EXA2CT - Exascale Algorithms and Advanced Computational Techniques
Numerical simulation is a crucial part of science and industry in Europe. The advancement of simulation as a discipline relies on increasingly compute intensive models that require more computational resources to run. This is the driver for the evolution to exascale. Due to limits in the increase in single processor performance, exascale machines will rely on massive parallelism on and off chip, with a complex hierarchy of resources. The large number of components and the machine complexity introduce severe problems for reliability and programmability. The former of these will require novel fault-aware algorithms and support software. In addition, the scale of the numerical models exacerbates the difficulties by making the use of more complex simulation algorithms necessary, for numerical stability reasons. A key example of this is increased reliance on solvers. Such solvers require global communication, which impacts scalability, and are often used with preconditioners, increasing complexity again. Unless there is a major rethink of the design of solver algorithms, their components and software structure, a large class of important numerical simulations will not scale beyond petascale. This in turn will hold back the development of European science and industry which will fail to reap the benefits from exascale. The EXA2CT project brings together experts at the cutting edge of the development of solvers, related algorithmic techniques, and HPC software architects for programming models and communication. It will take a revolutionary approach to exascale solvers and programming models, rather than the incremental approach of other projects. We will produce modular open source proto-applications that demonstrate the algorithms and programming techniques developed in the project, to help boot-strap the creation of genuine exascale codes.
- Eftekhari A., Gaedke-Merzhäuser L., Pasadakis D., Bollhoefer M., Scheidegger S., Schenk O. (2022) Large-Scale Precision Matrix Estimation With SQUIC, Social Science Research Network:1573-1375
- Donfack S. ., Sanan P. ., Schenk O., Reps B. ., Vanroose W. (2018) A High Arithmetic Intensity Krylov Subspace Method Based on Stencil Compiler Programs. Proceedings of the International Conference on High Performance Computing in Science and Engineering. Springer International Publishing. Lecture Notes in Computer Science, vol 9611. Springer, Cham.. HPCSE2017. Soláň, Czech Republic. May 2017
- Verbosio F., Kardoš J., Bianco M., Schenk O. (2018) Highly Scalable Stencil-based Matrix-free Stochastic Estimator for the Diagonal of the Inverse. 9th Workshop on Applications for Multi-Core Architectures. 30th IEEE International Symposium on Computer, Architecture and High Performance Computing (SBAC-PAD 2018). ENS Lyon, Lyon, France. September 24-27, 2018
- Schenk O., Giraud L. ., Agullo E., Arbenz P. (2018) Special issue on parallel matrix algorithms and applications (PMAA’16), Parallel Computing:1-2
- Říha L., Brzobohat'y T., Markopoulos A., Kozubek T., Meca O., Schenk O., Vanroose W. (2016) Efficient Implementation of Total FETI Solver for Graphic Processing Units Using Schur Complement. Proceedings of the International Conference on High Performance Computing in Science and Engineering. Springer International Publishing. Lecture Notes in Computer Science, vol 9611. Springer, Cham. HPCSE2015. Soláň, Czech Republic. May 2015
- Lengauer C., Bolten M., Falgout R. D. ., Schenk O. (2015) Advanced Stencil-Code Engineering (Dagstuhl Seminar 15161), Dagstuhl Report, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik:56-75