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Liao et al. introduced CudaTree [19], a GPU Random Forest implementation which adaptively switches between data and task parallelism.
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Meanwhile, the iPhone app is task-driven, and enables sales people to add, edit, and sync relevant data and tasks between Salesforce.com and their smartphone — with a focus on fast data entry and a better User Experience.
Three rules of GPGPU programming are introduced as well as Amdahl's law, Big-O notation, and the distinction between data-parallel and task-parallel programming.
We do not exclude that similar exercises can be performed on the O*NET task rating data; however, the possibility of triangulation/cross-checking between the WageIndicator task data and the O*NET task ratings seems, at best, unlikely since they make use of different classifications for both occupations and tasks within occupations.
These tasks can be broken down into one of four types: (i) tasks that read data; (ii) tasks that add data and (iii) tasks that remove data.
Although for task fMRI a smoothing kernel of 8 mm is more conventional, in the current study we used a kernel of 6 mm for consistency between rsfMRI and task fMRI data (for Quality Control see Supplementary Data).
These additional costs in a full exposure assessment include all tasks between data collection and statistical analysis, including data entry for paper questionnaires, data processing, visual inspection and quality control for inclinometer data, and observer time spent recording postures from video still frames.
To model this kind of applications we consider a Data Flow Graph (DFG) (an example is depicted in Figure 2) which is a directed acyclic graph where nodes are processing functions and edges describe communication between tasks (data dependencies between tasks).
As a result, it is observed that there is a significant correlation between TACOM scores and task performance time data.
These connectors are an attempt to simplify the task of moving between data sources and visualization tools.
Two typical analysis tasks of such data include understanding similarities between data records and correlating records with certain dimensions, some of which may be class-labeled data.
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