Analysis of two-level data mapping in an HPF compiler for distributed-memory machines

Application programs in scientific and technological fields have grown increasingly large and complex. Thus, it is becoming more difficult to parallelize these programs by hand using message-passing libraries. To reduce this difficulty, we are researching the compilation technology for an industry-standard data-parallel language: High Performance Fortran (HPF). This paper proposes new data-mapping analysis applicable to HPF data-remapping directives more general than those previous research has coped with. This analysis allows programmers to develop highly productive HPF programs. We implemented the analysis in our HPF compiler and evaluated using some benchmark programs. As a result, both programs with general HPF directives and with restricted ones were transformed to similar codes and these codes ran in similar processing speed on a Hitachi SR2201 supercomputer.