Robust · Robust and Energy-Efficient Numerical Solvers Towards Reliable and Sustainable Scientific Computations

Robust and Energy-Efficient Numerical Solvers Towards Reliable and Sustainable Scientific Computations

In High-Performance Computing (HPC), every six months there is an evaluation of top performing systems in terms of actual floating-point operations per second (flops); every 2-4 years the system on the top changes by another often with the new and better performing hardware. This competition forms the TOP500 list. All these systems consume enormous amount of power. Hence, there is an obvious question of power and energy efficiency in HPC. Thus, power and energy efficiency as well as time-to-solution are the sustainability aspects of HPC. Note that there is also the Green TOP500 list with the better ratio of flops per watt. While there are works on hardware, e.g. the European Processor Initiative (EPI), there are also works on applications and algorithms to make them more energy efficient. Computations in parallel environments, like the emerging Exascale systems, are usually orchestrated by complex runtimes that employ various strategies to uniformly and efficiently distribute computations and data. However, these strategies, pursuing excellent performance and scalability, may also impair numerical reliability (accuracy and reproducibility) of final results due to the dynamic and, thus, non-deterministic execution as well as non-associativity of floating-point operations. In this project, we are primarily focused on fundamental algorithmic solutions, which often are in the heart of real-world applications, and foresee to collaborate with hardware experts for a possible joint undertaking. In particular, we aim to make algorithms numerically reliable, meaning that users can always rely on the output result for different problems and various configurations of the same or another system. Numerical reliability is associated with accuracy (quality of results) and reproducibility (ability to obtain the same results on repeated executions). Additionally, scientific computations frequently rely upon only one working precision for computing problems with various complexities, which leads to the significant underutilization of the floating-point representation or the lack of accuracy. We aim to develop numerically reliable algorithms (also called robust algorithms) with a possibility to adjust them to the actual working precision pursuing the goal of sustainable computations.

Data: CORDIS, © European Union