標題: Periodic adaptive branch prediction and its application in superscalar processing in Prolog
作者: Ma, RL
Chung, CP
交大名義發表
資訊工程學系
National Chiao Tung University
Department of Computer Science
公開日期: 1995
摘要: Branch instructions create barriers to instruction prefetching, greatly reducing the fine-grained parallelism of programs. Branch prediction is a common method for solving this problem. We first present four lemmata in this paper describing the relationships among branch prediction hit rate and system performance, hardware efficiency, and branch prediction overhead. We then propose a branch prediction method called PAM (Periodic Adaptive Method). An abstract model and detailed implementation of PAM are described. PAM's prediction hit rate as measured by 10 Prolog benchmark programs is 97%. When implemented in a superscalar Prolog system, PAM enhances the degree of system parallelism by 68.8%. PAM can be applied to languages and applications other then the Prolog system we used in this study.
URI: http://hdl.handle.net/11536/2117
http://dx.doi.org/10.1093/comjnl/38.6.457
ISSN: 0010-4620
DOI: 10.1093/comjnl/38.6.457
期刊: COMPUTER JOURNAL
Volume: 38
Issue: 6
起始頁: 457
結束頁: 470
顯示於類別:期刊論文


文件中的檔案:

  1. A1995TR92300005.pdf