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Summary: Cache Performance of FastAllocating Programs
Marcelo J. R. Gonc›alves and Andrew W. Appel
Princeton University
Department of Computer Science
{mjrg,appel}@cs.princeton.edu
Abstract
We study the cache performance of a set of ML programs, com
piled by the Standard ML of New Jersey compiler. We find that
more than half of the reads are for objects that have just been allo
cated. We also consider the effects of varying software (garbage
collection frequency) and hardware (cache) parameters. Confirm
ing 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.
1 Introduction
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