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2019
DOI: 10.1007/s10844-019-00575-8
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Study of parallel processing area extraction and data transfer number reduction for automatic GPU offloading of IoT applications

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Cited by 13 publications
(26 citation statements)
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“…There are two main features for applications offloading. The first one is the loop statements suitable for a GPU are extracted using evolutionary computation [21] of the genetic algorithm (GA) [28], and the variables used in nested loop statements are transferred between the CPU and GPU in the outer-most loop possible [20].…”
Section: Previous Automatic Gpu Offloading Methods and Its Problemsmentioning
confidence: 99%
See 4 more Smart Citations
“…There are two main features for applications offloading. The first one is the loop statements suitable for a GPU are extracted using evolutionary computation [21] of the genetic algorithm (GA) [28], and the variables used in nested loop statements are transferred between the CPU and GPU in the outer-most loop possible [20].…”
Section: Previous Automatic Gpu Offloading Methods and Its Problemsmentioning
confidence: 99%
“…In addition to the appropriate extraction of loop statements, I previously proposed to transfer as many variables used in the nested loop statements as possible to the upper level loop to reduce the number of transfers between CPU and GPU [20]. When offloading a loop statement to a GPU with a naive method, if CPU-GPU transfer is executed at the lower level of nesting, transfer is done at each lower-level loop, which is not efficient.…”
Section: Previous Automatic Gpu Offloading Methods and Its Problemsmentioning
confidence: 99%
See 3 more Smart Citations