Recurrence analysis for effective array prefetching in Java

TL;DR: A new unified compile‐time analysis for software prefetching arrays and linked structures in Java is described to hide memory latency and it is shown that the additional loop transformations and careful scheduling of prefetches from previous work are not always necessary for modern architectures and Java programs.

Abstract: SUMMARY Java is an attractive choice for numerical, as well as other, algorithms due to the software engineering benefits of object-oriented programming. Because numerical programs often use large arrays that do not fit in the cache, they to suffer from poor memory performance. To hide memory latency, we describe a new unified compile-time analysis for software prefetching arrays and linked structures in Java. Our previous work uses data-flow analysis to discover linked data structure accesses. We generalize our prior approach to identify loop induction variables as well, which we call recurrence analysis. Our algorithm schedules prefetches for all array references that contain induction variables. We evaluate our technique using a simulator of an out-of-order superscalar processor running a set of array-based Java programs. Across all our programs, prefetching reduces execution time by a geometric mean of 23%, and the largest improvement is 58%. We also evaluate prefetching on a PowerPC processor, and we show that prefetching reduces execution time by a geometric mean of 17%. Because our analysis is much simpler and quicker than previous techniques, it is suitable for including in a just-in-time compiler. Traditional software prefetching algorithms for C and Fortran use locality analysis and sophisticated loop transformations. We further show that the additional loop transformations and careful scheduling of prefetches from previous work are not always necessary for modern architectures and Java programs.