Time-energy trade-offs in processing divisible loads on heterogeneous hierarchical memory systems

Abstract We analyze time and energy performance of distributed computations in heterogeneous systems with hierarchical memory. Different levels of memory hierarchy have different time and energy efficiency. Core memory may be too small to hold whole load to be processed, while computations using external storage are expensive in time and energy. In order to avoid the costs of processing the load in the external memory, it is allowed that the load is distributed to the worker processors in multiple installments. A minimum energy solution is found by use of mixed integer linear programming under a limit on schedule length. Two types of fast heuristics with several variants are also examined. The trade-off between the criteria of processing time and energy is studied. Key features of optimum solutions are analyzed. It is shown that holding machines in a diverse set of energy modes and limited use of the out-of-core memory can be beneficial for the time and energy performance. The proposed scheduling algorithms are evaluated in the terms of solution quality and runtimes.

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