Data Transformations for Eliminating Conflict Misses

Abstract

Many cache misses in scientific programs are due to conflicts caused by limited set associativity. We examine two compile-time data-layout transformations for eliminating conflict misses, concentrating on misses occuring on every loop iteration. Inter-variable padding adjusts variable base addresses, while intra-variable padding modifies array dimension sizes. Two levels of precision are evaluated. PADLlTE only uses array and column dimension sizes, relying on assumptions about common array reference patterns. PAD analyzes programs, detecting conflict misses by linearizing array references and calculating conflict distances between uniformly-generated references. The Euclidean algorithm for computing the gcd of two numbers is used to predict conflicts between different array columns for linear algebra codes. Experiments on a range of programs indicate PADLITE can eliminate conflicts for benchmarks, but PAD is more effective over a range of cache and problem sizes. Padding reduces cache miss rates by 16% on average for a 16K direct-mapped cache. Execution times are reduced by 6% on average, with some SPEC95 programs improving up to 15%.