@article{4506,
  author = {Davide Morelli, Antonio Cisternino},
  title = {A Compositional and Algebraic Framework for Resource Usage Characterization of Software and Hardware},
  journal = {Journal of Data Processing},
  year = {2025},
  volume = {15},
  number = {2},
  doi = {https://doi.org/10.6025/jdp/2025/15/2/59-66},
  url = {https://www.dline.info/jdp/fulltext/v15n2/jdpv15n2_2.pdf},
  abstract = {This paper presents a compositional and algebraic model for characterizing software and hardware based
on their resource usage, with a focus on energy consumption and completion time. Recognising the limitations
of instruction-level profiling and the nondeterminism introduced by modern system complexities, the
authors propose a black-box approach that measures the overall resource usage of programs without requiring
source code access. By treating programs as combinations of computational patterns, the model
uses synthetic benchmarks as building blocks to represent programs in terms of their resource behavior.
The authors introduce a linear algebra framework where resource usage data is captured in matrices,
enabling decomposition of programs into benchmark-based components. The model defines three conceptual
spaces—measurement space, splitup space, and benchmark space—to analyze program behavior, system
characteristics, and resource efficiency. This enables comparison of software across systems and identification
of unknown computational patterns.
Experimental results on a desktop system demonstrate how the model captures the growing dominance of
memory operations in mergesort as input size increases. The approach offers a scalable, source-independent
method for profiling and predicting energy and performance behavior across heterogeneous environments.
Future work will explore applications in cloud computing, virtual machines, and heterogeneous architectures
for real-time resource forecasting.},
}