In this talk, we present the PaStiX sparse supernodal solver, using hierarchical compression to reduce the burden on large blocks appearing during the nested dissection process. We compare the numerical stability, and the performance in terms of …
Sparse direct solvers is a time consuming operation required by many scientific applications to simulate physical problems. By its important overall cost, many studies tried to optimize the time to solution of those solvers on multi-core and …
The complexity of the hardware architectures of modern supercomputers led the community of developers of scientific libraries to adopt new parallel programming paradigms. Among them, task-based programming has certainly become one of the most popular …
Among the preprocessing steps of a sparse direct solver, reordering and block symbolic factorization are two major steps to reach a suitable granularity for BLAS kernels efficiency and runtime management. In this talk, we present a reordering …
In this thesis, we focus on the parallel solving of large sparse linear systems. Our main interest is on direct-iterative hybrid solvers such as HIPS, MAPHYS, PDSLIN or SHYLU, which rely on domain decomposition and Schur complement approaches. …
In the context of solving sparse linear systems, an ordering process partitions the matrix graph to minimize both fill-in and computational cost. We found that the ordering strategy used within supernodes might be enhanced to reduce the number of …
In this talk we will discuss our research activities on the design of parallel linear solvers for large scale problems that range from dense linear algebra, to parallel sparse direct solver and hybrid iterative-direct approaches. In particular we …
In the context of hybrid sparse linear solvers based on domain decomposition and Schur complement approaches, getting a domain decomposition tool leading to a good balancing of both the internal node set size and the interface node set size is a …