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By Philip John Koopman Jr.

This ebook combines structure with implementation thoughts for complicated programming languages

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S' c f g x -> (c (f x)) (g x) X RtempO x 1 Rtemp2 30 Chapter 4. Implementation of the TIGRE Machine First, three new heap cells are allocated, with pointers to them left in tempO, t e m p i , and temp2. Next, the newly allocated heap cells are written with values taken from the input parameters to the combinator. The first instruction deposits the contents of the right-hand side of the node pointed to by the second-to-top spine stack element (performing a double indirect fetch through the spine stack) into the left-hand side of the tempO node just allocated on the heap.

2· TIGRE P e r f o r m a n c e for S u p e r c o m b i n a t o r Compilation Supercombinator compilation, combined with strictness and sharing anal­ ysis, has been shown to give up to an order of magnitude speed improve­ ment for other combinator reduction methods (Fairbairn & Wray 1987). Of course, when the complexity of a combinator definition is not held constant, the concept of simply measuring the number of combinator reductions per second directly is not very meaningful. Therefore, all results for supercombinator reduction will be expressed in terms of abso­ lute time and a newly created metric, normalized RAPS.

2 compares some of these measurements with published benchmark results for other combinator reduction strategies. 3 compares TIGRE execution speeds with other languages. 4 provides a brief analysis of the performance results. 1. TIGRE PERFORMANCE ON VARIOUS PLATFORMS The performance of TIGRE can, in turn, only be compared to available performance measures for other machines. The available performance measures vary, and include reduction applications per second (RAPS, which may be thought of as the number of Turner Set combinatore executed per second), and nfib recursions per second (the number of increments performed by the recursive nfib benchmark per second).

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