Sentence examples for distributing the workload from inspiring English sources

Exact(11)

Horizontal Scaling: Horizontal scaling involves distributing the workload across many servers which may be even commodity machines.

By distributing the workload among multiple connected computers, a process is executed in parallel thus reducing the completion time.

By distributing the workload between multiple CUDA-enabled GPUs and multicore processors, accelerations as high as 1000 × for PRISM and 15 × for multislice are achieved relative to traditional multislice implementations using a single 4-GPU machine.

This research presents an execution tool developed for SPMD applications which is focused on managing communications heterogeneities, distributing the workload among cores and enhancing parallel performance on multicore clusters.

From a broader perspective, the big data platforms can be categorized into the following two types of scaling: Horizontal Scaling: Horizontal scaling involves distributing the workload across many servers which may be even commodity machines.

Based on the difference in expected residual and residual energy, a fuzzy logic-based clustering algorithm with an extension to the energy predication has been proposed to prolong the lifetime of WSNs by evenly distributing the workload [25].

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So he built some web-based tools to distribute the workload and threw a pizza party in the computer room at the Technical University of Budapest.

He offers this advice: Determine your potential loss, then do as much cross-training as possible and ask other members of the team for suggestions on ways to distribute the workload to alleviate colleagues' resentment.

The purpose of the load balancer is to distribute the workload among the available application servers.

This paper proposes a method to optimally distribute the workload on different GPUs by avoiding devices that are running idle.

To meet the requirements of said application contexts, the system can create computing processes to distribute the workload over the network and/or a cloud computing server.

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