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A seminar about Parallel Computing
A CPU is a microprocessor -- a computing engine on a chip. While
modern microprocessors are small, they're also really powerful. They can
interpret millions of instructions per second. Even so, there are some
computational problems that are so complex that a powerful microprocessor
would require years to solve them.
Computer scientists use different approaches to address this problem. One
potential approach is to push for more powerful microprocessors. Usually this
means finding ways to fit more transistors on a microprocessor chip.
Computer engineers are already building microprocessors with transistors that
are only a few dozen nanometers wide. How small is a nanometer? It's onebillionth
of a meter. A red blood cell has a diameter of 2,500 nanometers -- the
width of modern transistors is a fraction of that size.
Building more powerful microprocessors requires an intense and expensive
production process. Some computational problems take years to solve even
with the benefit of a more powerful microprocessor. Partly because of these
factors, computer scientists sometimes use a different approach: parallel
In general, parallel processing means that at least two microprocessors handle
parts of an overall task. The concept is pretty simple: A computer scientist
divides a complex problem into component parts using special software
specifically designed for the task. He or she then assigns each component part
to a dedicated processor. Each processor solves its part of the overall
computational problem. The software reassembles the data to reach the end
conclusion of the original complex problem.