Reuse-distance-based miss-rate prediction on a per instruction basis

Feedback-directed optimization has become an increasingly important tool in designing and building optimizing compilers. Recently, reuse-distance analysis has shown much promise in predicting the memory behavior of programs over a wide range of data sizes. Reuse-distance analysis predicts program locality by experimentally determining locality properties as a function of the data size of a program, allowing accurate locality analysis when the program’s data size changes. Prior work has established the effectiveness of reuse distance analysis in predicting whole-program locality and miss rates. In this paper, we show that reuse distance can also effectively predict locality and miss rates on a per instruction basis. Rather than predict locality by analyzing reuse distances for memory addresses alone, we relate those addresses to particular static memory operations and predict the locality of each instruction. Our experiments show that using reuse distance without cache simulation t...

• Type: Conference
• Year: 2004
• Where: ACMMSP
• Authors: Changpeng Fang, Steve Carr, Soner Önder, Zhenlin Wang
• Keywords: ACMMSP 2004, Data Size, Miss Rates, Reuse Distance