PrepTest 58, Section 4, Question 11
This passage was adapted from articles published in the 1990s.
The success that Nigerian-born computer scientist Philip Emeagwali (b. 1954) has had in designing computers that solve real-world problems has been fueled by his willingness to reach beyond established paradigms and draw inspiration for his designs from nature. In the 1980s, Emeagwali achieved breakthroughs in the design of parallel computer systems. Whereas single computers work sequentially, making one calculation at a time, computers connected in parallel can process calculations simultaneously. In 1989, Emeagwali pioneered the use of massively parallel computers that used a network of thousands of smaller computers to solve what is considered one of the most computationally difficult problems: predicting the flow of oil through the subterranean geologic formations that make up oil fields. Until that time, supercomputers had been used for oil field calculations, but because these supercomputers worked sequentially, they were too slow and inefficient to accurately predict such extremely complex movements.
To model oil field flow using a computer requires the simulation of the distribution of the oil at tens of thousands of locations throughout the field. At each location, hundreds of simultaneous calculations must be made at regular time intervals relating to such variables as temperature, direction of oil flow, viscosity, and pressure, as well as geologic properties of the basin holding the oil. In order to solve this problem, Emeagwali designed a massively parallel computer by using the Internet to connect to more than 65,000 smaller computers. One of the great difficulties of parallel computing is dividing up the tasks among the separate smaller computers so that they do not interfere with each other, and it was here that Emeagwali turned to natural processes for ideas, noting that tree species that survive today are those that, over the course of hundreds of millions of years, have developed branching patterns that have maximized the amount of sunlight gathered and the quantity of water and sap delivered. Emeagwali demonstrated that, for modeling certain phenomena such as subterranean oil flow, a network design based on the mathematical principle that underlies the branching structures of trees will enable a massively parallel computer to gather and broadcast the largest quantity of messages to its processing points in the shortest time.
In 1996 Emeagwali had another breakthrough when he presented the design for a massively parallel computer that he claims will be powerful enough to predict global weather patterns a century in advance. The computer's design is based on the geometry of bees' honeycombs, which use an extremely efficient three-dimensional spacing. Emeagwali believes that computer scientists in the future will increasingly look to nature for elegant solutions to complex technical problems. This paradigm shift, he asserts, will enable us to better understand the systems evolved by nature and, thereby, to facilitate the evolution of human technology.
This passage was adapted from articles published in the 1990s.
The success that Nigerian-born computer scientist Philip Emeagwali (b. 1954) has had in designing computers that solve real-world problems has been fueled by his willingness to reach beyond established paradigms and draw inspiration for his designs from nature. In the 1980s, Emeagwali achieved breakthroughs in the design of parallel computer systems. Whereas single computers work sequentially, making one calculation at a time, computers connected in parallel can process calculations simultaneously. In 1989, Emeagwali pioneered the use of massively parallel computers that used a network of thousands of smaller computers to solve what is considered one of the most computationally difficult problems: predicting the flow of oil through the subterranean geologic formations that make up oil fields. Until that time, supercomputers had been used for oil field calculations, but because these supercomputers worked sequentially, they were too slow and inefficient to accurately predict such extremely complex movements.
To model oil field flow using a computer requires the simulation of the distribution of the oil at tens of thousands of locations throughout the field. At each location, hundreds of simultaneous calculations must be made at regular time intervals relating to such variables as temperature, direction of oil flow, viscosity, and pressure, as well as geologic properties of the basin holding the oil. In order to solve this problem, Emeagwali designed a massively parallel computer by using the Internet to connect to more than 65,000 smaller computers. One of the great difficulties of parallel computing is dividing up the tasks among the separate smaller computers so that they do not interfere with each other, and it was here that Emeagwali turned to natural processes for ideas, noting that tree species that survive today are those that, over the course of hundreds of millions of years, have developed branching patterns that have maximized the amount of sunlight gathered and the quantity of water and sap delivered. Emeagwali demonstrated that, for modeling certain phenomena such as subterranean oil flow, a network design based on the mathematical principle that underlies the branching structures of trees will enable a massively parallel computer to gather and broadcast the largest quantity of messages to its processing points in the shortest time.
In 1996 Emeagwali had another breakthrough when he presented the design for a massively parallel computer that he claims will be powerful enough to predict global weather patterns a century in advance. The computer's design is based on the geometry of bees' honeycombs, which use an extremely efficient three-dimensional spacing. Emeagwali believes that computer scientists in the future will increasingly look to nature for elegant solutions to complex technical problems. This paradigm shift, he asserts, will enable us to better understand the systems evolved by nature and, thereby, to facilitate the evolution of human technology.
This passage was adapted from articles published in the 1990s.
The success that Nigerian-born computer scientist Philip Emeagwali (b. 1954) has had in designing computers that solve real-world problems has been fueled by his willingness to reach beyond established paradigms and draw inspiration for his designs from nature. In the 1980s, Emeagwali achieved breakthroughs in the design of parallel computer systems. Whereas single computers work sequentially, making one calculation at a time, computers connected in parallel can process calculations simultaneously. In 1989, Emeagwali pioneered the use of massively parallel computers that used a network of thousands of smaller computers to solve what is considered one of the most computationally difficult problems: predicting the flow of oil through the subterranean geologic formations that make up oil fields. Until that time, supercomputers had been used for oil field calculations, but because these supercomputers worked sequentially, they were too slow and inefficient to accurately predict such extremely complex movements.
To model oil field flow using a computer requires the simulation of the distribution of the oil at tens of thousands of locations throughout the field. At each location, hundreds of simultaneous calculations must be made at regular time intervals relating to such variables as temperature, direction of oil flow, viscosity, and pressure, as well as geologic properties of the basin holding the oil. In order to solve this problem, Emeagwali designed a massively parallel computer by using the Internet to connect to more than 65,000 smaller computers. One of the great difficulties of parallel computing is dividing up the tasks among the separate smaller computers so that they do not interfere with each other, and it was here that Emeagwali turned to natural processes for ideas, noting that tree species that survive today are those that, over the course of hundreds of millions of years, have developed branching patterns that have maximized the amount of sunlight gathered and the quantity of water and sap delivered. Emeagwali demonstrated that, for modeling certain phenomena such as subterranean oil flow, a network design based on the mathematical principle that underlies the branching structures of trees will enable a massively parallel computer to gather and broadcast the largest quantity of messages to its processing points in the shortest time.
In 1996 Emeagwali had another breakthrough when he presented the design for a massively parallel computer that he claims will be powerful enough to predict global weather patterns a century in advance. The computer's design is based on the geometry of bees' honeycombs, which use an extremely efficient three-dimensional spacing. Emeagwali believes that computer scientists in the future will increasingly look to nature for elegant solutions to complex technical problems. This paradigm shift, he asserts, will enable us to better understand the systems evolved by nature and, thereby, to facilitate the evolution of human technology.
This passage was adapted from articles published in the 1990s.
The success that Nigerian-born computer scientist Philip Emeagwali (b. 1954) has had in designing computers that solve real-world problems has been fueled by his willingness to reach beyond established paradigms and draw inspiration for his designs from nature. In the 1980s, Emeagwali achieved breakthroughs in the design of parallel computer systems. Whereas single computers work sequentially, making one calculation at a time, computers connected in parallel can process calculations simultaneously. In 1989, Emeagwali pioneered the use of massively parallel computers that used a network of thousands of smaller computers to solve what is considered one of the most computationally difficult problems: predicting the flow of oil through the subterranean geologic formations that make up oil fields. Until that time, supercomputers had been used for oil field calculations, but because these supercomputers worked sequentially, they were too slow and inefficient to accurately predict such extremely complex movements.
To model oil field flow using a computer requires the simulation of the distribution of the oil at tens of thousands of locations throughout the field. At each location, hundreds of simultaneous calculations must be made at regular time intervals relating to such variables as temperature, direction of oil flow, viscosity, and pressure, as well as geologic properties of the basin holding the oil. In order to solve this problem, Emeagwali designed a massively parallel computer by using the Internet to connect to more than 65,000 smaller computers. One of the great difficulties of parallel computing is dividing up the tasks among the separate smaller computers so that they do not interfere with each other, and it was here that Emeagwali turned to natural processes for ideas, noting that tree species that survive today are those that, over the course of hundreds of millions of years, have developed branching patterns that have maximized the amount of sunlight gathered and the quantity of water and sap delivered. Emeagwali demonstrated that, for modeling certain phenomena such as subterranean oil flow, a network design based on the mathematical principle that underlies the branching structures of trees will enable a massively parallel computer to gather and broadcast the largest quantity of messages to its processing points in the shortest time.
In 1996 Emeagwali had another breakthrough when he presented the design for a massively parallel computer that he claims will be powerful enough to predict global weather patterns a century in advance. The computer's design is based on the geometry of bees' honeycombs, which use an extremely efficient three-dimensional spacing. Emeagwali believes that computer scientists in the future will increasingly look to nature for elegant solutions to complex technical problems. This paradigm shift, he asserts, will enable us to better understand the systems evolved by nature and, thereby, to facilitate the evolution of human technology.
Which one of the following most accurately describes the function of the first two sentences of the second paragraph?
They provide an example of an established paradigm that Emeagwali's work has challenged.
They help explain why supercomputers are unable to accurately predict the movements of oil through underground geologic formations.
They provide examples of a network design based on the mathematical principles underlying the branching structures of trees.
They describe a mathematical model that Emeagwali used in order to understand a natural system.
They provide specific examples of a paradigm shift that will help scientists understand certain systems evolved by nature.
Explanations
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