Microarchitectural design space exploration made fast

  • Authors:

  • Qi Guo;Tianshi Chen;Yunji Chen;Ling Li;Weiwu Hu

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China and Graduate University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China and Loongson Technologies Corporation Limited, Beijing 100190, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China and Loongson Technologies Corporation Limited, Beijing 100190, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China and Loongson Technologies Corporation Limited, Beijing 100190, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China and Loongson Technologies Corporation Limited, Beijing 100190, China

  • Venue:
  • Microprocessors & Microsystems
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Predictive modeling is an emerging methodology for microarchitectural design space exploration. However, this method suffers from high costs to construct predictive models, especially when unseen programs are employed in performance evaluation. In this paper, we propose a fast predictive model-based approach for microarchitectural design space exploration. The key of our approach is utilizing inherent program characteristics as prior knowledge (in addition to microarchitectural configurations) to build a universal predictive model. Thus, no additional simulation is required for evaluating new programs on new configurations. Besides, due to employed model tree technique, we can provide insights of the design space for early design decisions. Experimental results demonstrate that our approach is comparable to previous approaches regarding their prediction accuracies of performance/energy. Meanwhile, the training time of our approach achieves 7.6-11.8x speedup over previous approaches for each workload. Moreover, the training costs of our approach can be further reduced via instrumentation technique.