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Each node had a memory configuration of 128 GB.
The resulting optimal power consumptions for each limit are plotted in Fig. 4. For instruction memory, a split memory configuration of only two instances already causes a drastic power reduction (80.5 % for IP reassembly, 67.6 % on average).
But it should be noted that the distance code approaches the efficiency of the maximum likelihood approach with the large datasets that are barely accessible by the latter method due to the time required because of the physical memory configuration of the system.
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This allows investigating the effect of split memory configurations of different sizes.
The main contribution of this work is a mathematical model to identify a low power memory configuration for a set of applications.
An actual memory configuration for a given set of application profiles can be identified by a pair of power consumption on the one hand and on-chip area requirement on the other hand.
Starting with the structure of the memory subsystem of the adapter, it introduces a partitioned memory configuration that improves adapter performance significantly.
The authors of [1] provide average power savings of 17.8 % for instruction and 47.8 % for data memory in comparison with a single-banked memory configuration.
It offers a choice of six processors, nine memory configurations from 4GB to 32GB and nine first or second hard drives.
SHARCNET enabled rapid computations and the execution of some analyses that required large memory configurations (up to 16GB per processor).
The EMR Hadoop cluster maintained the default memory configurations because the instances used were sufficiently large in terms of resources.
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