In global scheduling for ILP processors, regionenlarging optimizations, especially tail duplication, are commonly used. The code size increase due to such optimizations, however, raises serious concerns about the affected I-cache and TLB performance. In this paper, we propose a quantitative measure of the code size efficiency at compile time for any code size related optimization. Then, based on the efficiency of tail duplication, we propose the solutions to two related problems: (1) how to achieve the best performance for a given code size increase, (2) how to get the optimal code size efficiency for any program. Our study shows that code size increase has a significant but varying impact on IPC, e.g., the first 2% code size increase results in 18.5% increase in static IPC, but less than 1% when the given code size further increases from 20% to 30%. We then use this feature to define the optimal code size efficiency and to derive a simple, yet robust threshold scheme finding it. The experimental results using SPECint95 benchmarks show that this threshold scheme finds the optimal efficiency accurately. While the optimal efficiency results show an average increase of 2% in code size, the improved I-cache performance is observed and a speedup of 17% over the natural treegion results is achieved.
['Huiyang Zhou', 'Thomas M. Conte']
Code size efficiency in global scheduling for ILP processors