PT130.S2.P2.Q13 > Analytics

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This passage was adapted from articles published in the 1990s.

Intro topic · Computer scientist Philip Emeagwali's inspiration and work on parallel computing

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Describe Emeagwali's contribution · Developed parallel computing system to predict underground oil flow

Used network of many computers working simultaneously; solved speed and efficiency problems with traditional "sequential" computers

Problem · Predicting oil flow requires many simultaneous calculations

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Emeagwali's solution · Connect many individual computers over Internet to form huge parallel computer

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New problem and solution · Properly dividing parallel tasks is hard; solution was to design computer network like tree branches

Tree branches follow very efficient patterns for collecting and moving resources; computer networks that follow the same underlying patterns are very efficient at generating and transmitting data

Emeagwali's future work · Parallel computing modeled after spacing of honeycombs

Emeagwali's perspective · Nature will become a more important source of inspiration for technical solutions

Passage Style

Single position

Spotlight

13.

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Question Type

Implied

To understand why massively parallel computers hadn’t been used to model oil field flow prior to 1989, we should understand Emeagwali’s insight that allowed him to solve the problem of modeling oil field flow. In P2, the author tells us that one of the great difficulties of parallel computing is dividing tasks among smaller computers so that they don’t interfere with each other. How did Emeagwali solve this? By designing a system based on the branching structures of trees.

supercomputers are sufficiently ████████ ██ ██████ ████ █████████████ █████████ █████████ ████ ████████ ███████ ████ ███ ██████████

The author never suggests that supercomputers were sufficient for handling “most” (over half) problems arising from oil production. In addition, supercomputers failed to model oil field flow.

the possibility of █████ █ ███████ ██ ███████ █████████ ██ █████ ███████████████ █████████ ████████ ███ ███ ███ ████ ██████████

Not supported, because we don’t know that people were not aware of the “possibility” of using a network of smaller computers to solve difficult problems. We’re told that “one of the great difficulties of parallel computing is dividing up the tasts among the separate smaller computers so that they do not interfere with each other.” This suggests that people already were aware of the possibility of using a network of smaller computers; but the issue was that they hadn’t figured out how to get the computers not to interfere with each other.

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the general public ███ ███ ███ █████ ██ ███ █████████ ██ ████ ████████████ ██ ███ ████████

The general public’s view isn’t mentioned as a reason parallel computing hadn’t been used to model oil field flows.

oil companies had ███ ███ █████████ ███ ████ ███ ████████ ███ ████ ██ ███ ██ ████████████ ██████

The lack of need by oil companies isn’t mentioned as a reason parallel computing hadn’t been used to model oil field flows.

smaller computers can █████████ ████ ███ ███████ ████ ████ ███ █████████ ████████ ██ ████████ ██ █████ █ ███████████████ █████████ ███████

Supported by the middle of P2. Emeagwali’s breakthrough involved solving the problem of dividing tasks among smaller computers so that they don’t interfere with each other. This suggests that prior to Emeagwali, this interference problem was a reason parallel computing hadn’t been used to successfully model oil field flows.

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PrepTest 130 - Section 2 - Passage 2 - Question 13

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