increase
efficiency.
Learning Objective:
Describe benefits and challenges of parallel and distributed computing
CSN-2.B.1
Parallel computing consists of a parallel portion
and a sequential portion.
CSN-2.B.2
Solutions that use parallel computing can
scale more effectively than solutions that use
sequential computing.
CSN-2.B.3
Distributed computing allows problems to be
solved that could not be solved on a single
computer because of either the processing
time or storage needs involved.
CSN-2.B.4
Distributed computing allows much larger
problems to be solved quicker than they could
be solved using a single computer.
CSN-2.B.5
When increasing the use of parallel computing
in a solution, the efficiency of the solution
is still limited by the sequential portion. This
means that at some point, adding parallel
portions will no