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Content Provider | ACM Digital Library |
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Author | Stevens, K. Lomax, H. |
Abstract | This paper is addressed to those computer users who have the following interests in common. First, they are engaged in the numerical study of a discipline at a level where they wish to write or to understand critical portions of a computer code written in some high-level or scientific language. Second, the amount of numerical manipulations required in their study is large enough to require highly efficient use of the hardware of a major computer facility in order to prevent a calculation from becoming prohibitively costly. Finally, they are willing to accept the responsibility for making the code efficient, but they wish to minimize the amount of their time spent in doing so. At present it is usually difficult to realize the third of these interests in light of the first two. Big problems point to computers with special architectures, and these are usually difficult to program efficiently without devoting a sizable effort to data storage and data management. This paper explores some strategies that can be useful in reducing this difficulty. All of our examples will involve the solution of partial differential equations in three-dimensions by means of numerical algorithms requiring meshes with rectangular topologies. In all cases these “big problems” are defined to have data bases that are too large to fit in core. |
File Format | |
DOI | 10.1145/800207.806415 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 1975-12-08 |
Publisher Place | New York |
Access Restriction | Subscribed |
Content Type | Text |
Resource Type | Article |
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