Abstract
The ever-growing number of cores in a single chip has enabled cloud and HPC servers with better capabilities to exploit thread-level parallelism. However, this also leads to unforeseen issues related to the temperature, requiring an economically and environmentally expensive cooling infrastructure. Hence, providing better use of hardware resources is essential to reduce the operating temperature and related costs. In this scenario, we propose TAURUS: a thread-throttling and core-frequency optimization framework, which is entirely dynamic and transparent for the end user. When evaluating TAURUS on modern platforms, we show significant reductions in operating temperature and performance improvements over state-of-the-art strategies.
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