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Memory access has proven to be one of the bottlenecks in modern architectures. Improving memory locality and eliminating the amount of memory access can help release this bottleneck. We present a method for link-time profile-based optimization by reordering the global data of the program and modifying its code accordingly. The proposed optimization reorders the entire global data of the program, according to a representative execution rate of each instruction (or basic block) in the code. The data reordering is done in a way that enables the replacement of frequently-executed Load instructions, which reference the global data, with fast Add Immediate instructions. In addition, it tries to improve the global data locality and to reduce the total size of the global data area. The optimization was implemented into FDPR (Feedback Directed Program Restructuring), a post-link optimizer, which is part of the IBM AIX operating system for the IBM pSeries servers. Our results on SPECint2000 show a significant improvement of up to 11% (average 3%) in execution time, along with up to 97.9% (average 83%) reduction in memory references to the global variables via the global data access mechanism o f the program.

Constraints on the memory size of embedded systems require reducing the image size of executing programs. Common techniques include code compression and reduced instruction sets. We propose a novel technique that eliminates large portions of the executable image without compromising execution time (due to decompression) or code generation (due to reduced instruction sets). Frozen code and data portions are identified using profiling techniques and removed from the loadable image. They are replaced with branches to code stubs that load them in the unlikely case that they are accessed. The executable is sustained in a runnable mode.Analysis of the frozen portions reveals that most are error and uncommon input handlers. Only a minority of the code (less than 1%) that was identified as frozen during a training run, is also accessed with production datasets.The technique was applied on three benchmark suites (SPEC CINT2000, SPEC CFP2000, and MediaBench) and results in image size reductions of up to 73%, 92%, and 85% per suite, The average reductions are 59%, 79%, and 78% per suite.