Several numerical computation algorithms exhibit dependences that lead to a wavefront of the computation. Depending on the data distribution chosen, pipelining communication and computation can be the only way to avoid a sequential execution of the parallel code. The computation grain has to be wisely chosen to obtain at the same time a maximum parallelism and a small communication overhead. On the other hand, when the size of data exceeds the memory capacity of the target platform, data have to be stored on disk. The concept of out-of-core computation aims at minimizing the impact of the I/O needed to compute on such data. It has been applied successfully on several linear algebra applications. In this paper we apply out-of-core techniques...
Abstract. Wavefront computations are common in scientific applications. Although it is well understo...
A number of applications on parallel computers deal with very large data sets that cannot fit in mai...
Out-of-core implementations of algorithms for dense matrix computations have traditionally focused o...
Several numerical computation algorithms exhibit dependences that lead to a wavefront of the computa...
Several numerical computation algorithms exhibit dependences that lead to a wavefront in the computa...
International audienceMatrix computation algorithms often exhibit dependencies between neighboring e...
This thesis introduces two tools for efficiently access data of a wavefront algorithm in an out-of-c...
International audienceABSTRACT The memory usage of sparse direct solvers can be the bottleneck to so...
Cette thèse introduit deux outils pour l'accès performant aux données d'un algorithme à front d'onde...
International audienceThe memory usage of sparse direct solvers can be the bottleneck to solve large...
The difficulty of handling out-of-core data limits the performance of supercomputers as well as the ...
AbstractCompact numerical schemes provide high-order solution of PDEs with low dissipation and dispe...
(eng) The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems ...
The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems involv...
International audienceHigh performance sparse direct solvers are often a method of choice in various...
Abstract. Wavefront computations are common in scientific applications. Although it is well understo...
A number of applications on parallel computers deal with very large data sets that cannot fit in mai...
Out-of-core implementations of algorithms for dense matrix computations have traditionally focused o...
Several numerical computation algorithms exhibit dependences that lead to a wavefront of the computa...
Several numerical computation algorithms exhibit dependences that lead to a wavefront in the computa...
International audienceMatrix computation algorithms often exhibit dependencies between neighboring e...
This thesis introduces two tools for efficiently access data of a wavefront algorithm in an out-of-c...
International audienceABSTRACT The memory usage of sparse direct solvers can be the bottleneck to so...
Cette thèse introduit deux outils pour l'accès performant aux données d'un algorithme à front d'onde...
International audienceThe memory usage of sparse direct solvers can be the bottleneck to solve large...
The difficulty of handling out-of-core data limits the performance of supercomputers as well as the ...
AbstractCompact numerical schemes provide high-order solution of PDEs with low dissipation and dispe...
(eng) The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems ...
The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems involv...
International audienceHigh performance sparse direct solvers are often a method of choice in various...
Abstract. Wavefront computations are common in scientific applications. Although it is well understo...
A number of applications on parallel computers deal with very large data sets that cannot fit in mai...
Out-of-core implementations of algorithms for dense matrix computations have traditionally focused o...