It is worth noting that our system was not designed to perform better than data-intensive frameworks deployed in a high-bandwidth, low-latency environment.
Using a rapid interaction testing threshold of αFAST = 10−4, a total of 6.17 million pairs of SNPs were carried forward for consideration in the more computationally intensive GLM framework.
There is only a minimal loss in power for a threshold of αFAST = 10−4, compared to the benchmark of αFAST = 1 where all pairs of SNPs are tested for interaction in the computationally intensive GLM framework.
In this way, the number of regressions performed in the computationally intensive GLM framework is minimized, substantially reducing computation time, but at only minimal cost in terms of reduced power.
A wide variety of environmental management problems are solved with a computationally intensive simulation-optimization framework.
Due to the intensive analysis of framework properties required in the evaluation process, the addressees of this approach are rather developers or experienced users of the respective frameworks.
As such, the assembly-oriented design system can easily be implemented under the multi-agent-based knowledge-intensive Petri net framework with concurrent integration of multiple cooperative knowledge sources and software.
Energy model, a systematic data-intensive multi-objective framework replicating the energy sector of the country or globe, constitutes of energy resource supply, energy consumption sector-by-sector, energy transformation technologies, greenhouse gas emission and energy pricing.
This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept.
