Skip to content

Research at St Andrews

Understanding computation time: a critical discussion of time as a computational performance metric

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Open Access Status

  • Embargoed (until 1/01/50)

Abstract

Computation time is an important performance metric that scientists and software engineers use to determine whether an algorithm is capable of running within a reasonable time frame. We provide an accessible critical review of the factors that influence computation time, highlighting problems in its reporting in current research and the negative practical impact that this has on developers, recommending best practice for its measurement and reporting. Discussing how computers and coders measure time, a discrepancy is exposed between best practice in the primarily theoretical field of computational complexity, and the difficulty for non-specialists in applying such theoretical findings. We therefore recommend establishing a better reporting practice, highlighting future work needed to expose the effects of poor reporting. Freely shareable templates are provided to help developers and researchers report this information more accurately, helping others to build upon their work, and thereby reducing the needless global duplication of computational and human effort.
Close

Details

Original languageEnglish
Title of host publicationTime in variance
Subtitle of host publicationthe study of time
EditorsJo Parker, Paul Harris, Arkadiusz Misztal
PublisherBrill
Volume17
Publication statusAccepted/In press - 3 Aug 2020
EventThe 17th triennial conference of the International Society for the Study of Time: Time in Variance - Loyola Marymount University, California, United States
Duration: 23 Jun 201929 Jun 2019
http://www.studyoftime.org/ContentPage.aspx?ID=1043

Publication series

NameThe Study of Time
PublisherBrill

Conference

ConferenceThe 17th triennial conference of the International Society for the Study of Time
CountryUnited States
CityCalifornia
Period23/06/1929/06/19
Internet address

    Research areas

  • Time, Computation, Computation time, Computational complexity, Software, Hardware, Time complexity, GPU, CPU, TPU

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Ethics and acceptance of smart homes for older adults

    Pirzada, P., Wilde, A., Doherty, G. H. & Harris-Birtill, D., 9 Jul 2021, (E-pub ahead of print) In: Informatics for Health and Social Care. Latest Articles, 28 p.

    Research output: Contribution to journalReview articlepeer-review

  2. Generative deep learning in digital pathology workflows

    Morrison, D., Harris-Birtill, D. & Caie, P. D., 8 Apr 2021, (E-pub ahead of print) In: The American Journal of Pathology.

    Research output: Contribution to journalReview articlepeer-review

  3. Templated text synthesis for expert-guided multi-label extraction from radiology reports

    Schrempf, P., Watson, H., Park, E., Pajak, M., MacKinnon, H., Muir, K. W., Harris-Birtill, D. & O’Neil, A. Q., 24 Mar 2021, In: Machine Learning and Knowledge Extraction. 3, 2, p. 299-317 19 p.

    Research output: Contribution to journalArticlepeer-review

  4. Autofocus Net: Auto-focused 3D CNN for Brain Tumour Segmentation.

    Stefani, A., Rahmat, R. & Harris-Birtill, D. C. C., 8 Jul 2020, In Annual Conference on Medical Image Understanding and Analysis: Part of the Communications in Computer and Information Science book series (CCIS). Springer, Vol. 1248. p. 43-55 13 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

  5. Paying per-label attention for multi-label extraction from radiology reports

    Schrempf, P., Watson, H., Mikhael, S., Pajak, M., Falis, M., Lisowska, A., Muir, K. W., Harris-Birtill, D. & O'Neil, A. Q., 2020, Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings. Cardoso, J., Van Nguyen, H., Heller, N., Henriques Abreu, P., Isgum, I., Silva, W., Cruz, R., Pereira Amorim, J., Patel, V., Roysam, B., Zhou, K., Jiang, S., Le, N., Luu, K., Sznitman, R., Cheplygina, V., Mateus, D., Trucco, E. & Abbasi, S. (eds.). Cham: Springer, p. 277-289 13 p. (Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics); vol. 12446 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

ID: 270598057

Top