Finding a configuration of resources satisfying user requirements is, in the general case, a challenging task. However, power consumption and performance have different trends depending on the application considered and on its input. To provide such guarantees, a possible solution consists in changing the number of cores assigned to the application, their clock frequency, and the placement of application threads over the cores. On the other hand, for some applications, it could be possible to decrease their performance, yet maintain an acceptable level, in order to reduce their power consumption. In current computing systems, many applications require guarantees on their maximum power consumption to not exceed the available power budget. Energy savings through frequency reduction not only provide cost advantages, they also reduce resource contention and create additional thermal headroom for non-throttled cores improving performance. On average for six MPI applications, the fine-grained dynamic policy speeds execution by 1% while the coarse-grained application results in a 3% slowdown. The coarse and fine-grained application of dynamic policy shows best energy savings of 32.1% and 19.5% with a 2% slowdown in both cases. TORo-Core is used to construct a dynamic policy applied at coarse and fine-grained levels to modulate per-core power controls on Haswell machines. An experimental memory study presented on modern CPU architectures, Intel Sandybridge and Haswell, identifies a metric, TORo_core, that detects bandwidth saturation and increased latency. Recent processors equipped with on-board hardware counters allow real time monitoring of operating conditions such as energy and temperature, in addition to performance measures such as instructions retired and memory accesses. Power is increasingly the limiting factor in High Performance Computing (HPC) at Exascale and will continue to influence future advancements in supercomputing.
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