We study the cache performance of a set of ML programs, compiled by the Standard ML of New Jersey compiler. We find that more than half of the reads are for objects that have just been allocated. We also consider the effects of varying software (garbage collection frequency) and hardware (cache) parameters. Confirming results of related experiments, we found that ML programs can have good cache performance when there is no penalty for allocation. Even on caches that have an allocation penalty, we found that ML programs can have lower miss ratios than the C and Fortran SPEC92 benchmarks. Topics:: 4 benchmarks, performance analysis; 21 hardware design, measurements; 17 garbage collection, storage allocation; 46 runtime systems.

This technical report has been published as

Cache Performance of Fast-Allocating Programs. Marcelo J. R. Gonclaves and Andrew W. Appel, Proc. Seventh Int'l Conf. on Functional Programming and Computer Architecture, pp. 293-305, ACM Press, June 1995.