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Efficient Runtime Support for Future Programming Standards

Because of the increasing number of cores on a single chip and in order to profit from the potential of accelerators in compute clusters, message passing paradigms like MPI are often no longer sufficient to utilize the hardware in an optimal way. Therefore a growing number of applications will employ a hybrid approach for parallelization, like MPI+OpenMP or MPI+OpenACC or even MPI+OpenMP+OpenACC. The recent version 4.0 of the OpenMP specification addresses this by incorporating programming support for accelerator devices and SIMD units in modern microarchitectures. This increases the complexity of application development and correctness checking for parallel applications. In ELP, a modified OpenMP runtime will be developed, delivering runtime internal information to correctness analysis tools like MUST or debuggers like DDT. This will allow detecting certain error classes automatically. The data will also be used by the performance analysis tool Vampir to better understand the performance behavior of an application.

Copyright: © Bundesministerium für Bildung und Forschung (BMBF)

Scalable Tools for the Analysis and Optimization of Energy Consumption in HPC - Score-E

For some time, computing centres have been feeling the severe financial impact of the energy consumption of modern computing systems, especially in the area of high-performance computing (HPC). Today, the share of energy already accounts for a third of the total cost of ownership and is continuously growing. The main objective of the Score-E project, funded under the 3rd "HPC software for scalable parallel computers" call of the german Federal Ministry of Education and Research (BMBF), is to provide user-friendly analysis and optimization tools for the energy consumption of HPC applications. more

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Development of a Scalable Data-Mining-based Prediction Model for ICT and Power Systems – ScaMPo

The complexity of power grids and at the same time of supercomputers increases permanently. Especially the increasing share of renewable energy in the power generation implies fundamental changes in the power grid. A growing number of participants is able to produce and to consume power. Consequently, every behavior modification has influence on the grid. In the area of supercomputing, the complexity of the system increases. For instance, the new RWTH cluster CLAIX scheduled to start operation in Nov. 2016 will have 600 2-sockel compute nodes and each CPU will have 24 cores (48 cores including hyperthreading). Parts of the system will be accelerated by GPUs and the power consumption of the whole system but also of each component will be continuously monitored. Changes to the software stack and any replacement of a defect hardware component has an influence on the performance, the power consumption and also on the failure rate. Especially, the forecast of the impact of changes and its long-term effects like the reduction of failure rates is a high challenge. Data mining is the key technology to handle such complex systems and is in principle a computational process of discovering patterns in large data sets. In general, data mining is one of the key technologies for the digital society. Based on the two examples – handling of complex power grids and supercomputers – the project ScaMPo creates a scalable framework to collect the data and to store in a cloud infrastructure. Afterwards, the data will be analyzed, patterns will be discovered and the understanding of the system will be improved. In case of supercomputers the operation costs will be reduced while in power grid the stability and the penetration of renewable energy will be increased. This project will not develop new data mining techniques. Rather, the project will base on open-source approaches for data mining and focus on the strength of the project partner, which is the design of a scalable and a robust approach. The long-term vision of the project, is the generalization of the approach for other research areas and the creation of a competence center for scalable data mining technologies.

Performance, Optimization and Productivity - POP

ProPE is a project funded by the German Science foundation (DFG) from 2017 to 2020. It aims at developing a blueprint for a sustainable, structured, and process-oriented service infrastructure for performance engineering (PE) of high performance applications in German tier-2 or tier-3 scientific computing centers.

The vision of ProPE is to have a nationwide support infrastructure which allows application scientists to develop and use code with provably optimal hardware resource utilization on high performance systems, thus reducing IT costs of scientific progress.

Further information can be found on the project homepage.

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