Software-controlled processor stalls for time and energy efficient data locality optimization

Data locality optimization is a well-known goal when handling programs that must run as fast as possible or use a minimum amount of energy. However, usual techniques never address the significant impact of numerous stalled processor cycles that may occur when consecutive load and store instructions are accessing the same memory location. We show that two versions of the same program may exhibit similar memory performance, while performing very differently regarding their execution times because of the stalled processor cycles generated by many pipeline hazards. We propose a new programming structure called “xfor”, enabling the explicit control of the way data locality is optimized in a program and thus, to control the amount of stalled processor cycles. We show the benefits of xfor regarding execution time and energy saving.