In this work we investigate the design of task-based sparse direct
solvers which constitute extremely irregular workloads, with tasks of
different granularities and characteristics with variable memory
consumption on top of runtime systems. We prove the usability and
effectiveness of our approach with the implementation of a sparse
matrix multifrontal factorization based on a Sequential Task Flow
parallel programming model. Using this programming model, we developed
features such as the integration of dense 2D Communication Avoiding
algorithms in the multifrontal method allowing for better scalability
compared to the original approach used in the qr_mumps solver.
Following this approach, we address heterogeneous architectures where
task granularity and scheduling strategies are critical to achieve
performance. We present, for the multifrontal method, a hierarchical
strategy for data partitioning and a scheduling algorithm capable of
handling the heterogeneity of resources.