Energy characterization of graph workloads

Ankur Limaye, Antonino Tumeo, Tosiron Adegbija

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Graph algorithms are critical components of the big-data analysis workflow. The graph kernel performance is highly dependent on the input data graphs. The inherently sparse nature of the input graphs often results in irregular memory access patterns, which may not suit the data-locality based cache optimizations featured in current high-performance processors. Much prior research has identified several optimization opportunities by characterizing graph kernels on existing hardware. However, current graph workload characterization studies focus on performance-related observations and optimizations, overlooking the energy implications. In this paper, we address this technology gap by presenting an exhaustive and systematic energy characterization study of graph kernels. We characterize the six GAP benchmark suite kernels with a variety of input graphs on a dual-socket x86-based system. We then analyze how the algorithms, graph characteristics (like graph scale and degree), and system effects (like parallelism, simultaneous multithreading, and multiprocessing) impact the energy. Based on our analysis, we derive observations and insights to develop a basic energy model. The discussions in the paper can enable researchers to advance new models and energy-efficient architectures for graph workloads.

Original languageEnglish (US)
Article number100465
JournalSustainable Computing: Informatics and Systems
StatePublished - Mar 2021
Externally publishedYes


  • Big data
  • Energy characterization
  • Graph applications
  • Workload analysis

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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