In today’s exponential world of digital data, big data services have made the power consumption the lion’s share of the total cost. For instance, Google data centers consume almost 260 MW, about a quarter of the output of a nuclear power plant, enough to power 200 000 homes. Energy efficiency is therefore considered a major criterion for “sustainable” computing systems and services over the data deluge. However, energy-efficient computing systems make parallel programming even more complex and thereby less robust due to requirements of massive parallelism, heterogeneity and data locality.
The PREAPP project aims to devise novel programming models that will form foundations for a paradigm shift from energy “blind” to energy “aware” software development. The new models will enable one order of magnitude improvement in energy efficiency in comparison with today’s multicore computing, thereby greatly advancing green computing and sustainable services. The new models will facilitate unprecedented productivity for implementing scientific big data applications that run effectively on large-scale high-performance computing (HPC) platforms, which are based on cutting-edge manycore architectures. The threshold of adopting large-scale parallel computing will thus be considerably lowered for a large number of computational scientists in several disciplines.
In the PREAPP project, the Arctic Green Computing group is the project manager.