Abstract
The performance of a branch predictor is measured not only by the prediction accuracy - parameters like predictor size, energy expenditure, latency of execution play a key role in predictor selection. The task of selecting the best predictor considering all the different parameters, is therefore, a non-trivial one, and is considered one of the foremost challenges. In this paper, we present a framework that systematically addresses this important challenge using the concept of aggregation and unification and makes a predictor selection based on different parameters. We present experimental results of our framework on the Siemens and SPEC 2006 benchmarks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
The journal of instruction-level parallelism, championship branch prediction. https://www.jilp.org/cbp/
Software artifacts repository. http://sir.unl.edu/portal/index.php
Ghosh, R., et al.: CoCOA: a framework for comparing aggregate client operations in BPO services. In: SCC, pp. 539–546. IEEE (2016)
Henning, J.L.: SPEC CPU2006 benchmark descriptions. ACM SIGARCH Comput. Architect. News 34(4), 1–17 (2006)
Jiménez, D.A., et al.: The impact of delay on the design of branch predictors. In: MICRO, pp. 67–76. ACM (2000)
Kemeny, J.G.: Mathematics without numbers. Daedalus 88(4), 577–591 (1959)
Liu, Y.T., et al.: Supervised rank aggregation. In: WWW, pp. 481–490. ACM (2007)
Parikh, D., et al.: Power issues related to branch prediction. In: HPCA, pp. 233–244. IEEE (2002)
Sarangi, S.R., et al.: Tejas: a java based versatile micro-architectural simulator. In: PATMOS 2015, pp. 47–54. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Das, M., Banerjee, A., Sardar, B. (2017). A Framework for Branch Predictor Selection with Aggregation on Multiple Parameters. In: Kaushik, B., Dasgupta, S., Singh, V. (eds) VLSI Design and Test. VDAT 2017. Communications in Computer and Information Science, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-7470-7_8
Download citation
DOI: https://doi.org/10.1007/978-981-10-7470-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7469-1
Online ISBN: 978-981-10-7470-7
eBook Packages: Computer ScienceComputer Science (R0)