ALEA: A Fine-Grain Energy Profiling Tool
- Submitting institution
-
Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 123727873
- Type
- D - Journal article
- DOI
-
10.1145/3050436
- Title of journal
- ACM Transactions on Architecture and Code Optimization
- Article number
- 1
- First page
- -
- Volume
- 14
- Issue
- 1
- ISSN
- 1544-3566
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the highest impact ACM journal in the field of Computer Architecture, this paper achieved accurate measurement of power consumption on microprocessors without hardware modifications at the finest known granularity, with a frequency of up to 10 KHz vs. a best known frequency of 3.57 KHz. Supported by an EPSRC grant (EP/L000555/1) the paper led to follow-on funding from the European Commission (H2020-688540) to support research on tools for power measurements in processors with manufacturing variation-dependent operating margins and software that leverages hardware-specific operating margins to improve performance and energy-efficiency in compute servers.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
