Incorporating speculative execution into scheduling of control-flow intensive behavioral descriptions

• Published in: DAC 98: DAC: 35th Annual ACM/IEEE Design Automation Conference pp. 108 - 113
• Main Authors: Lakshminarayana, Ganesh, Raghunathan, Anand, Jha, Niraj K.
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 01.01.1998; IEEE
• Series: ACM Conferences
• Subjects: Clocks; Computer systems organization > Dependable and fault-tolerant systems and networks; Data flow computing; Delay; General and reference > Cross-computing tools and techniques > Performance; Hardware; High level synthesis; Laboratories; Mathematics of computing > Mathematical software; Networks > Network performance evaluation; Permission; Processor scheduling; Scheduling algorithm; Software performance; Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms; Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms; Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making; high-level synthesis; telecommunication
• ISBN: 0897919645; 9780897919647
• DOI: 10.1145/277044.277067

Speculative execution refers to the execution of parts of a computation before the execution of the conditional operations that decide whether it needs to be executed. It has been shown to be a promising technique for eliminating performance bottlenecks imposed by control flow in hardware and software implementations alike. In this paper, we present techniques to incorporate speculative execution in a fine-grained manner into scheduling of control-flow intensive behavioral descriptions. We demonstrate that failing to take into account information such as resource constraints and branch probabilities can lead to significantly sub-optimal performance. We also demonstrate that it may be necessary to speculate simultaneously along multiple paths, subject to resource constraints, in order to minimize the delay overheads incurred when prediction errors occur. Experimental results on several benchmarks show that our speculative scheduling algorithm can result in significant (upto seven-fold) improvements in performance (measured in terms of the average number of clock cycles) as compared to scheduling without speculative execution. Also, the best and worst case execution times for the speculatively performed schedules are the same as or better than the corresponding values for the schedules obtained without speculative execution.