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Energy aware computations on many core systems

Besides accuracy of the results, the overall solution time is the main quantity programmers focussing on. On the other hand the compute nodes transform electrical energy to heat which has to be dissipated afterwards. Extrapolating the recent hardware developments by ARM and Intel as well a by NVIDIA and AMD we have to scope with many cores on one chip that all have to transfer data to/from the main memory transpassing a hierarchy of caches and/or faster memory.
We will present available tools to determine the power consumption when executing various application codes on different hardware as a conventional CPU-Cluster, the Intel’s Knights Landing and the ThunderX by ARM. The application codes range from the simple Jacobi iteration to fully coupled cardiovascular simulations.
Choosing the Eikonal solver as one special application we will demonstrate how algorithmic changes and different of memory access patterns will reduce the overall energy consumption although the overall runtime might not be reduced.