Algorithms & data structures
Field results with the APL compiler
APL '86 Proceedings of the international conference on APL
Automatic parallelization of APL-style programs
APL '90 Conference proceedings on APL 90: for the future
Assembler utility functions for APL2/PC
APL '91 Proceedings of the international conference on APL '91
ACM SIGPLAN Notices
Execution of automatically parallelized APL programs on RP3
IBM Journal of Research and Development
APL '93 Proceedings of the international conference on APL
The testing of an APL compiler
APL '93 Proceedings of the international conference on APL
| Hi-index | 0.00 |
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.