New features of our DSC system for distributing a symbolic computation task over a network of processors are described. A new scheduler sends parallel subtasks to those compute nodes that are best suited in handling the added load of CPU usage and memory. Furthermore, a subtask can communicate back to the process that spawned it by a co-routine style calling mechanism. Two large experiments are described in this improved setting. In the first we have implemented an algorithm that can prove a number of more than 1,000 decimal digits prime in about 2 months elapsed time on some 20 computers. In the second a parallel version of a sparse linear system solver is used to compute the solution of sparse linear systems over finite fields. We are abl...
Abstract. Numerical linear algebra and combinatorial optimization are vast subjects; as is their int...
Systems of linear equations arise at the heart of many scientific and engineering applications. Many...
International audienceTask-based programming models have been widely studied in the context of dense...
AbstractNew features of our DSC system for distributing a symbolic computation task over a network o...
this paper appeared in "Design and Implementation of Symbolic Computation Systems," A. Mio...
(eng) Scheduling a program (i.e. constructing a timetable for the execution of its operations) is on...
Problems in the class of unstructured sparse matrix computations are characterized by highly irregul...
We describe a coarse-grain parallel software system for the homogeneous solution of linear systems. ...
The era of manycore computing will bring new fundamental challenges that the techniques designed for...
We investigate the efficient iterative solution of large-scale sparse linear systems on shared-memor...
AbstractWe have recently multiprocessed a code for the direct solution of sparse linear equations on...
International audienceScientific workloads are often described by directed acyclic task graphs. This...
This thesis explores a fundamental issue in large-scale parallel computing: how to schedule tasks on...
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
It is important to have a fast, robust and scalable algorithm to solve a sparse linear system AX=B. ...
Abstract. Numerical linear algebra and combinatorial optimization are vast subjects; as is their int...
Systems of linear equations arise at the heart of many scientific and engineering applications. Many...
International audienceTask-based programming models have been widely studied in the context of dense...
AbstractNew features of our DSC system for distributing a symbolic computation task over a network o...
this paper appeared in "Design and Implementation of Symbolic Computation Systems," A. Mio...
(eng) Scheduling a program (i.e. constructing a timetable for the execution of its operations) is on...
Problems in the class of unstructured sparse matrix computations are characterized by highly irregul...
We describe a coarse-grain parallel software system for the homogeneous solution of linear systems. ...
The era of manycore computing will bring new fundamental challenges that the techniques designed for...
We investigate the efficient iterative solution of large-scale sparse linear systems on shared-memor...
AbstractWe have recently multiprocessed a code for the direct solution of sparse linear equations on...
International audienceScientific workloads are often described by directed acyclic task graphs. This...
This thesis explores a fundamental issue in large-scale parallel computing: how to schedule tasks on...
Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time o...
It is important to have a fast, robust and scalable algorithm to solve a sparse linear system AX=B. ...
Abstract. Numerical linear algebra and combinatorial optimization are vast subjects; as is their int...
Systems of linear equations arise at the heart of many scientific and engineering applications. Many...
International audienceTask-based programming models have been widely studied in the context of dense...