Sequential Scheduling of Dataflow Graphs for Memory Peak Minimization - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

Sequential Scheduling of Dataflow Graphs for Memory Peak Minimization


Many computing systems are constrained by their fixed amount of shared memory. Modeling applications with task or Synchronous DataFlow (SDF) graphs makes it possible to analyze and optimize their memory peak. The problem studied by this paper is the memory peak minimization of such graphs when scheduled sequentially. Regarding task graphs, former work has focused on the Series-Parallel Directed Acyclic Graph (SP-DAG) subclass and proposed techniques to find the optimal sequential algorithm w.r.t. memory peak. In this paper, we propose task graph transformations and an optimized branch and bound algorithm to solve the problem on a larger class of task graphs. The approach also applies to SDF graphs after converting them to task graphs. However, since that conversion may produce very large graphs, we also propose a new suboptimal method, similar to Partial Expansion Graphs, to reduce the problem size. We evaluate our approach on classic benchmarks, on which we always outperform the state-of-the-art.
Fichier principal
Vignette du fichier
main.pdf (528.71 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04163123 , version 1 (17-07-2023)





Pascal Fradet, Alain Girault, Alexandre Honorat. Sequential Scheduling of Dataflow Graphs for Memory Peak Minimization. LCTES 2023 - 24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, Jun 2023, Orlando (FL), United States. pp.76-86, ⟨10.1145/3589610.3596280⟩. ⟨hal-04163123⟩
22 View
63 Download



Gmail Facebook X LinkedIn More