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Section: New Results

Scheduling trees of malleable tasks for sparse linear algebra

Participants : Abdou Guermouche [Univ. Bordeaux/Inria Bordeaux Sud-Ouest] , Loris Marchal, Bertrand Simon, Oliver Sinnen [Univ. Auckland/New Zealand] , Frédéric Vivien.

Scientific workloads are often described by directed acyclic task graphs. This is in particular the case for multifrontal factorization of sparse matrices —the focus of this work— whose task graph is structured as a tree of parallel tasks. Prasanna and Musicus  [84] , [85] advocated using the concept of malleable tasks to model parallel tasks involved in matrix computations. In this powerful model each task is processed on a time-varying number of processors. Following Prasanna and Musicus, we consider malleable tasks whose speedup is $p^{\alpha }$, where $p$ is the fractional share of processors on which a task executes, and $\alpha$ ($0<\alpha \le 1$) is a task-independent parameter. Firstly, we use actual experiments on multicore platforms to motivate the relevance of this model for our application. Then, we study the optimal time-minimizing allocation proposed by Prasanna and Musicus using optimal control theory. We greatly simplify their proofs by resorting only to pure scheduling arguments. Building on the insight gained thanks to these new proofs, we extend the study to distributed (homogeneous or heterogeneous) multicore platforms. We prove the NP-completeness of the corresponding scheduling problem, and we then propose some approximation algorithms [28] .

In a second step, we studied a simplified speed-up function for malleable tasks, corresponding to perfect parallelism for a number of processors below a given threshold. The threshold depends on the task. We proved that scheduling independent chains of malleable tasks under this model is NP-complete. We study the performance of a classical allocation policy which is agnostic of the threshold and a simple greedy heuristic, and proved that both are 2-approximation algorithms, even if in practice, the latter often outperforms the former.