A Guidebook to Fortran on Supercomputers by John M. Levesque

By John M. Levesque

Contents: creation; Supercomputer structure; Fortran; Vectorization of Fortran courses. Index. This ebook explains intimately either the underlying structure of modern-day supercomputers and the way in which a compiler maps Fortran code onto that structure. most vital, the constructs fighting complete optimizations are defined, and particular concepts for restructuring a software are supplied.

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Since contiguous vectors are always handled more effi­ ciently than noncontiguous vectors, it is good practice to vary the left-most subscript in inner loops where possible. It is also important (if possible) to have the longest dimension of an array as the left-most to achieve long-vec­ tor processing in inner-loop references. Finally, note the diagonal processing, shown in loop 3020, has a stride of five, which is the length of a column plus one. We shall see in a later section that when nonunit strides are unavoidable, as in diagonal processing, it is sometimes important to adjust the dimension of the arrays to avoid memory-bank conflicts.

An additional device, the "stream unit," performs many special-purpose data-motion operations, among which are the "gather-periodic" and "scatter-periodic" instructions, which specifically handle strided data. These instructions can be used to vectorize the following loop: DO 2090 1 = 1 , 10000, 10 A(I) = B(I) + C(I) 2090 CONTINUE Here the vector pipeline cannot directly fetch or store every tenth item of data. So the vector stream unit issues gather-periodic instructions to fetch the necessary data from the B and C arrays and stores the data into temporary contiguous arrays in memory.

On the Cray computers there is only one of each kind of functional unit in a CPU: one adder, one multiplier, and so forth. " For these machines, the multiple pipelines act in a manner similar to those of the CYBER 205; that is, all duplicate func­ tional units work on the same vectors, each taking a separate segment of the data. In effect, it is as if the vector length were divided by the number of pipelines, with the time to complete a vector operation reduced by about the same factor. Chaining.

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