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Characterizing Task Scheduling Performance Based on Data Reuse

Ceballos, G. ,  Grass, T. ,  Black-Schaffer, D. ,  Hugo, A.
Book Title:
Proceedings of the 9th Nordic Workshop on Multi-Core Computing


Through the past years, several scheduling heuristics were
introduced to improve the performance of task-based ap-
plications, with schedulers increasingly becoming aware of
memory-related bottlenecks such as data locality and cache
sharing. However, there are not many useful tools that pro-
vide insights to developers about why and where different
schedulers do better scheduling, and how this is related to
the applications’ performance. In this work we present a
technique to characterize different task schedulers based on
the analysis of data reuse, providing high-level, quantitative
information that can be directly correlated with tasks per-
formance variation. This flexible insight is key for optimiza-
tion in many contexts, including data locality, throughput,
memory footprint or even energy efficiency.