lightweight reference affinity analysis
Previous studies have shown that array regrouping and structure splitting significantly improve data locality. The most effective technique relies on profiling every access to every data element. The high overhead impedes its adoption in a general compiler, In this paper, we show that for array regrouping in scientific programs, the overhead is not needed since the same benefit can be obtained by pure program analysis.We present an interprocedural analysis technique for array regrouping. For each global array, the analysis summarizes the access pattern by access-frequency vectors and then groups arrays with similar vectors. The analysis is context sensitive, so it tracks the exact array access. For each loop or function call, it uses two methods to estimate the frequency of the execution. The first is symbolic analysis in the compiler. The second is lightweight profiling of the code. The same interprocedural analysis is used to cumulate the overall execution frequency by considering the calling context. We implemented a prototype of both the compiler and the profiling analysis in the IBM compiler, evaluated array regrouping on the entire set of SPEC CPU2000 FORTRAN benchmarks, and compared different analysis methods. The pure compiler-based array regrouping improves the performance for the majority of programs, leaving little room for improvement by code or data profiling.
affinity compiler frequency data regrouping data interleving memory optimization