The DOI matches its Crossref record.
Gprof: A call graph execution profiler
Abstract
Large complex programs are composed of many small routines that implement abstractions for the routines that call them. To be useful, an execution profiler must attribute execution time in a way that is significant for the logical structure of a program as well as for its textual decomposition. This data must then be displayed to the user in a convenient and informative way. The gprof profiler accounts for the running time of called routines in the running time of the routines that call them. The design and use of this profiler is described.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Answering conceptual queries with Ferret
de Alwis, B., & Murphy, G. C. (2008) · Proceedings of the 30th international conference on Software engineering · doi:10.1145/1368088.1368092 · Cited by 63
Huang, L., & Zhu, T. (2021) · Proceedings of the ACM Symposium on Cloud Computing · doi:10.1145/3472883.3486994 · Cited by 37
Visual Debugging Techniques for Reactive Data Visualization
Hoffswell, J., Satyanarayan, A., & Heer, J. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12903 · Cited by 24
Software Black Box: an alternative mechanism for failure analysis
Elbaum, S., & Munson, J. C. (2002) · Proceedings 11th International Symposium on Software Reliability Engineering. ISSRE 2000 · doi:10.1109/issre.2000.885887 · Cited by 15
Large-Scale Neo-Heterogeneous Programming and Optimization of SNP Detection on Tianhe-2
Cui, Y., Liao, X., Peng, S., Lu, Y., Yang, C., Wang, B., & Wu, C. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-20119-1_6 · Cited by 4
Metadata
- Authors
- Graham, S. L., Kessler, P. B., & Mckusick, M. K.
- Venue
- Proceedings of the 1982 SIGPLAN symposium on Compiler construction - SIGPLAN '82
- Publisher
- ACM Press
- Published
- 1982
- Pages
- 120-126
- OpenAlex
- W2144433126
- Cited by
- 971
- References
- 0
- FWCI
- 1.07
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Gprof: A call graph execution profiler. https://app.cochat.ai/papers/10.1145/800230.806987
- The paper
- Graham, S. L., Kessler, P. B., & Mckusick, M. K. (1982). Gprof: A call graph execution profiler. Proceedings of the 1982 SIGPLAN symposium on Compiler construction - SIGPLAN '82. https://doi.org/10.1145/800230.806987
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
