The task of a high performance computing system is to carry out its calculations (mainly scientific applications) with maximum performance and energy efficiency. Up until now, this goal could only be achieved by exclusively assigning an appropriate number of cores/nodes to parallel applications. As a consequence, applications had to be highly optimised in order to achieve even only a fraction of a supercomputer′s peak performance which required huge efforts on the programmer side.<br>This problem is expected to become more serious on future exascale systems with millions of compute cores. Many of today′s highly scalable applications will not be able to utilise an exascale system′s extreme parallelism due to node specific limitations like e.g. I/O bandwidth. Therefore, to be able to efficiently use future supercomputers, it will be necessary to simultaneously run more than one application on a node. To be able to efficiently perform co–scheduling, applications must not slow down each other, i.e. candidates for co–scheduling could e.g. be a memory–bound and a compute bound application.<br>Within this context, it might also be necessary to dynamically migrate applications between nodes if e.g. a new application is scheduled to the system.<br>In order to be able to monitor performance and energy efficiency during operation, additional sensors are required. These need to be correlated to running applications to deliver values for key performance indicators.<br>Main topics<br>Exascale architectures, supercomputers, scheduling, performance sensors, energy efficiency, task migration<br>
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Stockholm
Country
Sweden
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