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In Machine Learning or other computationally intensive operations the workloads can often not fully utilize the hardware that they use. This imbalance leads to an unnecessary high energy consumption. Using more balanced hardware, the energy consumption of such workloads can be decreased while only sacrificing a small amount of computing time. In the second clean-IT openXchange live talk Max Plauth talked about the research he conducts in field.
Max Plauth is a PhD candidate in the Operating Systems and Middleware Group at the Hasso Plattner Institute. In 2017, Max Plauth was awarded the IBM Ph.D. Fellowship Award for his work on integrating hardware accelerators in virtualized environments. Recently, he has focused his research efforts on energy-aware computing and heterogeneous systems.