Objectives

Currently, the biggest obstacle for achieving exascale computing is energy consumption. Today, the world's fastest supercomputers operate in the petaflop/s range (1015 computer operations per second) and require between 8 and 18 megawatts, with the energy costs amounting to about US$ 1 million per megawatt and year. That is why simulation software developed as part of the ExaHyPE project will be consistently designed for the requirements of future energy-efficient hardware.

On the hardware side, an extreme parallelization is to be expected. By 2020, supercomputers will encompass hundreds of millions processor cores. At the same time, the hardware – which is pushed to its physical limits to achieve the further increase in performance and still must run as energy efficiently as possible – will increasingly tend to be plagued with interruptions and fluctuating performance curves. ExaHyPE will consequently examine the dynamic distribution of computer operations to processor cores – even if these fail while performing calculations.

Another objective is to reduce the internal-hardware communication simultaneously with the parallelization. Each data transfer is implemented at the expense of energy consumption. In ten years, supercomputers will be able to run calculations 1000 times faster than today. However, memory access time will fail to evolve at the same rate. The used algorithms should be inherently memory-efficient and require as little data transfer as possible to ensure fast, energy-efficient computer operations.

In order to take full advantage of the smallest possible amount of memory, the consortium is developing new scalable algorithms, which dynamically increase the resolution of simulations, i.e. the implemented numerical observation points, wherever the computer simulation needs – and only there. As a result, scientists will be able to limit the necessary computer operations to a minimum while simultaneously achieving the greatest possible accuracy for the simulation.

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