The running time of programs in a paging machine generally increases as the store in which programs are constrained to run decreases. Experiment, however, have revealed cases in which the reverse is true: a decrease in the size of the store is accompanied by a decrease in running time. An informal discussion of the anomalous behavior is given, and for the case of the FIFO replacement algorithm a formal treatment is presented.

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The performance of a compiler is usually measured in terms of the execution efficiency of compiled code and the speed of compilation. For an APL compiler, we are also concerned about its relative performance with respect to the interpreter: C/I, the ratio of the speed of compiled code over interpretation. We give performance data on 4 groups of examples of moderate size: i) scalar style code where an interpreter does poorly and the C/I ratio is very high, ii) good APL style code where interpreter still does poorly due to inherent iterativeness or recursion, and the C/I ratio is high, and iii) good APL style code where the interpreter is very efficient on large data, and the C/I ratio is low, and iv) some particular primitives. These examples include neural net simulation, machine simulation, network routing, signal processing and mathematical computations. The APL370 compiler not only speeds up applications of iterative nature, but also gives good performance to codes utilizing APL's strength such as boolean selection and boolean data manipulation. APL QUOTE QUADChing, Nelson and Shi

No abstract

The performance of a compiler is usually measured in terms of the execution efficiency of compiled code and the speed of compilation. For an APL compiler, we are also concerned about its relative performance with respect to the interpreter: C/I, the ratio of the speed of compiled code over interpretation. We give performance data on 4 groups of examples of moderate size: i) scalar style code where an interpreter does poorly and the C/I ratio is very high, ii) good APL style code where interpreter still does poorly due to inherent iterativeness or recursion, and the C/I ratio is high, and iii) good APL style code where the interpreter is very efficient on large data, and the C/I ratio is low, and iv) some particular primitives. These examples include neural net simulation, machine simulation, network routing, signal processing and mathematical computations. The APL370 compiler not only speeds up applications of iterative nature, but also gives good performance to codes utilizing APL's strength such as boolean selection and boolean data manipulation.