Skip to main content

Concluding Remarks

  • Chapter
  • First Online:
Automatic Tuning of Compilers Using Machine Learning

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSPOLIMI))

  • 1099 Accesses

Abstract

In this book, we have tackled the major problems of compiler autotuning. We have used machine learning, DSE, and meta-heuristic techniques to construct efficient and accurate models to induce prediction models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amir H. Ashouri .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ashouri, A.H., Palermo, G., Cavazos, J., Silvano, C. (2018). Concluding Remarks. In: Automatic Tuning of Compilers Using Machine Learning. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-71489-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71489-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71488-2

  • Online ISBN: 978-3-319-71489-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics