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Tactic: Minimize Referencing to Data
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: green-ml-enabled-systems
Title
Minimize Referencing to Data
Description
Machine learning models require reading and writing enormous amounts of data in the ML workflow. Reading data means retrieving information from storage, while writing data means storing or updating the information. These operations may increase unnecessary data movements and memory usage, which influence the energy consumption of computing. To avoid non-essential referencing of data, reading and writing operations must be designed carefully.
Participant
Software Designer
Related software artifact
ML Model
Context
Machine Learning, General
Software feature
Inference
Tactic intent
Improve energy efficiency by avoiding unnecessary data read/write operations
Target quality attribute
Energy Efficiency
Other related quality attributes
Resource Utilization
Measured impact
< unknown >
Source
Shanbhag, S., Chimalakonda, S., Sharma, V. S., & Kaulgud, V. (2022, June). Shriram Shanbhag, Sridhar Chimalakonda, Vibhu Saujanya Sharma, and Vikrant Kaulgud. 2022. Towards a Catalog of Energy Patterns in Deep Learning Development. In Proceedings of the International Conference on Evaluation and Assessment in Software Engineering 2022. 150–159. (DOI: https://doi.org/10.1145/3530019.3530035)Graphical representation
