Optimizing Program Locality through CMEs and GAs

Caches have become increasingly important with the widening gap between main memory and processor speeds. Small and fast cache memories are designed to bridge this discrepancy. However, they are only effective when programs exhibit sufficient data locality. Performance o

Optimizing program locality through CMEs and GAs

Caches have become increasingly important with the widening gap between main memory and processor speeds. Small and fast cache memories are designed to bridge this discrepancy. However, they are only effective when programs exhibit sufficient data locality. Performance of memory hierarchy can be improved by means of data and loop transformations. Tiling is a loop transformation that aims at reducing capacity misses by exploiting reuse at the lower levels of cache. Padding is a data transformation targeted to reduce conflict misses. We present an accurate cost model which makes use of the cache miss equations (CMEs) to guide tiling and padding transformations. It describes misses across different hierarchy levels and considers the effects of other hardware components such as branch predictors. We combine the cost model with a genetic algorithm (GA) to select the tile and pad factors that enhance the program. Our results show that this scheme is useful to optimize programs' performance. When compared to previous works, we observe that with a reasonable compile-time overhead, our approach obtains significant performance improvements for all studied kernels on a variety of architectures.Peer Reviewe

Optimizing program locality through CMEs and GAs

Caches have become increasingly important with the widening gap between main memory and processor speeds. Small and fast cache memories are designed to bridge this discrepancy. However, they are only effective when programs exhibit sufficient data locality. Performance of memory hierarchy can be improved by means of data and loop transformations. Tiling is a loop transformation that aims at reducing capacity misses by exploiting reuse at the lower levels of cache. Padding is a data transformation targeted to reduce conflict misses. We present an accurate cost model which makes use of the cache miss equations (CMEs) to guide tiling and padding transformations. It describes misses across different hierarchy levels and considers the effects of other hardware components such as branch predictors. We combine the cost model with a genetic algorithm (GA) to select the tile and pad factors that enhance the program. Our results show that this scheme is useful to optimize programs' performance. When compared to previous works, we observe that with a reasonable compile-time overhead, our approach obtains significant performance improvements for all studied kernels on a variety of architectures.Peer Reviewe