An Architecture for Combinator Graph Reduction by Philip John Koopman Jr.

By Philip John Koopman Jr.

This booklet combines structure with implementation suggestions for complicated programming languages

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21 seconds on an Acorn RISC Machine (Fairbairn & Wray 1987). This number includes sharing and some strict­ ness analysis. 96 seconds. Table 5-6 compares this performance to TIGRE running on roughly comparable hardware with Turner Set combinators, supercombinator compilation, and super­ combinator with some sharing and strictness analysis (but, almost cer­ tainly, not as good an analysis as that available through the TIM compiler). From this limited data, TIGRE appears to be faster than TIM on small benchmarks.

TIGRE heap memory is likewise allocated from the C heap space (and is managed with garbage collection by TIGRE). Combinator memory corresponds to the compiled C program, which contains the combinator definitions. The scratchpad registers are implemented using register variables. As spine nodes are unwound, addresses to the nodes are placed onto a software-managed stack. When a combinator node is found, a s w i t c h statement (case statement) is executed to jump to the appropriate com­ binator code.

3. TIGRE ASSEMBLER DEFINITIONS OF COMBINATORS 1 9 41 f IFx* 0 9 IFx= 0 1 f IF OR 1 X Figure 4-6. The I F combinator. IFxfg^(If) OR (Ig) The code evaluates the first argument. Then, if the first argument is true (non-zero), the second argument is used to overwrite the third argument, otherwise the third argument is left in place. Finally, an I combinator is placed in the left-hand side of the third argument node, converting the node to a jump to the selected subtree. While the I F combinator could be implemented so as not to rewrite graphs, in the style of the projection combinators, the overhead involved in repeatedly evalu­ ating the first argument probably outweighs the savings possible from not rewriting the graph.

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