Recent advances in sparse direct solvers

International audienceDirect methods for the solution of sparse systems of linear equations of the form A x = b are used in a wide range of numerical simulation applications. Such methods are based on the decomposition of the matrix into a product of triangular factors (e.g., A = L U ), followed by triangular solves. They are known for their numerical accuracy and robustness but are also characterized by a high memory consumption and a large amount of computations. Here we survey some research directions that are being investigated by the sparse direct solver community to alleviate these issues: memory-aware scheduling techniques, low-rank approximations, and distributed/shared memory hybrid programming

A Bridge Between the Asynchronous Message Passing Model and Local Computations in Graphs

International audienceA distributed system is a collection of processes that can interact. Three major process interaction models in distributed systems have principally been considered: - the message passing model, - the shared memory model, - the local computation model. In each model the processes are represented by vertices of a graph and the interactions are represented by edges. In the message passing model and the shared memory model, processes interact by communication primitives: messages can be sent along edges or atomic read/write operations can be performed on registers associated with edges. In the local computation model interactions are defined by labelled graph rewriting rules; supports of rules are edges or stars. These models (and their sub-models) reflect different system architectures, different levels of synchronization and different levels of abstraction. Understanding the power of various models, the role of structural network properties and the role of the initial knowledge enhances our understanding of basic distributed algorithms. This is done with some typical problems in distributed computing: election, naming, spanning tree construction, termination detection, network topology recognition, consensus, mutual exclusion. Furthermore, solutions to these problems constitute primitive building blocks for many other distributed algorithms. A survey may be found in [FR03], this survey presents some links with several parameters of the models including synchrony, communication media and randomization. An important goal in the study of these models is to understand some relationships between them. This paper is a contribution to this goal; more precisely we establish a bridge between tools and results presented in [YK96] for the message passing model and tools and results presented in [Ang80, BCG+96, Maz97, CM04, CMZ04, Cha05] for the local computation mode

Power and limits of distributed computing shared memory models

Due to the advent of multicore machines, shared memory distributed computing models taking into account asynchrony and process crashes are becoming more and more important. This paper visits some of the models for these systems, and analyses their properties from a computability point of view. Among them, the snapshot model and the iterated model are particularly investigated. The paper visits also several approaches that have been proposed to model crash failures. Among them, the wait-free case where any number of processes can crash is fundamental. The paper also considers models where up to t processes can crash, and where the crashes are not independent. The aim of this survey is to help the reader to better understand recent advances on what is known about the power and limits of distributed computing shared memory models and their underlying mathematics.Ce rapport est une introduction au modèles de calcul asynchrone pour les systèmes à mémoire partagée