International audienceWe describe how to enhance parallelism in an asynchronous distributed-memory environment with limited memory dedicated to communication. In order to maximize asynchronism, we characterize deadlock situations and establish global properties to prevent or avoid them. We also characterize some communication patterns and define a class of broadcast trees ensuring good efficiency for series of successive asynchronous broadcasts. The impact of this work is illustrated on asynchronous sparse multifrontal solvers but has a larger scope
We study, using analytic models and simulation, the performance of the multifrontal methods on distr...
The article of record as published may be found at http://dx.doi.org/10.1155/2015/295393A Navier-Sto...
Distributed optimization algorithms are highly attractive for solving big data problems. In particul...
International audienceWe describe how to enhance parallelism in an asynchronous distributed-memory e...
AbstractCommunication costs are an important factor in the performance of massively parallel algorit...
The solution of sparse systems of linear equations is at the heart of numerous applicationfields. Wh...
International audienceTo solve sparse systems of linear equations, multifrontal methods rely on dens...
Parallelizing sparse irregular application on distributed memory systems poses serious scalability c...
Eliminating synchronizations is one of the important techniques related to minimizing communications...
Abstract Efficiently solving large sparse linear systems on loosely coupled net-works of computers i...
International audienceWe introduce shared-memory parallelism in a parallel distributed-memory solver...
An approach to carrying out asynchronous, distributed simulation on multiprocessor message-passing a...
We consider the solution of both symmetric and unsymmetric systems of sparse linear equations. A new...
Distributing discrete event simulations among several processors appears to be a promising approach ...
Thread allocation is an important problem in dis-tributed real-time and embedded (DRE) systems. A th...
We study, using analytic models and simulation, the performance of the multifrontal methods on distr...
The article of record as published may be found at http://dx.doi.org/10.1155/2015/295393A Navier-Sto...
Distributed optimization algorithms are highly attractive for solving big data problems. In particul...
International audienceWe describe how to enhance parallelism in an asynchronous distributed-memory e...
AbstractCommunication costs are an important factor in the performance of massively parallel algorit...
The solution of sparse systems of linear equations is at the heart of numerous applicationfields. Wh...
International audienceTo solve sparse systems of linear equations, multifrontal methods rely on dens...
Parallelizing sparse irregular application on distributed memory systems poses serious scalability c...
Eliminating synchronizations is one of the important techniques related to minimizing communications...
Abstract Efficiently solving large sparse linear systems on loosely coupled net-works of computers i...
International audienceWe introduce shared-memory parallelism in a parallel distributed-memory solver...
An approach to carrying out asynchronous, distributed simulation on multiprocessor message-passing a...
We consider the solution of both symmetric and unsymmetric systems of sparse linear equations. A new...
Distributing discrete event simulations among several processors appears to be a promising approach ...
Thread allocation is an important problem in dis-tributed real-time and embedded (DRE) systems. A th...
We study, using analytic models and simulation, the performance of the multifrontal methods on distr...
The article of record as published may be found at http://dx.doi.org/10.1155/2015/295393A Navier-Sto...
Distributed optimization algorithms are highly attractive for solving big data problems. In particul...