Abstract
Branch instructions create barriers to instruction fetching, thus greatly reducing the fine-grained parallelism of programs. One common method for solving this problem is branch prediction. In this paper, we first present four lemmas to clarify the relationship between the branch prediction hit rate and system performance, hardware efficiency, and branch prediction overhead. We then propose a new branch prediction method called PAM (Period Adaptive Method). An abstract model and detailed implementation of PAM are described. The prediction hit rate of this method was measured using ten Prolog benchmark programs and found to be 97%. When implemented in a superscalar Prolog system, PAM enhances the degree of system parallelism by 80%.
| Original language | English |
|---|---|
| Pages | 744-751 |
| Number of pages | 8 |
| State | Published - 1 Dec 1994 |
| Event | Proceedings of the 1994 International Conference on Parallel and Distributed Systems - Hsinchu, China Duration: 19 Dec 1994 → 21 Dec 1994 |
Conference
| Conference | Proceedings of the 1994 International Conference on Parallel and Distributed Systems |
|---|---|
| City | Hsinchu, China |
| Period | 19/12/94 → 21/12/94 |
Fingerprint Dive into the research topics of 'Branch prediction for enhancing fine-grained parallelism in prolog'. Together they form a unique fingerprint.
Cite this
Ma, R. L., & Chung, C-P. (1994). Branch prediction for enhancing fine-grained parallelism in prolog. 744-751. Paper presented at Proceedings of the 1994 International Conference on Parallel and Distributed Systems, Hsinchu, China, .