Team Objectives & Research Activities
- Study and development of numerical methods and software for optimal use (performance and reliability) of scalar, vector, and parallel computers in scientific computing.
- Promote high performance computing through advanced training and consulting activities.
- Computational kernels and libraries for high performance computers
- Large sparse matrix calculations:
- direct linear solvers
- iterative linear solvers, preconditioning
- Domain decomposition
- Large scale nonlinear systems and optimization
- Reliability of numerical software,
- Heterogeneous computing
- Organized by CERFACS: training cycles and workshops, seminars and summer visits, hands on teaching on clusters, and courses.
- With CERFACS participation: engineering training for Dassault, CEA-INRIA-EDF courses, CNRS, INP and ENSTA courses.
A scientific paper on algorithmics among the most frequently downloaded.
The Society for Industrial and Applied Mathematics (SIAM) has implemented a new feature that provides a list of the SIAM journal articles that are most often downloaded. We are pleased to congratulate Pavel Jiranek, postdoctoral fellow in the Parallel Algorithms team: his joint paper with Miroslav Rozloznik and Martin Gutknecht "How to Make Simpler GMRES and GCR More Stable " (SIMAX, vol. 30-4, pp 1483-1499, 2008) was the fifth most downloaded article from the "SIAM Journal Matrix Analysis and its Applications" (SIMAX) in January 2009. SIMAX is one of the premier journals in the field of numerical linear algebra.
Success at the Stckholm PRACE petascale summerschool - 68 billion equations solved
During the PRACE Petascale Summer School held in Stockholm on August 26th-29th, Xavier Pinel and Xavier Vasseur from the Algo Team successfully ported a three-dimensional Helmholtz solver to the IBM Blue Gene P at Juelich, Germany and the CRAY XT4 at Espoo, Finland. They showed almost perfect scalability on up to 65,536 cores both in the weak sense (when the computation per processor is kept constant) and in the strong sense (when the same problem is run on an increasing number of processors) on academic model problems. They were able to solve a linear system of 68 billion unknowns in less than 3 hours setting a new benchmark for the solution of large sparse indefinite systems. The PhD thesis of Xavier Pinel is funded by TOTAL and cosupervised by Serge Gratton (CNES/CERFACS) and Xavier Vasseur. This work is in collaboration with Henri Calandra at TOTAL.