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Pre-fetching is utilized to hide data fetch latency behind the computation.
Requests to nearby regions of data fetch the data from the disk cache (e.g., when scrolling through serial sections at the same location).
Posting a data set in some public web space lets people who want the data fetch it at their leisure and doesn't cost you anything if the recipients decide they don't need it after all.
Those power saving strategies could include limiting data fetch intervals, turning down display brightness, turning off open applications or even preventing some from running.
Due to the significant memory access requirement for video encoding tasks, a large amount of clock cycles is consumed by the processing core while waiting for the data fetch from local memory spaces.
As has been highlighted in Section "Experimental facility for validations of cache performance Trends", our experimental scenarios employ the Tier 2 configuration i.e. application executions and data fetch operations are conducted on the same physical server.
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Powerful computing capacity is required to analyze the power data fetched from electronic devices to recognize the device status for smarter applications of tomorrow.
Across a range of parameterized designs, we observe up to 50× reduction in the quantity of data fetched from external memory.
This system, called BDMPI,1 leverages the semantics of MPI's API to orchestrate the execution of a large number of MPI processes on much fewer compute nodes, so that the running processes maximize the amount of computation that they perform with the data fetched from the disk.
From the data fetched by ({mathsf {P}} ), a subgraph (G_Q(V_{mathsf {P}}, E_{mathsf {P}})) is built.
With the massive levels of sharding of data, fetching that data incurs a large fanout on the system backend resulting in reading from several servers and many different pieces of data from each server.
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