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Since several map tasks can read entities in parallel and process them, the reduce adds one more load balancing level between the map task and the reduce task for the purpose of decreasing the overload on these tasks.
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We employed 20 instances and each of them is configured with a maximum of 2 map tasks and 1 reduce task (we don't have reduce tasks for PML) for task trackers.
It executes the map task only for the splits that have been changed, and the reduce task only for the changed intermediate results.
Each reduce task is responsible for gathering information on the numbers obtained for a set of seeds.
Previously, it has also been suggested that co-variation between different control variables can be exploited to reduce task variance [81 86], for example, when deviations of two variables compensate for each other, as has been observed for variations in body and pistol angles compensating for each other to achieve a steady pointing position [81,82].
With two replicas, each reduce task is indispensable for the MapReduce job to finish because a quorum of two identical results is required for validation.
The original TSST-C protocol [27] was modified to reduce task difficulty appropriately for our younger age group, and we offered toys as potential rewards for high performance to increase motivation, with all children receiving a reward.
It can be seen that the optimum level for each factor is represented by the highest point in the graph (as presented in Figure 6); that is, L2 for time of system up, L2 for map task capacity, L1 for reduce task capacity, etc.
mapreduce.reduce.java.opts -Xmx2304m Maximum JVM heap memory for reduce task.
Each reduce task is usually responsible for processing the intermediate data associated with many intermediate keys.
The reduce task merges all pairs for a given word, and emits a final "word list(document ID)" pair.
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